From b6973e1078f0114d63e6201df757a8e8a985bfed Mon Sep 17 00:00:00 2001 From: Christopher Paciorek Date: Fri, 13 Dec 2024 10:02:23 -0800 Subject: [PATCH] Robustify handling of numerical issues in samplers (#1512) * Make minor edit to REL_INST. * Error trap adaptation interval < 2 for various block samplers. * Robustify handling of NaN/Inf/-Inf in samplers. Add checkLogProb() that converts NaN to -Inf and warns of Inf values. Use checkLogProb() in various samplers (primarily slice and RW style). * Fix indentation. * Fix syntax error. * updates * updated indentation * Add missing checkLogProb in sampler_RW. * Fix typo in name. * Refine use of checkLogProb to use directly on log prob values, to better handle possible cases like "3 - Inf". Fix tests in light of use of checkLogProb. * Clean up testing for use of checkLogProb. Recreate gold files for trunc and dynamicIndexing testing for mcmc tests that get different samples because of changes in number of random numbers generated. * Inline checkLogProb. --------- Co-authored-by: Daniel Turek --- packages/nimble/R/MCMC_samplers.R | 83 ++- packages/nimble/R/MCMC_utils.R | 29 +- packages/nimble/R/RCfunction_core.R | 2 + packages/nimble/R/genCpp_sizeProcessing.R | 3 + packages/nimble/R/genCpp_toEigenize.R | 4 +- packages/nimble/inst/CppCode/Utils.cpp | 6 + packages/nimble/inst/include/nimble/Utils.h | 5 + .../dynamicIndexingTestLog_Correct.Rout | 110 ++-- .../tests/testthat/test-dynamicIndexing.R | 4 +- packages/nimble/tests/testthat/test-mcmc.R | 7 +- .../tests/testthat/truncTestLog_Correct.Rout | 522 +++++++++--------- 11 files changed, 403 insertions(+), 372 deletions(-) diff --git a/packages/nimble/R/MCMC_samplers.R b/packages/nimble/R/MCMC_samplers.R index c89ffe597..99c0b5e41 100644 --- a/packages/nimble/R/MCMC_samplers.R +++ b/packages/nimble/R/MCMC_samplers.R @@ -124,16 +124,19 @@ sampler_binary <- nimbleFunction( if(!model$isBinary(target)) stop('can only use binary sampler on discrete 0/1 (binary) nodes') }, run = function() { - currentLogProb <- model$getLogProb(calcNodes) + currentLogProb <- checkLogProb(model$getLogProb(calcNodes)) model[[target]] <<- 1 - model[[target]] - otherLogProbPrior <- model$calculate(target) + otherLogProbPrior <- checkLogProb(model$calculate(target)) if(otherLogProbPrior == -Inf) { otherLogProb <- otherLogProbPrior } else { - otherLogProb <- otherLogProbPrior + model$calculate(calcNodesNoSelf) + otherLogProb <- otherLogProbPrior + checkLogProb(model$calculate(calcNodesNoSelf)) } - acceptanceProb <- 1/(exp(currentLogProb - otherLogProb) + 1) - jump <- (!is.nan(acceptanceProb)) & (runif(1,0,1) < acceptanceProb) + if(currentLogProb == -Inf & otherLogProb == -Inf) + stop("in binary sampler, all log probability density values are negative infinity and sampling cannot proceed") + logProbDiff <- currentLogProb - otherLogProb + acceptanceProb <- 1/(exp(logProbDiff) + 1) + jump <- (!is.nan(acceptanceProb)) & (runif(1,0,1) < acceptanceProb) # `is.nan` probably not needed with use of `checkLogProb`. if(jump) { ##model$calculate(calcNodesPPomitted) nimCopy(from = model, to = mvSaved, row = 1, nodes = target, logProb = TRUE) @@ -184,26 +187,23 @@ sampler_categorical <- nimbleFunction( }, run = function() { currentValue <- model[[target]] - logProbs[currentValue] <<- model$getLogProb(calcNodes) + logProbs[currentValue] <<- checkLogProb(model$getLogProb(calcNodes)) for(i in 1:k) { if(i != currentValue) { model[[target]] <<- i - logProbPrior <- model$calculate(target) + logProbPrior <- checkLogProb(model$calculate(target)) if(logProbPrior == -Inf) { logProbs[i] <<- -Inf } else { - if(is.nan(logProbPrior)) { - logProbs[i] <<- -Inf - } else { - logProbs[i] <<- logProbPrior + model$calculate(calcNodesNoSelf) - if(is.nan(logProbs[i])) logProbs[i] <<- -Inf - } + logProbs[i] <<- logProbPrior + checkLogProb(model$calculate(calcNodesNoSelf)) } } } maxLP <- max(logProbs) - if(maxLP == Inf | is.nan(maxLP)) cat("Warning: categorical sampler for '", target, "' encountered an invalid model density, and sampling results are likely invalid.\n") + if(maxLP == -Inf) stop("in categorical sampler, all log probability density values are negative infinity and sampling cannot proceed") + infLogProbs <- logProbs == Inf logProbs <<- logProbs - maxLP + logProbs[infLogProbs] <<- 0 ## Prevent NaN inputs into `rcat`. probs <<- exp(logProbs) newValue <- rcat(1, probs) ## rcat normalizes the probabilities internally if(!is.na(newValue) & newValue != currentValue) { @@ -363,12 +363,12 @@ sampler_RW <- nimbleFunction( } } model[[target]] <<- propValue - logMHR <- model$calculateDiff(target) + logMHR <- checkLogProb(model$calculateDiff(target)) if(logMHR == -Inf) { jump <- FALSE nimCopy(from = mvSaved, to = model, row = 1, nodes = target, logProb = TRUE) } else { - logMHR <- logMHR + model$calculateDiff(calcNodesNoSelf) + propLogScale + logMHR <- logMHR + checkLogProb(model$calculateDiff(calcNodesNoSelf)) + propLogScale jump <- decide(logMHR) if(jump) { ##model$calculate(calcNodesPPomitted) @@ -520,14 +520,14 @@ sampler_RW_noncentered <- nimbleFunction( } model[[target]] <<- propValue - logMHR <- model$calculateDiff(target) + logMHR <- checkLogProb(model$calculateDiff(target)) if(logMHR == -Inf) { jump <- FALSE nimCopy(from = mvSaved, to = model, row = 1, nodes = target, logProb = TRUE) } else { ## Shift effects and add log-determinant of Jacobian of transformation. logMHR <- logMHR + updateNoncentered(propValue, currentValue) - logMHR <- logMHR + model$calculateDiff(calcNodesNoSelf) + propLogScale + logMHR <- logMHR + checkLogProb(model$calculateDiff(calcNodesNoSelf)) + propLogScale jump <- decide(logMHR) if(jump) { ##model$calculate(calcNodesPPomitted) @@ -689,12 +689,12 @@ sampler_RW_block <- nimbleFunction( for(i in 1:tries) { propValueVector <- generateProposalVector() values(model, targetAsScalar) <<- propValueVector - lpD <- model$calculateDiff(calcNodesProposalStage) + lpD <- checkLogProb(model$calculateDiff(calcNodesProposalStage)) if(lpD == -Inf) { jump <- FALSE nimCopy(from = mvSaved, to = model, row = 1, nodes = calcNodesProposalStage, logProb = TRUE) } else { - lpD <- lpD + model$calculateDiff(calcNodesDepStage) + lpD <- lpD + checkLogProb(model$calculateDiff(calcNodesDepStage)) jump <- decide(lpD) if(jump) { ##model$calculate(calcNodesPPomitted) @@ -941,9 +941,9 @@ sampler_slice <- nimbleFunction( setAndCalculateTarget = function(value = double()) { if(discrete) value <- floor(value) model[[target]] <<- value - lp <- model$calculate(target) + lp <- checkLogProb(model$calculate(target)) if(lp == -Inf) return(-Inf) - lp <- lp + model$calculate(calcNodesNoSelf) + lp <- lp + checkLogProb(model$calculate(calcNodesNoSelf)) returnType(double()) return(lp) }, @@ -1082,10 +1082,10 @@ sampler_slice_noncentered <- nimbleFunction( setAndCalculateTarget = function(value = double()) { if(discrete) value <- floor(value) model[[target]] <<- value - lp <- model$calculate(target) + lp <- checkLogProb(model$calculate(target)) if(lp == -Inf) return(-Inf) lp <- lp + updateNoncentered(value) - lp <- lp + model$calculate(calcNodesNoSelf) + lp <- lp + checkLogProb(model$calculate(calcNodesNoSelf)) returnType(double()) return(lp) }, @@ -1215,7 +1215,7 @@ sampler_ess <- nimbleFunction( theta_min <- theta - 2*Pi theta_max <- theta values(model, target) <<- f[1:d]*cos(theta) + nu[1:d]*sin(theta) + target_mean[1:d] - lp <- model$calculate(calcNodesNoSelf) + lp <- checkLogProb(model$calculate(calcNodesNoSelf)) numContractions <- 0 while((is.nan(lp) | lp < u) & theta_max - theta_min > eps & numContractions < maxContractions) { # must be is.nan() ## The checks for theta_max - theta_min small and max number of contractions are @@ -1224,7 +1224,7 @@ sampler_ess <- nimbleFunction( if(theta < 0) theta_min <- theta else theta_max <- theta theta <- runif(1, theta_min, theta_max) values(model, target) <<- f[1:d]*cos(theta) + nu[1:d]*sin(theta) + target_mean[1:d] - lp <- model$calculate(calcNodesNoSelf) + lp <- checkLogProb(model$calculate(calcNodesNoSelf)) numContractions <- numContractions + 1 } if(theta_max - theta_min <= eps | numContractions == maxContractions) { @@ -1370,9 +1370,9 @@ sampler_AF_slice <- nimbleFunction( for(i in 1:d) if(discrete[i] == 1) targetValues[i] <- floor(targetValues[i]) values(model, target) <<- targetValues - lp <- model$calculate(calcNodesProposalStage) + lp <- checkLogProb(model$calculate(calcNodesProposalStage)) if(lp == -Inf) return(lp) - lp <- lp + model$calculate(calcNodesDepStage) + lp <- lp + checkLogProb(model$calculate(calcNodesDepStage)) returnType(double()) return(lp) }, @@ -1499,15 +1499,9 @@ sampler_crossLevel <- nimbleFunction( propLP0 <- 0 for(iSF in seq_along(lowConjugateGetLogDensityFunctions)) { propLP0 <- propLP0 + lowConjugateGetLogDensityFunctions[[iSF]]$run() } propValueVector <- topRWblockSamplerFunction$generateProposalVector() - topLP <- my_setAndCalculateTop$run(propValueVector) - if(is.na(topLP)) { - logMHR <- -Inf - jump <- decide(logMHR) - if(jump) { - nimCopy(from = model, to = mvSaved, row = 1, nodes = calcNodes, logProb = TRUE) - } else { - nimCopy(from = mvSaved, to = model, row = 1, nodes = calcNodes, logProb = TRUE) - } + topLP <- checkLogProb(my_setAndCalculateTop$run(propValueVector)) + if(topLP == -Inf) { + nimCopy(from = mvSaved, to = model, row = 1, nodes = calcNodes, logProb = TRUE) } else { for(iSF in seq_along(lowConjugateSamplerFunctions)) @@ -1516,7 +1510,7 @@ sampler_crossLevel <- nimbleFunction( propLP1 <- 0 for(iSF in seq_along(lowConjugateGetLogDensityFunctions)) propLP1 <- propLP1 + lowConjugateGetLogDensityFunctions[[iSF]]$run() - logMHR <- modelLP1 - modelLP0 - propLP1 + propLP0 + logMHR <- checkLogProb(modelLP1) - checkLogProb(modelLP0) - checkLogProb(propLP1) + checkLogProb(propLP0) jump <- decide(logMHR) if(jump) { nimCopy(from = model, to = mvSaved, row = 1, nodes = calcNodes, logProb = TRUE) @@ -1728,7 +1722,7 @@ sampler_RW_dirichlet <- nimbleFunction( thetaVecProp <- thetaVec thetaVecProp[i] <- propValue values(model, target) <<- thetaVecProp / sum(thetaVecProp) - logMHR <- alphaVec[i]*propLogScale + currentValue - propValue + model$calculateDiff(calcNodesNoSelf) + logMHR <- alphaVec[i]*propLogScale + currentValue - propValue + checkLogProb(model$calculateDiff(calcNodesNoSelf)) jump <- decide(logMHR) } else jump <- FALSE if(adaptive & jump) timesAcceptedVec[i] <<- timesAcceptedVec[i] + 1 @@ -1848,7 +1842,7 @@ sampler_RW_wishart <- nimbleFunction( ## matrix multiply to get proposal value (matrix) model[[target]] <<- t(propValue_chol) %*% propValue_chol ## decide and jump - logMHR <- model$calculateDiff(calcNodes) + logMHR <- checkLogProb(model$calculateDiff(calcNodes)) deltaDiag <- thetaVec_prop[1:d]-thetaVec[1:d] for(i in 1:d) logMHR <- logMHR + (d+2-i)*deltaDiag[i] ## took me quite a while to derive this jump <- decide(logMHR) @@ -1971,7 +1965,8 @@ sampler_RW_lkj_corr_cholesky <- nimbleFunction( propValue[jprime] <<- z[jprime, i] * sqrt(partialSumsProp[jprime]) } model[[target]][j:i, i] <<- propValue[j:i] - logMHR <- calculateDiff(model, calcNodesNoSelf) + calculateDiff(model, target) + logMHR <- checkLogProb(calculateDiff(model, calcNodesNoSelf)) + + checkLogProb(calculateDiff(model, target)) ## Adjust MHR to account for non-symmetric proposal by adjusting prior on U to transformed scale (i.e., y). ## cosh component is for dz/dy and other component is for du/dz where 'u' is the corr matrix. ## This follows Stan reference manual Section 10.12 (for version 2.27). @@ -2143,12 +2138,12 @@ sampler_RW_block_lkj_corr_cholesky <- nimbleFunction( ## Adjust for log determinant term from initial values logMHR <- logMHR - logDetJac - lpD <- calculateDiff(model, calcNodesProposalStage) + lpD <- checkLogProb(calculateDiff(model, calcNodesProposalStage)) if(lpD == -Inf) { nimCopy(from = mvSaved, to = model, row = 1, nodes = calcNodesProposalStage, logProb = TRUE) jump <- FALSE } else { - logMHR <- logMHR + lpD + calculateDiff(model, calcNodesDepStage) + logMHR <- logMHR + lpD + checkLogProb(calculateDiff(model, calcNodesDepStage)) jump <- decide(logMHR) if(jump) { nimCopy(from = model, to = mvSaved, row = 1, nodes = calcNodes, logProb = TRUE) @@ -2423,7 +2418,7 @@ CAR_scalar_RW <- nimbleFunction( propValue <- rnorm(1, mean = model[[targetScalar]], sd = scale) model[[targetScalar]] <<- propValue lp1 <- dcarList[[1]]$run() + model$calculate(depNodes) - logMHR <- lp1 - lp0 + logMHR <- checkLogProb(lp1) - checkLogProb(lp0) jump <- decide(logMHR) if(jump) { model$calculate(targetScalar) diff --git a/packages/nimble/R/MCMC_utils.R b/packages/nimble/R/MCMC_utils.R index e7090b645..b1a1583b9 100644 --- a/packages/nimble/R/MCMC_utils.R +++ b/packages/nimble/R/MCMC_utils.R @@ -11,9 +11,9 @@ #' @export decide <- function(logMetropolisRatio) { if(is.na(logMetropolisRatio)) return(FALSE) - if(logMetropolisRatio > 0) return(TRUE) - if(runif(1,0,1) < exp(logMetropolisRatio)) return(TRUE) - return(FALSE) + if(logMetropolisRatio > 0) return(TRUE) + if(runif(1,0,1) < exp(logMetropolisRatio)) return(TRUE) + return(FALSE) } #NOTE: DETAILS(WAS BLANK) REMOVED @@ -55,7 +55,8 @@ decideAndJump <- nimbleFunction( copyNodesDeterm <- ccList$copyNodesDeterm; copyNodesStoch <- ccList$copyNodesStoch # not used: calcNodes, calcNodesNoSelf }, run = function(modelLP1 = double(), modelLP0 = double(), propLP1 = double(), propLP0 = double()) { - logMHR <- modelLP1 - modelLP0 - propLP1 + propLP0 + ## Check each one individually to catch case like `3 - Inf`. + logMHR <- checkLogProb(modelLP1) - checkLogProb(modelLP0) - checkLogProb(propLP1) + checkLogProb(propLP0) jump <- decide(logMHR) if(jump) { nimCopy(from = model, to = mvSaved, row = 1, nodes = target, logProb = TRUE) @@ -71,8 +72,26 @@ decideAndJump <- nimbleFunction( } ) +checkLogProb <- function(logProb) { + if(is.na(logProb)) + return(-Inf) + if(logProb == Inf) + print("MCMC sampling encountered a log probability density value of infinity. Results of sampling may not be valid.") + return(logProb) +} - +## checkLogProb <- nimbleFunction( +## name = "checkLogProb", +## run = function(logProb = double()) { +## if(is.na(logProb)) +## return(-Inf) +## if(logProb == Inf) +## print("MCMC sampling encountered a log probability density value of infinity. Results of sampling may not be valid.") +## return(logProb) +## returnType(double()) + +## } +## ) #' Creates a nimbleFunction for setting the value of a scalar model node, diff --git a/packages/nimble/R/RCfunction_core.R b/packages/nimble/R/RCfunction_core.R index d4dc5c08d..7f49de6e8 100644 --- a/packages/nimble/R/RCfunction_core.R +++ b/packages/nimble/R/RCfunction_core.R @@ -75,6 +75,8 @@ fxnsNotAllowedInAD <- c( 'invwish_chol', 'car_normal','car_proper','multi','dirch') ), 'getLogProb', 'decide', + 'checkLogProb', + 'checkLogProbWarn', 'rankSample', 'any_na', 'any_nan', diff --git a/packages/nimble/R/genCpp_sizeProcessing.R b/packages/nimble/R/genCpp_sizeProcessing.R index 93d81f443..0442506e7 100644 --- a/packages/nimble/R/genCpp_sizeProcessing.R +++ b/packages/nimble/R/genCpp_sizeProcessing.R @@ -139,6 +139,8 @@ sizeCalls <- c( 'nimArr_rmulti', 'nimArr_rdirch'), 'sizeRmultivarFirstArg'), makeCallList(c('decide', + 'checkLogProb', + 'checkLogProbWarn', 'size', 'getsize', 'getNodeFunctionIndexedInfo', @@ -155,6 +157,7 @@ sizeCalls <- c( ADbreak = 'sizeADbreak') scalarOutputTypes <- list(decide = 'logical', + checkLogProb = 'double', size = 'integer', nimAnyNA = 'logical', nimAnyNaN = 'logical', diff --git a/packages/nimble/R/genCpp_toEigenize.R b/packages/nimble/R/genCpp_toEigenize.R index 179528ea5..0994d7c0a 100644 --- a/packages/nimble/R/genCpp_toEigenize.R +++ b/packages/nimble/R/genCpp_toEigenize.R @@ -3,7 +3,8 @@ toEigenizeNoCalls <- c('dim', 'run.time', - 'nimOptimDefaultControl') + 'nimOptimDefaultControl', + 'checkLogProbWarn') toEigenizeYesCalls <- c(paste0('nimDiagonal', c('D','I','B')), 'diagonal', @@ -23,6 +24,7 @@ toEigenizeYesCalls <- c(paste0('nimDiagonal', c('D','I','B')), toEigenizeMaybeCalls <- c('map', c('decide', + 'checkLogProb', 'size', 'getsize', 'getNodeFunctionIndexedInfo', diff --git a/packages/nimble/inst/CppCode/Utils.cpp b/packages/nimble/inst/CppCode/Utils.cpp index 680622ce4..0962815be 100644 --- a/packages/nimble/inst/CppCode/Utils.cpp +++ b/packages/nimble/inst/CppCode/Utils.cpp @@ -21,6 +21,7 @@ #include #include "nimble/Utils.h" +#include "nimble/RcppNimbleUtils.h" // A utility function that will return floor(x) unless x is within numerical imprecision of an integer, in which case it will return round(x) int floorOrEquivalent(double x) { @@ -62,6 +63,11 @@ bool decide(double lMHr) { // simple function accept or reject based on log Metr return(false); } +void checkLogProbWarn() { + _nimble_global_output<<"MCMC sampling encountered a log probability density value of infinity. Results of sampling may not be valid.\n"; + nimble_print_to_R(_nimble_global_output); +} + void nimStop(string msg) {NIMERROR("%s", msg.c_str());} void nimStop() {NIMERROR("Error. Exiting from compiled execution.");} diff --git a/packages/nimble/inst/include/nimble/Utils.h b/packages/nimble/inst/include/nimble/Utils.h index 73915a9e0..f46cfc19e 100644 --- a/packages/nimble/inst/include/nimble/Utils.h +++ b/packages/nimble/inst/include/nimble/Utils.h @@ -162,6 +162,11 @@ class nimbleTimerClass_ { bool decide(double lMHr); //void allocate(vector< vector > *vv, int sampleSize, int variableSize); +void checkLogProbWarn(); + +static inline double checkLogProb(double logProb) { if(ISNAN(logProb)) return(-std::numeric_limits::infinity()); if(logProb == std::numeric_limits::infinity()) checkLogProbWarn(); return(logProb);} + + void nimStop(string msg); void nimStop(); diff --git a/packages/nimble/tests/testthat/dynamicIndexingTestLog_Correct.Rout b/packages/nimble/tests/testthat/dynamicIndexingTestLog_Correct.Rout index 6551af939..f2e35acc6 100644 --- a/packages/nimble/tests/testthat/dynamicIndexingTestLog_Correct.Rout +++ b/packages/nimble/tests/testthat/dynamicIndexingTestLog_Correct.Rout @@ -177,59 +177,59 @@ sd 1.50149 0.8846588 1.495999 0.1330843 0.9396094 1.430439 1.072287 0 0. 97.5% 4.00000 4.0000000 4.000000 3.0000000 4.0000000 4.000000 4.000000 2 4.0000000 4.0000000 4.0000000 4.000000 4.0000000 4.000000 2 4.0000000 4.000000 3 4.0000000 4.0000000 4.000000 2 4.000000 4.000000 4.000000 4.0000000 4.000000 2.00000000 4.000000 4.000000 -0.99597621 1.63124038 0.32045181 -0.4249512 3.34524729 0.7182967 0.13351076 0.17255027 0.45046239 0.17061201 0.25782114 0.44612409 0.42162172 8.3172687 ===== Finished MCMC test for basic mixture model with conjugacy. ===== ===== Starting MCMC test for basic mixture model without conjugacy. ===== - k[1] k[2] k[3] k[4] k[5] k[6] k[7] k[8] k[9] k[10] k[11] k[12] k[13] k[14] k[15] k[16] k[17] k[18] k[19] k[20] k[21] k[22] k[23] k[24] k[25] k[26] k[27] k[28] k[29] k[30] k[31] k[32] k[33] k[34] k[35] k[36] k[37] k[38] k[39] k[40] k[41] k[42] k[43] k[44] k[45] k[46] k[47] k[48] k[49] k[50] k[51] k[52] k[53] k[54] k[55] k[56] k[57] k[58] k[59] k[60] k[61] k[62] k[63] k[64] k[65] k[66] k[67] k[68] k[69] k[70] k[71] k[72] k[73] k[74] k[75] k[76] k[77] k[78] k[79] k[80] k[81] k[82] k[83] k[84] k[85] k[86] k[87] k[88] k[89] k[90] k[91] k[92] k[93] k[94] k[95] k[96] k[97] k[98] k[99] -mean 2.7280000 1.7140000 2.9220000 2.5840000 2.2960000 3.4960000 2.7440000 3.4060000 2.340000 2.8080000 2.8640000 2.9180000 2.3400000 2.9020000 2.2400000 2.6100000 1.1600000 2.8720000 2.9280000 2.3440000 2.4660000 2.850000 2.7180000 2.484000 2.7180000 2.6200000 2.2820000 2.3480000 1.5000000 2.6000000 2.3220000 2.3500000 2.4520000 3.1660000 2.9140000 2.9400000 2.5600000 2.6360000 2.2620000 2.3360000 1.1600000 2.3060000 2.4200000 2.5840000 2.5760000 2.9360000 1.2140000 2.8060000 2.3380000 2.3040000 2.2960000 2.4300000 2.8580000 2.8600000 2.4480000 2.3340000 2.7640000 1.1400000 2.8020000 2.8620000 2.7580000 3.7240000 2.8980000 2.8820000 2.3100000 2.5860000 2.9140000 1.6520000 2.4620000 2.8940000 2.7480000 2.4500000 2.9260000 2.7320000 2.9400000 2.3260000 2.616000 2.7320000 2.8360000 2.9180000 2.8820000 2.8260000 2.8560000 2.2360000 2.7520000 2.4240000 1.6440000 2.2280000 2.7920000 2.8840000 2.3660000 2.7340000 2.2240000 2.8920000 2.9140000 2.2760000 2.3080000 2.7800000 2.8680000 -sd 0.4454355 0.9581441 0.3100133 0.5014448 0.6612553 0.8691921 0.4459391 0.9136923 0.939236 0.5794891 0.3431319 0.2888648 0.9370999 0.3234273 0.5959113 0.5122742 0.5431298 0.3344244 0.3209705 0.5350107 0.5453796 0.357429 0.9572401 0.527542 0.4548514 0.5100198 0.6125573 0.5398879 0.8668927 0.5024988 0.9423864 0.5622564 0.5479932 0.9860978 0.3013064 0.2838329 0.8966649 0.4939554 0.5567548 0.5363575 0.5431298 0.5770876 0.5178188 0.5054255 0.5184221 0.3034603 0.6172244 0.5736044 0.9367556 0.6932136 0.6850713 0.5344823 0.3493997 0.9891192 0.5439557 0.7235268 0.4435058 0.5108051 0.5791951 0.3452454 0.4425242 0.6904908 0.3793201 0.3351128 0.6021837 0.5207171 0.2945803 0.9384334 0.5631966 0.3391076 0.4391831 0.8227112 0.2695725 0.4523109 0.2377247 0.9452571 0.922087 0.9623035 0.3813067 0.3089772 0.3229312 0.3794892 0.3570981 0.5907639 0.9676615 0.8059299 0.9354218 0.5258984 0.6012145 0.3560865 0.9261531 0.4468146 0.5852427 0.3957895 0.3504992 0.4942799 0.5345161 0.4146612 0.3504592 -2.5% 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.4750000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 -97.5% 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 4.0000000 3.000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.5250000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 - k[100] k[101] k[102] k[103] k[104] k[105] k[106] k[107] k[108] k[109] k[110] k[111] k[112] k[113] k[114] k[115] k[116] k[117] k[118] k[119] k[120] k[121] k[122] k[123] k[124] k[125] k[126] k[127] k[128] k[129] k[130] k[131] k[132] k[133] k[134] k[135] k[136] k[137] k[138] k[139] k[140] k[141] k[142] k[143] k[144] k[145] k[146] k[147] k[148] k[149] k[150] k[151] k[152] k[153] k[154] k[155] k[156] k[157] k[158] k[159] k[160] k[161] k[162] k[163] k[164] k[165] k[166] k[167] k[168] k[169] k[170] k[171] k[172] k[173] k[174] k[175] k[176] k[177] k[178] k[179] k[180] k[181] k[182] k[183] k[184] k[185] k[186] k[187] k[188] k[189] k[190] k[191] k[192] k[193] k[194] k[195] k[196] k[197] k[198] k[199] -mean 2.9380000 2.272000 2.7680000 1.152000 2.3780000 2.2960 2.8500000 3.9200000 2.7420000 2.3820000 2.2780000 2.7540000 2.7760000 2.8540000 2.8620000 3.4720000 3.6240000 3.1720000 2.2500000 1.660000 2.8340000 2.9160000 2.8640000 1.6380000 2.3380000 2.9160000 2.8360000 2.9240000 2.2580000 2.7900000 2.8580000 2.7440000 2.9120000 2.7300000 1.1680000 2.284000 1.1840000 2.8860000 2.3220000 2.9020000 2.464000 2.7820000 2.7580000 2.8940000 2.7700000 2.2580000 2.7740000 2.3860000 2.9080000 2.900000 2.728000 2.8660000 2.3300000 2.3760000 2.6080000 2.5880000 2.9140000 2.2560000 2.774000 2.6300000 2.9140000 2.4120000 2.4440000 2.6440000 2.8820000 2.930000 2.9140000 2.8640000 1.6640000 2.2820000 2.7800000 1.6620000 2.2940000 2.6420000 3.1640000 2.3020000 2.8740000 2.8580000 2.8920000 2.3360000 2.7560000 3.3120000 2.7580000 1.5920000 2.9160000 2.4280000 2.9140000 2.7440000 2.8080000 2.3260000 2.2520000 2.9240000 2.9060000 2.4280000 2.864000 2.8960000 2.4320000 2.6080000 2.8340000 2.3420000 -sd 0.3071558 0.637832 0.4319133 0.530527 0.5725134 0.6521 0.3684719 0.3923109 0.4470298 0.7780017 0.5525637 0.4403087 0.6185379 0.3700783 0.3622409 0.8824815 0.7822069 0.9860835 0.6133942 0.939236 0.3777955 0.2985335 0.3489233 0.9320372 0.7299463 0.2985335 0.3916769 0.3383089 0.6166007 0.4174308 0.3716345 0.4548382 0.9971181 0.4533332 0.5553312 0.497835 0.5786308 0.4211398 0.5467301 0.3295652 0.534111 0.4228873 0.4379722 0.3886723 0.4306634 0.6198422 0.6194283 0.9201616 0.3158092 0.313363 0.458734 0.3409935 0.5307196 0.7718909 0.5048224 0.5203822 0.3617537 0.6125475 0.418658 0.5035943 0.3617537 0.7896221 0.5435576 0.5037574 0.4200344 0.277947 0.3561709 0.3546202 0.9428053 0.5684384 0.6035964 0.9410245 0.6756308 0.5161092 0.9874483 0.6062434 0.3321817 0.3606885 0.3529662 0.9364068 0.6428768 0.9510336 0.4379722 0.9138963 0.3051725 0.5379543 0.9962877 0.4414224 0.5829371 0.5406557 0.5595733 0.2799656 0.3367355 0.8980138 0.360227 0.3866303 0.5421844 0.5087767 0.3777955 0.5528538 -2.5% 2.0000000 2.000000 2.0000000 1.000000 2.0000000 2.0000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 1.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.4750000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 -97.5% 3.0000000 4.000000 3.0000000 3.000000 4.0000000 4.0000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 - k[200] k[201] k[202] k[203] k[204] k[205] k[206] k[207] k[208] k[209] k[210] k[211] k[212] k[213] k[214] k[215] k[216] k[217] k[218] k[219] k[220] k[221] k[222] k[223] k[224] k[225] k[226] k[227] k[228] k[229] k[230] k[231] k[232] k[233] k[234] k[235] k[236] k[237] k[238] k[239] k[240] k[241] k[242] k[243] k[244] k[245] k[246] k[247] k[248] k[249] k[250] k[251] k[252] k[253] k[254] k[255] k[256] k[257] k[258] k[259] k[260] k[261] k[262] k[263] k[264] k[265] k[266] k[267] k[268] k[269] k[270] k[271] k[272] k[273] k[274] k[275] k[276] k[277] k[278] k[279] k[280] k[281] k[282] k[283] k[284] k[285] k[286] k[287] k[288] k[289] k[290] k[291] k[292] k[293] k[294] k[295] k[296] k[297] k[298] k[299] -mean 2.304000 2.6960000 2.8740000 2.9020000 1.188000 2.2720000 2.9120000 2.8500000 2.9160000 2.7860000 2.3180000 1.6620000 2.9240000 2.4660000 2.7500000 1.7100000 2.930000 2.892000 2.7400000 2.3120000 2.8120000 2.6440000 3.1800000 2.8300000 2.4680000 2.9380000 2.2980000 2.2620000 2.7120000 1.6040000 3.5560000 2.7940000 2.3500000 2.7680000 2.8380000 2.7960000 2.938000 2.9400000 2.8100000 2.2760000 2.7840000 2.314000 2.424000 2.7600000 2.380000 1.1440000 2.358000 2.3940000 2.8700000 2.8600000 2.7580000 2.8900000 2.8840000 2.2240000 2.9220000 2.7820000 2.9140000 2.8500000 2.9460000 2.3480000 2.574000 2.8960000 2.3600000 2.9180000 2.9200000 2.9160000 2.8860000 2.9180000 2.7700000 3.8240000 2.7580000 2.2640000 2.5540000 3.7880000 2.8160000 2.9060000 2.4860000 1.6840000 2.6240000 2.8320000 2.8560000 2.9080000 1.2080000 2.3400000 2.9160000 2.7240000 1.164000 1.6920000 1.5960000 1.6680000 2.9280000 2.9100000 2.4360000 2.8940000 2.9320000 1.6040000 2.8460000 2.2960000 2.8700000 2.9140000 -sd 0.540526 0.9473008 0.3440359 0.3907703 0.584242 0.6283356 0.3640344 0.3684719 0.3051725 0.6040976 0.5151765 0.9410245 0.2727137 0.5634527 0.4334464 0.9569386 0.324516 0.329474 0.9624201 0.6808694 0.5703566 0.4997635 0.9846518 0.3865214 0.5343661 0.3005606 0.5778094 0.6213922 0.7000258 0.9191701 0.8320147 0.5935221 0.5404481 0.9716296 0.3899489 0.9779042 0.265135 0.2908077 0.3926938 0.5696253 0.4401494 0.513743 0.529892 0.4413497 0.923746 0.5174936 0.929281 0.7848541 0.4398715 0.3586883 0.4379722 0.3318437 0.3560865 0.5920516 0.3034801 0.6158202 0.3013064 0.3629925 0.2349349 0.5688085 0.518712 0.4018794 0.7372157 0.2888648 0.2788468 0.3538282 0.3243677 0.3571149 0.6211858 0.5671572 0.6389464 0.6380582 0.8925678 0.6162918 0.4080159 0.3760943 0.5388736 0.9497094 0.5051082 0.3795578 0.3570981 0.3630421 0.6111325 0.9328131 0.3303972 0.4519209 0.549279 0.9523392 0.9156751 0.9442241 0.3209705 0.3132031 0.5161752 0.4184665 0.3340647 0.9191701 0.3668148 0.5805671 0.3425415 0.3013064 -2.5% 2.000000 2.0000000 2.0000000 1.0000000 1.000000 2.0000000 1.4750000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 1.000000 1.0000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.4750000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 -97.5% 4.000000 4.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.000000 3.0000000 3.000000 3.0000000 3.000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 - k[300] k[301] k[302] k[303] k[304] k[305] k[306] k[307] k[308] k[309] k[310] k[311] k[312] k[313] k[314] k[315] k[316] k[317] k[318] k[319] k[320] k[321] k[322] k[323] k[324] k[325] k[326] k[327] k[328] k[329] k[330] k[331] k[332] k[333] k[334] k[335] k[336] k[337] k[338] k[339] k[340] k[341] k[342] k[343] k[344] k[345] k[346] k[347] k[348] k[349] k[350] k[351] k[352] k[353] k[354] k[355] k[356] k[357] k[358] k[359] k[360] k[361] k[362] k[363] k[364] k[365] k[366] k[367] k[368] k[369] k[370] k[371] k[372] k[373] k[374] k[375] k[376] k[377] k[378] k[379] k[380] k[381] k[382] k[383] k[384] k[385] k[386] k[387] k[388] k[389] k[390] k[391] k[392] k[393] k[394] k[395] k[396] k[397] k[398] k[399] -mean 2.9080000 2.2660000 2.778000 2.6000000 2.3800000 2.7880000 2.9140000 2.5560000 1.6620000 2.2760000 2.3020000 2.7200000 2.4800000 2.9280000 2.5440000 2.9340000 2.5860000 2.3260000 1.5840000 2.7960000 2.2680000 2.9180000 2.9180000 2.3000000 2.3760000 2.2420000 2.8980000 2.9320000 2.7720000 2.7500000 2.2280000 2.2440000 2.532000 1.1840000 2.3180000 2.3600000 2.5940000 2.2680000 2.9080000 2.3980000 2.3780000 2.514000 3.8720000 2.9100000 2.2400000 2.9220000 2.3720000 2.7860000 2.5740000 2.4620000 2.7300000 2.2720000 2.9200000 2.8660000 2.2880000 2.2420000 2.7920000 2.9120000 2.7720000 2.4720000 2.7380000 2.3420000 2.3000000 2.9180000 2.630000 2.7780000 2.3100000 2.3340000 2.578000 2.9140000 2.2540000 3.3000000 2.4000000 2.3900000 2.3240000 2.4120000 2.9140000 2.2800000 2.3680000 1.6780000 2.248000 2.7480000 2.9160000 2.9260000 2.2760000 2.7520000 2.9020000 2.4720000 2.7860000 2.2120000 2.6120000 2.9180000 2.2720000 2.6380000 2.8740000 2.3360000 2.3340000 2.2580000 2.3960000 2.7660000 -sd 0.3028522 0.5620282 0.620876 0.5104126 0.7620771 0.6031447 0.2945803 0.5209672 0.9410245 0.5937416 0.6955311 0.4582794 0.5311914 0.3271545 0.8840969 0.3063195 0.5050883 0.9410074 0.9102753 0.5958978 0.6300555 0.2888648 0.2888648 0.5201356 0.9210432 0.6001302 0.3461729 0.3091393 0.4386352 0.4471016 0.5733004 0.6043132 0.873213 0.5786308 0.6616279 0.5468071 0.5076213 0.6363851 0.3574795 0.7879325 0.7643851 0.873688 0.4899961 0.3132031 0.5356995 0.2829773 0.5781735 0.6040976 0.8933758 0.5414262 0.4577325 0.5573484 0.3373835 0.3409935 0.6370775 0.5900273 0.5911302 0.3471268 0.6267389 0.5308291 0.4491765 0.6913696 0.5390558 0.2818419 0.519268 0.6111161 0.6218306 0.5504999 0.502415 0.3561709 0.5496255 0.9548946 0.9130539 0.9206493 0.9427628 0.5466751 0.3143272 0.5850509 0.9264408 0.9466299 0.546895 0.4482162 0.2847915 0.2840517 0.5937416 0.4368956 0.3295652 0.5530169 0.6074059 0.5618606 0.5310556 0.3398632 0.6624906 0.5054374 0.4271873 0.7184262 0.5319869 0.5657527 0.7902106 0.4377525 -2.5% 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 -97.5% 3.0000000 4.0000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.000000 3.0000000 4.0000000 4.0000000 3.000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 - k[400] k[401] k[402] k[403] k[404] k[405] k[406] k[407] k[408] k[409] k[410] k[411] k[412] k[413] k[414] k[415] k[416] k[417] k[418] k[419] k[420] k[421] k[422] k[423] k[424] k[425] k[426] k[427] k[428] k[429] k[430] k[431] k[432] k[433] k[434] k[435] k[436] k[437] k[438] k[439] k[440] k[441] k[442] k[443] k[444] k[445] k[446] k[447] k[448] k[449] k[450] k[451] k[452] k[453] k[454] k[455] k[456] k[457] k[458] k[459] k[460] k[461] k[462] k[463] k[464] k[465] k[466] k[467] k[468] k[469] k[470] k[471] k[472] k[473] k[474] k[475] k[476] k[477] k[478] k[479] k[480] k[481] k[482] k[483] k[484] k[485] k[486] k[487] k[488] k[489] k[490] k[491] k[492] k[493] k[494] k[495] k[496] k[497] k[498] k[499] -mean 2.8000000 2.3360000 2.7900000 2.858000 2.788000 1.2120000 3.7120000 2.8600000 2.5740000 2.4760000 2.8660000 2.9160000 2.9060000 2.7380000 2.4420000 2.6020000 2.8520000 1.6500000 2.3280000 2.8900000 2.2140000 2.6460000 2.8460000 2.7580000 2.3540000 1.1680000 2.8380000 2.8480000 2.9140000 2.756000 2.7320000 1.152000 2.7480000 2.2680000 2.5760000 2.4560000 3.8840000 2.3480000 3.1140000 1.164000 2.3760000 1.6040000 2.7580000 2.8540000 2.3680000 2.3340000 2.8360000 1.164000 2.8080000 2.7620000 2.8540000 2.312000 2.910000 1.6080000 2.8500000 3.1480000 2.2180000 2.6540000 1.1460000 1.1960000 2.8000000 2.4540000 2.8480000 2.8820000 2.8980000 2.6260000 2.9620000 2.8960000 2.8140000 2.4060000 2.5860000 2.8220000 2.7740000 2.2200000 2.2920000 2.8480000 2.8920000 2.9260000 2.5960000 2.4500000 3.3280000 1.1960000 2.8900000 1.1420000 2.2940000 2.7540000 2.4020000 2.7880000 2.7340000 2.7240000 2.7260000 2.9320000 2.726000 2.4580000 1.6820000 2.9040000 2.7940000 2.2520000 2.2820000 2.312000 -sd 0.5871025 0.9406773 0.5988466 0.355089 0.418816 0.6162918 0.7028827 0.3642325 0.5109267 0.5495708 0.3525515 0.2985335 0.3760943 0.6528464 0.5321676 0.5060714 0.4759717 0.9366187 0.5449863 0.3977643 0.4986997 0.9286597 0.3668148 0.4287232 0.9351112 0.5553312 0.3742139 0.3810333 0.3561709 0.639752 0.4478584 0.530527 0.9666254 0.6519402 0.5066927 0.5259441 0.4679551 0.5472614 0.9934676 0.549279 0.9253846 0.9191701 0.4333724 0.3590848 0.9264408 0.5431704 0.3916769 0.549279 0.5725309 0.6373134 0.3590848 0.513013 0.306738 0.9187078 0.3629925 0.9899778 0.5128372 0.4927165 0.5188665 0.5952248 0.5836792 0.5370856 0.3703651 0.3468669 0.3159044 0.4966057 0.2380532 0.3184129 0.5727653 0.5115539 0.5207171 0.3828958 0.4327801 0.5658979 0.5995456 0.3593805 0.3855299 0.3297597 0.5072461 0.5254066 0.9456238 0.5952248 0.3437096 0.5122116 0.9536156 0.9692073 0.9132711 0.5964625 0.6694328 0.6728517 0.9595653 0.3155553 0.675156 0.5484867 0.9479838 0.3729103 0.9764584 0.5413706 0.6413257 0.706863 -2.5% 1.0000000 1.0000000 1.0000000 2.000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 1.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.000000 1.0000000 1.0000000 2.0000000 2.000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.4750000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 -97.5% 3.0000000 3.0000000 3.0000000 3.000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.5250000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.000000 3.000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.5250000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.000000 - k[500] k[501] k[502] k[503] k[504] k[505] k[506] k[507] k[508] k[509] k[510] k[511] k[512] k[513] k[514] k[515] k[516] k[517] k[518] k[519] k[520] k[521] k[522] k[523] k[524] k[525] k[526] k[527] k[528] k[529] k[530] k[531] k[532] k[533] k[534] k[535] k[536] k[537] k[538] k[539] k[540] k[541] k[542] k[543] k[544] k[545] k[546] k[547] k[548] k[549] k[550] k[551] k[552] k[553] k[554] k[555] k[556] k[557] k[558] k[559] k[560] k[561] k[562] k[563] k[564] k[565] k[566] k[567] k[568] k[569] k[570] k[571] k[572] k[573] k[574] k[575] k[576] k[577] k[578] k[579] k[580] k[581] k[582] k[583] k[584] k[585] k[586] k[587] k[588] k[589] k[590] k[591] k[592] k[593] k[594] k[595] k[596] k[597] k[598] k[599] k[600] -mean 2.8980000 2.7680000 2.7540000 2.4740000 2.5960000 2.900000 2.824000 2.9240000 2.8680000 1.1660000 2.4600000 1.6500000 2.2720000 2.5560000 2.7080000 2.9180000 2.7860000 2.2520000 2.91600 2.7640000 2.6540000 3.7240000 2.9100000 2.9000000 1.7040000 2.2580000 2.3300000 2.636000 2.850000 2.8880000 2.5300000 2.7780000 1.6160000 1.1480000 2.6120000 2.8800000 2.9120000 1.7040000 2.7540000 2.8220000 2.9840000 2.4600000 1.6600000 2.9180000 2.3860000 2.3480000 2.8560000 2.596000 2.8880000 2.3920000 2.8380000 2.296000 1.6740000 2.8680000 2.8080000 2.2040000 2.7260000 2.4460000 2.782000 2.604000 3.1800000 2.7480000 2.2680000 2.9120000 1.5980000 2.6120000 2.7520000 2.8540000 2.8920000 3.6200000 2.6220000 1.210000 2.324000 2.3740000 2.3380000 2.6180000 2.4140000 3.176000 2.8760000 2.7300000 2.3200000 2.4800000 2.728000 2.782000 2.448000 2.4260000 2.7960000 2.4380000 2.9080000 3.4840000 2.3520000 2.3500000 2.9200000 2.7240000 2.9120000 2.3240000 2.7620000 2.2600000 2.2540000 2.9080000 2.2660000 -sd 0.3343944 0.4225317 0.6342622 0.5531147 0.5072461 0.337977 0.556456 0.2799656 0.3446935 0.5504999 0.5449715 0.9366187 0.6315169 0.8924443 0.4595197 0.3514585 0.6106962 0.5909267 0.36498 0.4343747 0.4927165 0.6904908 0.3551793 0.3824821 0.9561446 0.5440846 0.5981769 0.497996 0.373871 0.3341846 0.8801416 0.6176398 0.9242578 0.5240661 0.5118398 0.4358669 0.2973766 0.9561446 0.4403087 0.5612004 0.9988691 0.5412818 0.9413673 0.3457095 0.9223369 0.9362955 0.3514413 0.515087 0.3401285 0.9187078 0.3847754 0.696674 0.9452571 0.3388298 0.5794891 0.5088239 0.4553446 0.5400623 0.609277 0.509611 0.9846518 0.6583029 0.5592867 0.3105622 0.9154628 0.5157402 0.4414587 0.3534599 0.4155495 0.7853876 0.4976055 0.612086 0.944886 0.7584686 0.9410245 0.5065463 0.7999274 0.985376 0.3359311 0.4533332 0.5311914 0.5460736 0.458734 0.609277 0.554898 0.5225611 0.5958978 0.5279332 0.3630421 0.8759444 0.9304986 0.5658094 0.2859432 0.6758236 0.3471268 0.5400067 0.6404501 0.6173835 0.6279113 0.3343045 0.5655827 -2.5% 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 1.000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.00000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 2.000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 -97.5% 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.00000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 4.000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.000000 4.0000000 3.0000000 3.0000000 4.0000000 4.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 - k[601] k[602] k[603] k[604] k[605] k[606] k[607] k[608] k[609] k[610] k[611] k[612] k[613] k[614] k[615] k[616] k[617] k[618] k[619] k[620] k[621] k[622] k[623] k[624] k[625] k[626] k[627] k[628] k[629] k[630] k[631] k[632] k[633] k[634] k[635] k[636] k[637] k[638] k[639] k[640] k[641] k[642] k[643] k[644] k[645] k[646] k[647] k[648] k[649] k[650] k[651] k[652] k[653] k[654] k[655] k[656] k[657] k[658] k[659] k[660] k[661] k[662] k[663] k[664] k[665] k[666] k[667] k[668] k[669] k[670] k[671] k[672] k[673] k[674] k[675] k[676] k[677] k[678] k[679] k[680] k[681] k[682] k[683] k[684] k[685] k[686] k[687] k[688] k[689] k[690] k[691] k[692] k[693] k[694] k[695] k[696] k[697] k[698] k[699] k[700] -mean 2.3480000 2.8580000 2.7620000 2.6180000 2.3140000 2.2380000 2.8960000 2.890000 2.3880000 2.9080000 2.8660000 2.778000 2.9180000 2.7320000 2.7840000 2.7600000 2.5740000 2.3240000 2.4640000 2.3220000 2.7980000 2.9280000 2.8460000 2.7640000 2.4900000 2.7460000 2.3460000 2.2180000 2.3040000 2.3460000 2.9080000 2.7300000 2.4540000 2.784000 2.792000 2.9120000 2.904000 2.7340000 2.4320000 2.7980000 2.9100000 2.7600000 2.2780000 2.8940000 2.7900000 2.9000000 2.8920000 2.3640000 2.7640000 2.5520000 2.9080000 2.7400000 3.0980000 2.4460000 1.1340000 2.9120000 2.8980000 2.3720000 2.2380000 2.9180000 2.5440000 2.9200000 2.324000 2.3880000 2.4620000 2.606000 2.930000 2.9220000 2.6460000 2.9260000 2.6320000 2.850000 2.824000 2.5660000 2.9140000 2.506000 3.2640000 3.2920000 2.8620000 2.9000000 2.2840000 2.7320000 2.8020000 2.9220000 2.2820000 2.2840000 2.8920000 2.8520000 2.380000 2.754000 2.2740000 2.8120000 2.2360000 2.7500000 2.6000000 2.5920000 2.4440000 2.9360000 2.7720000 2.3140000 -sd 0.7402648 0.3606885 0.4309611 0.5182791 0.5291162 0.5156431 0.3653093 0.387557 0.9204077 0.3630421 0.3468207 0.420797 0.3457095 0.4478584 0.9773712 0.4275109 0.9000802 0.9427628 0.5525528 0.9381237 0.5915199 0.3271545 0.3668148 0.4435058 0.5536362 0.4448368 0.5611718 0.5577618 0.5368057 0.5575893 0.3574795 0.4620899 0.5370856 0.601721 0.597872 0.2973766 0.314614 0.4468146 0.8066457 0.5847048 0.3195375 0.9696806 0.5704093 0.3391076 0.9756042 0.3319946 0.3472422 0.9280229 0.6333295 0.5017964 0.3282552 0.4481089 0.9951767 0.5288131 0.4985389 0.3584871 0.9947739 0.9270031 0.5271734 0.3024218 0.8863607 0.3373835 0.944886 0.7840187 0.5377121 0.497251 0.277947 0.2758045 0.9329656 0.2769068 0.5148847 0.357429 0.556456 0.8984757 0.3617537 0.862236 0.9654887 0.9573762 0.3452454 0.3259025 0.5899492 0.4478584 0.5860742 0.3034801 0.5959751 0.5831158 0.3170758 0.3719849 0.921574 0.637414 0.5324387 0.5703566 0.5184995 0.4471016 0.9130539 0.5040279 0.5435576 0.2608449 0.4294005 0.9493189 -2.5% 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.475000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 1.000000 2.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.000000 2.0000000 1.4750000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 -97.5% 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.000000 3.0000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.000000 4.0000000 3.0000000 4.000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 - k[701] k[702] k[703] k[704] k[705] k[706] k[707] k[708] k[709] k[710] k[711] k[712] k[713] k[714] k[715] k[716] k[717] k[718] k[719] k[720] k[721] k[722] k[723] k[724] k[725] k[726] k[727] k[728] k[729] k[730] k[731] k[732] k[733] k[734] k[735] k[736] k[737] k[738] k[739] k[740] k[741] k[742] k[743] k[744] k[745] k[746] k[747] k[748] k[749] k[750] k[751] k[752] k[753] k[754] k[755] k[756] k[757] k[758] k[759] k[760] k[761] k[762] k[763] k[764] k[765] k[766] k[767] k[768] k[769] k[770] k[771] k[772] k[773] k[774] k[775] k[776] k[777] k[778] k[779] k[780] k[781] k[782] k[783] k[784] k[785] k[786] k[787] k[788] k[789] k[790] k[791] k[792] k[793] k[794] k[795] k[796] k[797] k[798] k[799] k[800] -mean 3.1500000 2.3740000 2.9340000 2.726000 2.2940000 2.356000 2.5700000 2.464000 1.6860000 2.6900000 3.3080000 2.3540000 2.3900000 2.7800000 2.3920000 2.6180000 2.8940000 2.8980000 2.3660000 2.5920000 2.4200000 2.3660000 3.2920000 2.6440000 2.3320000 1.6600000 2.2560000 1.644000 2.9220000 2.5640000 2.8520000 2.8260000 2.3980000 2.3320000 2.2640000 2.3360000 2.6140000 2.9240000 2.9440000 2.3720000 2.4520000 2.7280000 1.1720000 2.9320000 2.2640000 2.9420000 2.7140000 2.9280000 2.7900000 2.7600000 2.8520000 2.8220000 2.450000 1.660000 1.1920000 2.8980000 2.3380000 2.4180000 2.3300000 1.6660000 2.4820000 2.360000 2.6080000 2.406000 2.9240000 2.8480000 2.356000 2.7540000 2.9680000 2.904000 2.8640000 2.2800000 2.3240000 2.858000 2.2980000 2.5760000 3.9760000 2.7720000 2.9200000 2.8600000 2.7080000 2.9040000 3.0480000 1.6440000 2.3180000 2.7700000 2.8620000 2.8500000 2.8280000 2.6220000 2.9160000 1.6740000 2.8480000 2.4700000 2.2720000 2.7460000 2.3740000 2.8040000 2.8800000 2.786000 -sd 0.9886632 0.7741593 0.2716461 0.675156 0.4980563 0.556672 0.9026237 0.548914 0.9493189 0.4629564 0.9523392 0.5378313 0.7868534 0.6102005 0.9143347 0.5104872 0.3391076 0.3343944 0.5768097 0.5119181 0.8030503 0.5662909 0.9573762 0.4997635 0.5462351 0.9413673 0.5468657 0.933277 0.2899727 0.5043935 0.3554556 0.5443497 0.5139302 0.5571327 0.5718023 0.7239836 0.5113972 0.3262466 0.2775212 0.5746969 0.5255935 0.4716576 0.5612897 0.2748219 0.6221561 0.2660405 0.9581441 0.3016587 0.5853078 0.4367855 0.3554556 0.3932242 0.547814 0.939236 0.5897726 0.3283468 0.9367556 0.8053404 0.9438653 0.9424545 0.5387546 0.929585 0.5204592 0.793993 0.2939333 0.3649141 0.556672 0.4448368 0.9984838 0.314614 0.3489233 0.5119807 0.9406347 0.355089 0.9502642 0.5106324 0.2179886 0.6203109 0.3603605 0.3530571 0.6926005 0.3271055 0.9998477 0.9354218 0.6706531 0.4306634 0.3452454 0.3684719 0.5319605 0.5095442 0.2985335 0.9452571 0.3703651 0.5456147 0.6251381 0.4357336 0.9207712 0.5711711 0.9917495 0.613969 -2.5% 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 2.000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.000000 2.0000000 2.000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.000000 1.0000000 2.0000000 4.0000000 1.0000000 1.4750000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.000000 -97.5% 4.0000000 4.0000000 3.0000000 3.000000 3.0000000 4.000000 4.0000000 3.525000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.525000 3.000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 4.000000 3.0000000 3.0000000 4.000000 3.0000000 4.0000000 3.000000 3.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.000000 - k[801] k[802] k[803] k[804] k[805] k[806] k[807] k[808] k[809] k[810] k[811] k[812] k[813] k[814] k[815] k[816] k[817] k[818] k[819] k[820] k[821] k[822] k[823] k[824] k[825] k[826] k[827] k[828] k[829] k[830] k[831] k[832] k[833] k[834] k[835] k[836] k[837] k[838] k[839] k[840] k[841] k[842] k[843] k[844] k[845] k[846] k[847] k[848] k[849] k[850] k[851] k[852] k[853] k[854] k[855] k[856] k[857] k[858] k[859] k[860] k[861] k[862] k[863] k[864] k[865] k[866] k[867] k[868] k[869] k[870] k[871] k[872] k[873] k[874] k[875] k[876] k[877] k[878] k[879] k[880] k[881] k[882] k[883] k[884] k[885] k[886] k[887] k[888] k[889] k[890] k[891] k[892] k[893] k[894] k[895] k[896] k[897] k[898] k[899] k[900] -mean 2.3100000 2.3920000 2.9540000 2.3300000 2.4580000 3.3120000 2.2480000 1.6700000 2.7880000 2.7460000 2.2640000 2.7680000 2.9040000 2.448000 2.4500000 2.8600000 2.5020000 2.8040000 2.8920000 3.2840000 2.3600000 2.4540000 2.8940000 2.6180000 2.4000000 2.9260000 2.7480000 2.7600000 2.8440000 2.8400000 2.9140000 2.6080000 2.2780000 2.9240000 2.7300000 2.9000000 2.6260000 2.3200000 2.7540000 2.8280000 1.5900000 2.7680000 2.2760000 2.3100000 2.5800000 2.2760000 2.8560000 2.5980000 2.244000 2.4460000 2.3140000 2.2820000 2.9060000 2.9100000 2.766000 2.2180000 2.8600000 1.6820000 2.9100000 2.7520000 2.3640000 2.9200000 2.4820000 2.9320000 2.2700000 2.3140000 3.1580000 2.3180000 1.6160000 2.8200000 2.8660000 2.8920000 2.2600000 1.6100000 2.6080000 2.9140000 2.7460000 2.3380000 2.9080000 2.8200000 2.850000 2.9160000 2.5760000 2.9040000 2.8900000 2.7580000 2.7740000 2.9080000 2.7640000 2.5120000 2.2920000 2.73200 2.4400000 2.4780000 2.7540000 2.3520000 2.6420000 2.9000000 2.2080000 2.8980000 -sd 0.5161714 0.9165239 0.9989393 0.5307196 0.5374138 0.9510336 0.5357893 0.9438653 0.6064582 0.4403087 0.5503724 0.4272483 0.3331756 0.525212 0.5329804 0.3586883 0.8599273 0.5850372 0.4057898 0.9597845 0.9274267 0.5219472 0.3988511 0.4985711 0.5182056 0.2620331 0.4482162 0.4367855 0.3740907 0.3723941 0.3013064 0.5356396 0.6304848 0.2727137 0.4488909 0.3196942 0.4925538 0.7032703 0.6405502 0.3933516 0.9119008 0.6252663 0.6361079 0.5352317 0.9062243 0.6698667 0.3735547 0.5028934 0.597644 0.5211787 0.7017042 0.6222945 0.3760943 0.3607774 0.629314 0.5613432 0.3473345 0.9479838 0.3318437 0.4414587 0.7540831 0.3190668 0.5461433 0.9966758 0.6179512 0.5440551 0.9874138 0.7027544 0.9242578 0.3845722 0.3409935 0.4008208 0.5630578 0.9206493 0.5127004 0.3143272 0.6467771 0.9410245 0.3685208 0.5515509 0.373871 0.3481183 0.4987198 0.3620029 0.3437096 0.4333724 0.6129236 0.3630421 0.6333295 0.8691184 0.6660505 0.45672 0.5394274 0.5459965 0.9692073 0.5372833 0.5199776 0.3196942 0.9708868 0.3897432 -2.5% 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.4750000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.00000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 -97.5% 3.5250000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.00000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 - k[901] k[902] k[903] k[904] k[905] k[906] k[907] k[908] k[909] k[910] k[911] k[912] k[913] k[914] k[915] k[916] k[917] k[918] k[919] k[920] k[921] k[922] k[923] k[924] k[925] k[926] k[927] k[928] k[929] k[930] k[931] k[932] k[933] k[934] k[935] k[936] k[937] k[938] k[939] k[940] k[941] k[942] k[943] k[944] k[945] k[946] k[947] k[948] k[949] k[950] k[951] k[952] k[953] k[954] k[955] k[956] k[957] k[958] k[959] k[960] k[961] k[962] k[963] k[964] k[965] k[966] k[967] k[968] k[969] k[970] k[971] k[972] k[973] k[974] k[975] k[976] k[977] k[978] k[979] k[980] k[981] k[982] k[983] k[984] k[985] k[986] k[987] k[988] k[989] k[990] k[991] k[992] k[993] k[994] k[995] k[996] k[997] k[998] k[999] k[1000] mu[1] -mean 2.8940000 2.39000 1.7120000 2.2560000 2.8740000 2.902000 2.7840000 1.1680000 2.2960000 2.9360000 2.9160000 2.4720000 2.9120000 2.3280000 1.1800000 1.1840000 2.7480000 2.898000 2.8200000 2.89000 2.7700000 2.8940000 2.8420000 2.768000 2.3600000 2.9240000 2.45800 2.9000000 2.6160000 2.8660000 2.4160000 3.1000000 2.8940000 2.3320000 2.3760000 2.9120000 2.616000 1.152000 2.4540000 2.8300000 3.4880000 2.6160000 2.2840000 2.8420000 2.5860000 2.6000000 2.9120000 2.2840000 2.9300000 2.2400000 2.256000 2.9000000 2.6240000 2.4460000 2.7400000 1.188000 2.9200000 2.3480000 2.2880000 2.4420000 2.2540000 2.3900000 2.7840000 2.3740000 2.9060000 2.2740000 2.7380000 1.1400000 2.3200000 2.256000 2.902000 2.8420000 2.7300000 2.924000 2.910000 2.5960000 2.4620000 1.1400000 2.9040000 2.9080000 2.3820000 2.7520000 2.9160000 2.2560000 2.2640000 2.8780000 2.2360000 2.9060000 2.8100000 2.7500000 2.8960000 2.406000 2.8520000 2.9040000 2.4300000 2.8420000 2.3720000 1.1800000 2.3460000 2.8760000 1.0871229 -sd 0.3886723 0.91847 0.9585895 0.5541463 0.3498124 0.310788 0.4215441 0.5553312 0.5771049 0.2608449 0.3051725 0.5602175 0.3528527 0.5412966 0.5729367 0.5786308 0.4391831 0.384567 0.5515509 0.36629 0.6244035 0.3834816 0.3759237 0.618823 0.5576935 0.3323325 0.54113 0.3876862 0.9155438 0.3468207 0.9058616 0.9959839 0.4136499 0.7147011 0.9232165 0.2905595 0.922087 0.530527 0.5295705 0.5456147 0.8737178 0.4990412 0.6574256 0.3705545 0.9056735 0.9152461 0.3584871 0.6482163 0.2631321 0.5431298 0.605969 0.3824821 0.5090602 0.5250098 0.4481089 0.584242 0.2859432 0.9362955 0.5845849 0.5207479 0.5496255 0.7868534 0.6050423 0.9229451 0.3120236 0.6353987 0.4446926 0.5108051 0.5311914 0.605969 0.310788 0.3705545 0.4577325 0.313721 0.306738 0.4911889 0.5414262 0.5108051 0.3209205 0.3574795 0.9218327 0.4504462 0.3594474 0.5431888 0.6025198 0.3336745 0.5374772 0.3707275 0.5679305 0.6421251 0.4117318 0.791465 0.3719849 0.3209205 0.5419293 0.3651063 0.5533791 0.5729367 0.9338244 0.3476575 0.3031380 -2.5% 1.4750000 1.00000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.000000 1.0000000 2.00000 1.0000000 1.4750000 2.0000000 1.000000 2.0000000 2.0000000 2.00000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.4750000 2.0000000 2.0000000 2.0000000 2.0000000 1.4750000 1.0000000 1.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 0.5772989 -97.5% 3.0000000 3.00000 3.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.00000 3.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.00000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.000000 3.000000 3.0000000 3.0000000 3.000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 1.7233433 - mu[2] mu[3] mu[4] mu0 p[1] p[2] p[3] p[4] tau -mean 10.8357255 5.2819980 16.850914 8.934835 0.09202794 0.3039133 0.54045394 0.06360484 13.616054 -sd 0.6099564 0.3098964 1.148652 9.064099 0.02193831 0.0409123 0.03645824 0.02354226 13.338143 -2.5% 9.7471240 4.6887234 14.656299 -5.723290 0.05422392 0.2274665 0.47140352 0.02618860 4.218013 -97.5% 12.0542618 5.7390441 19.183040 26.869554 0.13787768 0.3876429 0.60641453 0.11764065 45.227606 + k[1] k[2] k[3] k[4] k[5] k[6] k[7] k[8] k[9] k[10] k[11] k[12] k[13] k[14] k[15] k[16] k[17] k[18] k[19] k[20] k[21] k[22] k[23] k[24] k[25] k[26] k[27] k[28] k[29] k[30] k[31] k[32] k[33] k[34] k[35] k[36] k[37] k[38] k[39] k[40] k[41] k[42] k[43] k[44] k[45] k[46] k[47] k[48] k[49] k[50] k[51] k[52] k[53] k[54] k[55] k[56] k[57] k[58] k[59] k[60] k[61] k[62] k[63] k[64] k[65] k[66] k[67] k[68] k[69] k[70] k[71] k[72] k[73] k[74] k[75] k[76] k[77] k[78] k[79] k[80] k[81] k[82] k[83] k[84] k[85] k[86] k[87] k[88] k[89] k[90] k[91] k[92] k[93] k[94] k[95] k[96] k[97] k[98] k[99] k[100] +mean 2.4460000 1.9460000 2.7240000 2.3780000 2.7240000 3.9040000 2.4400000 3.8960000 2.6060000 2.8060000 2.5880000 2.6940000 2.5980000 2.7120000 2.6840000 2.3800000 1.2520000 2.5880000 2.7960000 2.3480000 2.3600000 2.5940000 3.5760000 2.3300000 2.4660000 2.3420000 2.4480000 2.450000 1.9080000 2.3940000 2.5680000 2.4440000 2.3160000 3.836000 2.7260000 2.8000000 3.3400000 2.4140000 2.50400 2.4200000 1.2860000 2.5340000 2.3560000 2.3800000 2.3500000 2.784000 1.3240000 2.8200000 2.6360000 2.9720000 2.9480000 2.3680000 2.6080000 3.7560000 2.3580000 2.9320000 2.4460000 1.2980000 2.7980000 2.5720000 2.4740000 3.9640000 2.7880000 2.6120000 2.546000 2.3720000 2.7160000 1.9540000 2.3600000 2.6880000 2.4580000 3.1960000 2.7180000 2.4720000 2.7100000 2.5920000 3.4560000 3.6240000 2.5880000 2.7600000 2.5700000 2.5700000 2.5840000 2.6840000 3.6200000 3.1780000 1.9880000 2.5020000 2.8240000 2.7560000 2.6040000 2.4440000 2.7140000 2.790000 2.7700000 2.4340000 2.3580000 2.4580000 2.5780000 2.8200000 +sd 0.5250098 0.9924989 0.4607043 0.5794718 0.9431878 0.4279606 0.5319454 0.4444988 0.7667724 0.4948269 0.5086979 0.4656153 0.7807786 0.4706368 0.9369631 0.5587705 0.6613401 0.5086979 0.4180113 0.7098618 0.6567058 0.5193298 0.8182684 0.6081445 0.5379803 0.5564668 0.8003606 0.790411 0.9886814 0.5860468 0.7915992 0.7668377 0.5768965 0.549279 0.4597246 0.4102885 0.9413673 0.5994353 0.84107 0.7434416 0.6994157 0.8592559 0.6309201 0.5551724 0.5441435 0.416763 0.7349178 0.4899798 0.7514208 0.9925656 0.9976405 0.6460734 0.5048224 0.6552272 0.6439451 0.9926463 0.5250098 0.7114833 0.4916089 0.5151181 0.5384272 0.2661685 0.4375373 0.5079094 0.865669 0.5781735 0.4730662 0.9989393 0.6412663 0.4971421 0.5448208 0.9795422 0.4635793 0.5420365 0.4586073 0.7789413 0.8886188 0.7822069 0.4967389 0.4503394 0.5153943 0.5035943 0.4974323 0.9348218 0.7853876 0.9839982 0.9989253 0.8338985 0.4790438 0.4436865 0.7644297 0.5472321 0.9518487 0.431593 0.4488909 0.7555035 0.7091133 0.5336718 0.5142421 0.4194662 +2.5% 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.00000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 4.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 +97.5% 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.000000 3.0000000 3.0000000 4.0000000 4.0000000 4.00000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.5250000 4.0000000 3.000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 + k[101] k[102] k[103] k[104] k[105] k[106] k[107] k[108] k[109] k[110] k[111] k[112] k[113] k[114] k[115] k[116] k[117] k[118] k[119] k[120] k[121] k[122] k[123] k[124] k[125] k[126] k[127] k[128] k[129] k[130] k[131] k[132] k[133] k[134] k[135] k[136] k[137] k[138] k[139] k[140] k[141] k[142] k[143] k[144] k[145] k[146] k[147] k[148] k[149] k[150] k[151] k[152] k[153] k[154] k[155] k[156] k[157] k[158] k[159] k[160] k[161] k[162] k[163] k[164] k[165] k[166] k[167] k[168] k[169] k[170] k[171] k[172] k[173] k[174] k[175] k[176] k[177] k[178] k[179] k[180] k[181] k[182] k[183] k[184] k[185] k[186] k[187] k[188] k[189] k[190] k[191] k[192] k[193] k[194] k[195] k[196] k[197] k[198] k[199] k[200] +mean 2.7160000 2.434000 1.268000 2.4060000 2.676000 2.5980000 4 2.4440000 3.1300000 2.5220000 2.4520000 2.7740000 2.5580000 2.5840000 3.9480000 3.9480000 3.7880000 2.7680000 1.966000 2.5580000 2.7100000 2.580000 2.0280000 2.8480000 2.7300000 2.5720000 2.7960000 2.6620000 2.4880000 2.5860000 2.4520000 3.6840000 2.4320000 1.3200000 2.4100000 1.2800000 2.8180000 2.3960000 2.7140000 2.3480000 2.4780000 2.4800000 2.8020000 2.4860000 2.6600000 2.7980000 2.5840000 2.7240000 2.6940000 2.4600000 2.6120000 2.4120000 3.1520000 2.356000 2.3560000 2.8060000 2.6240000 2.4960000 2.3960000 2.7800000 3.2280000 2.3540000 2.3900000 2.7960000 2.7180000 2.7980000 2.5600000 2.0320000 2.4740000 2.7960000 1.968000 2.9140000 2.3620000 3.8680000 2.4700000 2.6220000 2.5440000 2.7140000 2.6080000 2.8100000 3.9240000 2.4740000 1.9440000 2.7120000 2.3540000 3.7520000 2.4680000 2.8580000 2.3960000 2.4720000 2.6800000 2.728000 2.5720000 2.5980000 2.8160000 2.4000000 2.3600000 2.6260000 2.4280000 2.4140000 +sd 0.9492873 0.546114 0.681987 0.7281651 0.936364 0.5185883 0 0.5323823 0.9894737 0.8549725 0.5516381 0.5324387 0.5129935 0.5171837 0.3185891 0.3185891 0.6162918 0.9633442 0.989344 0.5051201 0.4799591 0.525502 0.9885193 0.9812348 0.4706836 0.4993142 0.4321545 0.9281589 0.5572046 0.5168542 0.5255935 0.7302126 0.5271924 0.7339464 0.7583206 0.6917782 0.3913852 0.7377483 0.4654431 0.6226326 0.5496547 0.5423914 0.4232662 0.5462607 0.9306623 0.5115852 0.7694987 0.4693235 0.4741452 0.5300584 0.5118398 0.7585557 0.9873427 0.556672 0.5778684 0.4203777 0.9166813 0.5390409 0.5760622 0.4472136 0.9725779 0.6430482 0.5887217 0.4367671 0.4548514 0.4354023 0.5166564 0.9944616 0.8164909 0.4720484 0.986368 0.9942742 0.5653275 0.4970615 0.8309518 0.5055959 0.5221199 0.4654431 0.7664927 0.4882384 0.3827754 0.5494796 0.9913777 0.4790773 0.6461571 0.6598233 0.5418147 0.3976837 0.7377483 0.8213191 0.4796458 0.458734 0.7912447 0.5262603 0.4080159 0.6639893 0.5468071 0.5163887 0.7522951 0.7427701 +2.5% 2.0000000 2.000000 1.000000 2.0000000 2.000000 2.0000000 4 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.9500000 2.9500000 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 +97.5% 4.0000000 3.525000 3.000000 4.0000000 4.000000 3.0000000 4 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.5250000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.5250000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 + k[201] k[202] k[203] k[204] k[205] k[206] k[207] k[208] k[209] k[210] k[211] k[212] k[213] k[214] k[215] k[216] k[217] k[218] k[219] k[220] k[221] k[222] k[223] k[224] k[225] k[226] k[227] k[228] k[229] k[230] k[231] k[232] k[233] k[234] k[235] k[236] k[237] k[238] k[239] k[240] k[241] k[242] k[243] k[244] k[245] k[246] k[247] k[248] k[249] k[250] k[251] k[252] k[253] k[254] k[255] k[256] k[257] k[258] k[259] k[260] k[261] k[262] k[263] k[264] k[265] k[266] k[267] k[268] k[269] k[270] k[271] k[272] k[273] k[274] k[275] k[276] k[277] k[278] k[279] k[280] k[281] k[282] k[283] k[284] k[285] k[286] k[287] k[288] k[289] k[290] k[291] k[292] k[293] k[294] k[295] k[296] k[297] k[298] k[299] +mean 3.5480000 2.6020000 2.7820000 1.2700000 2.7280000 2.8380000 2.5900000 2.7100000 2.8320000 2.4020000 1.9240000 2.7500000 2.3240000 2.464000 2.0020000 2.7960000 2.7080000 3.5840000 2.6780000 2.8220000 2.3880000 3.7960000 2.6040000 2.3220000 2.8060000 2.4420000 2.6300000 2.8220000 1.9780000 3.9400000 2.8020000 2.366000 3.5200000 2.5940000 3.6320000 2.6900000 2.8200000 2.5360000 2.444000 2.5160000 2.4000000 2.3060000 2.4260000 2.6160000 1.2660000 2.5160000 3.1860000 2.8140000 2.5680000 2.4680000 2.6820000 2.7260000 2.6980000 2.7180000 2.8300000 2.7220000 2.6080000 2.6900000 2.3800000 2.3420000 2.8180000 2.940000 2.7300000 2.7220000 2.7860000 2.6100000 2.8180000 2.7900000 3.9920000 2.8180000 2.6360000 3.4440000 3.9760000 2.614000 2.8160000 2.3280000 1.9580000 2.3660000 2.5940000 2.5700000 2.7860000 1.2700000 2.5700000 2.7940000 2.4480000 1.2740000 1.9400000 1.8800000 1.9180000 2.8120000 2.7120000 2.3540000 2.8080000 2.7900000 1.9480000 2.5780000 2.5520000 2.5980000 2.6800000 +sd 0.8373161 0.5060714 0.4322611 0.6826654 0.9506964 0.3950546 0.5161714 0.4586073 0.4518676 0.7412793 0.9880164 0.4471016 0.6195091 0.548914 0.9939678 0.4180113 0.4681606 0.8125666 0.9381237 0.4719762 0.5951171 0.6059028 0.4976739 0.6317549 0.4155832 0.8099061 0.9136572 0.4803931 0.9937258 0.3415161 0.4808935 0.721863 0.8550217 0.5231744 0.7757445 0.4672651 0.4146612 0.5227337 0.784918 0.5748085 0.7273638 0.6042302 0.5225611 0.7653205 0.6783541 0.8191985 0.9784266 0.4239853 0.5271924 0.5343661 0.4704451 0.4553446 0.9426416 0.4592361 0.4664066 0.4659939 0.5087767 0.4715344 0.7270882 0.5528538 0.4064559 0.993163 0.4577325 0.4787216 0.4342593 0.5004006 0.4014952 0.5161714 0.1263643 0.4830556 0.9236939 0.8969242 0.2179886 0.515301 0.4030744 0.6206338 0.9930803 0.5733249 0.5269909 0.5231131 0.4478897 0.6797235 0.7812302 0.4146177 0.5290139 0.6869263 0.9891192 0.9836336 0.9844869 0.4110888 0.4706368 0.6461571 0.4236685 0.4362116 0.9855143 0.5258031 0.8745431 0.5068627 0.4920205 +2.5% 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 4.0000000 1.0000000 2.0000000 2.0000000 4.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 +97.5% 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.525000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 + k[300] k[301] k[302] k[303] k[304] k[305] k[306] k[307] k[308] k[309] k[310] k[311] k[312] k[313] k[314] k[315] k[316] k[317] k[318] k[319] k[320] k[321] k[322] k[323] k[324] k[325] k[326] k[327] k[328] k[329] k[330] k[331] k[332] k[333] k[334] k[335] k[336] k[337] k[338] k[339] k[340] k[341] k[342] k[343] k[344] k[345] k[346] k[347] k[348] k[349] k[350] k[351] k[352] k[353] k[354] k[355] k[356] k[357] k[358] k[359] k[360] k[361] k[362] k[363] k[364] k[365] k[366] k[367] k[368] k[369] k[370] k[371] k[372] k[373] k[374] k[375] k[376] k[377] k[378] k[379] k[380] k[381] k[382] k[383] k[384] k[385] k[386] k[387] k[388] k[389] k[390] k[391] k[392] k[393] k[394] k[395] k[396] k[397] k[398] k[399] k[400] +mean 2.6840000 2.4500000 2.8060000 2.3840000 2.848000 2.7800000 2.7080000 2.3480000 2.0180000 2.4900000 2.9560000 2.4980000 2.3660000 2.778000 3.368000 2.7920000 2.3740000 2.5960000 2.0080000 2.8180000 2.7140000 2.7020000 2.73200 2.3980000 2.5940000 2.6800000 2.7060000 2.8120000 2.4780000 2.4560000 2.6940000 2.7220000 3.1700000 1.264000 2.7480000 2.4500000 2.3860000 2.7380000 2.7720000 3.2060000 2.9160000 3.3500000 4 2.7160000 2.534000 2.7240000 2.3560000 2.8300000 3.4180000 2.3240000 2.4820000 2.52600 2.782000 2.5640000 2.7500000 2.6500000 2.8040000 2.8220000 2.7880000 2.3500000 2.4940000 2.6900000 2.3420000 2.6820000 2.3940000 2.8200000 2.5480000 2.3740000 2.3360000 2.7940000 2.4380000 3.91600 2.5640000 2.6320000 2.5620000 2.3140000 2.7220000 2.5180000 2.5740000 1.9340000 2.5140000 2.4640000 2.7400000 2.7160000 2.51000 2.5000000 2.7120000 2.380000 2.8080000 2.5920000 2.3540000 2.7800000 2.9680000 2.3940000 2.8200000 2.9940000 2.4160000 2.5340000 3.1960000 2.4640000 2.7980000 +sd 0.4739126 0.8029879 0.5068311 0.5667331 0.983275 0.5405408 0.4595197 0.5361632 0.9877385 0.8290202 0.9960161 0.5573305 0.6456607 0.443971 0.930757 0.4484844 0.5751396 0.7654776 0.9969573 0.4746858 0.9455115 0.4707858 0.45672 0.7407384 0.7631046 0.9377413 0.4732991 0.4061847 0.5532886 0.5372535 0.9413481 0.9478993 0.9833789 0.671719 0.9541051 0.7593769 0.5741632 0.9587463 0.4695284 0.9785085 0.9914181 0.9366187 0 0.4601821 0.861585 0.4563337 0.6944267 0.4488909 0.9082543 0.6227356 0.5534334 0.85486 0.441436 0.5043935 0.9577759 0.9236917 0.4920042 0.4032881 0.5249831 0.6514205 0.5352916 0.9357624 0.6855479 0.4704451 0.5722052 0.4858726 0.8678723 0.6892562 0.5326081 0.4381186 0.7895586 0.40158 0.7944598 0.7414009 0.7895586 0.5933871 0.4616734 0.8574067 0.7834229 0.9877222 0.8480816 0.5415631 0.4569657 0.4558064 0.84102 0.5500956 0.4576843 0.660661 0.5016366 0.9028656 0.5597416 0.4560877 0.9964747 0.5791674 0.4048801 0.9979759 0.7456917 0.8545786 0.9795422 0.5378499 0.5193606 +2.5% 2.0000000 2.0000000 1.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.00000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 4 2.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.00000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.00000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.00000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 +97.5% 3.0000000 4.0000000 3.0000000 4.0000000 4.000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.000000 4.000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.00000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4 3.0000000 4.000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.00000 3.000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.00000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.00000 3.5250000 3.0000000 4.000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 + k[401] k[402] k[403] k[404] k[405] k[406] k[407] k[408] k[409] k[410] k[411] k[412] k[413] k[414] k[415] k[416] k[417] k[418] k[419] k[420] k[421] k[422] k[423] k[424] k[425] k[426] k[427] k[428] k[429] k[430] k[431] k[432] k[433] k[434] k[435] k[436] k[437] k[438] k[439] k[440] k[441] k[442] k[443] k[444] k[445] k[446] k[447] k[448] k[449] k[450] k[451] k[452] k[453] k[454] k[455] k[456] k[457] k[458] k[459] k[460] k[461] k[462] k[463] k[464] k[465] k[466] k[467] k[468] k[469] k[470] k[471] k[472] k[473] k[474] k[475] k[476] k[477] k[478] k[479] k[480] k[481] k[482] k[483] k[484] k[485] k[486] k[487] k[488] k[489] k[490] k[491] k[492] k[493] k[494] k[495] k[496] k[497] k[498] k[499] k[500] +mean 2.5540000 2.8060000 2.6220000 2.452000 1.2700000 3.9760000 2.6120000 2.3900000 2.380000 2.5880000 2.6980000 2.7920000 2.8100000 2.2700000 2.3900000 2.8040000 1.8980000 2.3180000 2.8000000 2.5380000 3.3640000 2.5940000 2.4780000 2.5920000 1.290000 2.5800000 2.5740000 2.8080000 2.852000 2.5080000 1.3020000 3.6040000 2.8380000 2.3960000 2.3460000 3.9880000 2.4160000 3.836000 1.2720000 2.6180000 1.9500000 2.4540000 2.5960000 2.5620000 2.4040000 2.5840000 1.2800000 2.7880000 2.8320000 2.6040000 2.3900000 2.6740000 2.0320000 2.640000 3.8040000 2.5100000 2.3700000 1.2620000 1.2920000 2.7960000 2.3780000 2.5640000 2.7420000 2.6820000 2.3340000 2.8180000 2.6820000 2.7940000 2.3460000 2.3600000 2.5940000 2.458000 2.7360000 2.4980000 2.626000 2.824000 2.7940000 2.3880000 2.3280000 3.8680000 1.278000 2.7020000 1.3260000 2.586000 3.506000 2.5980000 2.7680000 2.8220000 2.7620000 3.6160000 2.768000 2.8060000 2.3280000 1.9640000 2.7740000 3.5800000 2.4940000 2.6500000 2.9180000 2.6960000 +sd 0.7901472 0.5107698 0.5288434 0.544324 0.6826654 0.2179886 0.5157402 0.5784195 0.678617 0.5086979 0.4682248 0.4302397 0.4715344 0.5636803 0.5749445 0.4218292 0.9846497 0.6646499 0.4293818 0.8637408 0.9323317 0.5076213 0.5311122 0.7789413 0.703484 0.5139341 0.5263821 0.4092321 0.417666 0.5536688 0.7154157 0.7977825 0.9867641 0.5999198 0.6380425 0.1546086 0.7616457 0.549279 0.6833373 0.7597568 0.9927089 0.5481651 0.5032798 0.7946187 0.7415415 0.5210441 0.6917782 0.5249831 0.4518676 0.5056633 0.7368758 0.4818593 0.9904231 0.512763 0.5952248 0.8386338 0.5742469 0.6739915 0.7040792 0.5048701 0.6602939 0.5238825 0.4559076 0.4746858 0.5243987 0.4209875 0.4661659 0.5099845 0.6285492 0.5683714 0.5115539 0.526108 0.9529534 0.8434565 0.504612 0.411537 0.4426691 0.5849276 0.6108701 0.4970615 0.688232 0.4665097 0.7381149 0.771878 0.862236 0.7599678 0.5429231 0.4845468 0.5458497 0.7885351 0.463255 0.5068311 0.6108701 0.9902855 0.4464556 0.8154323 0.8410581 0.9258588 0.9946127 0.4775354 +2.5% 1.0000000 1.0000000 2.0000000 2.000000 1.0000000 4.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 4.0000000 2.0000000 2.000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 1.0000000 1.000000 2.000000 1.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 +97.5% 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 4.0000000 3.0000000 4.0000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.000000 3.0000000 3.0000000 3.0000000 3.5250000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.5250000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.000000 4.0000000 4.0000000 3.000000 3.000000 3.0000000 4.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 + k[501] k[502] k[503] k[504] k[505] k[506] k[507] k[508] k[509] k[510] k[511] k[512] k[513] k[514] k[515] k[516] k[517] k[518] k[519] k[520] k[521] k[522] k[523] k[524] k[525] k[526] k[527] k[528] k[529] k[530] k[531] k[532] k[533] k[534] k[535] k[536] k[537] k[538] k[539] k[540] k[541] k[542] k[543] k[544] k[545] k[546] k[547] k[548] k[549] k[550] k[551] k[552] k[553] k[554] k[555] k[556] k[557] k[558] k[559] k[560] k[561] k[562] k[563] k[564] k[565] k[566] k[567] k[568] k[569] k[570] k[571] k[572] k[573] k[574] k[575] k[576] k[577] k[578] k[579] k[580] k[581] k[582] k[583] k[584] k[585] k[586] k[587] k[588] k[589] k[590] k[591] k[592] k[593] k[594] k[595] k[596] k[597] k[598] k[599] k[600] +mean 2.4080000 2.8020000 2.3700000 2.3460000 2.722000 2.7900000 2.6900000 2.5860000 1.3840000 2.3240000 1.9480000 2.718000 3.3900000 2.4600000 2.7800000 2.8200000 2.6860000 2.8300000 2.4920000 2.4080000 3.9840000 2.7860000 2.7880000 2.0080000 2.4840000 2.5180000 2.3640000 2.5520000 2.5960000 3.3600000 2.8080000 1.8820000 1.3180000 2.3800000 2.8280000 2.6960000 1.9880000 2.4440000 2.8220000 3.7520000 2.3260000 1.994000 2.7880000 2.6280000 2.6280000 2.5820000 2.3980000 2.7060000 2.5120000 2.5980000 2.9160000 2.0220000 2.564000 2.8200000 2.4940000 2.4740000 2.3420000 2.7740000 2.3780000 3.8400000 2.7900000 2.5060000 2.7140000 1.9380000 2.3840000 2.4640000 2.5680000 2.8180 3.9640000 2.3240000 1.290000 2.6240000 2.9800000 2.6080000 2.3980000 3.1540000 3.8160000 2.604000 2.4480000 2.4000000 2.3340000 2.4820000 2.824000 2.342000 2.3560000 2.8380000 2.3580000 2.7940000 3.9480000 2.5860000 2.4000000 2.7140000 2.7980000 2.7940000 2.4020000 2.8180000 2.6340000 2.6640000 2.7260000 2.48400 +sd 0.5196885 0.5285402 0.6526499 0.5682692 0.457312 0.5238787 0.4629564 0.5168542 0.7885351 0.6097274 0.9915959 0.948829 0.9206493 0.5300584 0.4381598 0.4733372 0.9365673 0.3916718 0.5427016 0.5849961 0.1783469 0.4342593 0.4466038 0.9888841 0.8337471 0.8503659 0.5658129 0.5213825 0.5032798 0.9338867 0.4770652 0.9869266 0.7307146 0.5729367 0.3984137 0.4690672 0.9908681 0.5247998 0.4803931 0.6598233 0.6070098 0.989911 0.4510686 0.7447989 0.7528277 0.5017764 0.5833323 0.4604215 0.8289936 0.4948593 0.9914181 0.9937258 0.527694 0.4604607 0.8290589 0.5384272 0.6370618 0.5286615 0.5654693 0.5431298 0.5004006 0.8481762 0.4611174 0.9920304 0.5596162 0.5525528 0.5078305 0.4162 0.2661685 0.5400067 0.703484 0.7534982 0.9947759 0.7664927 0.5764204 0.9880468 0.5786308 0.509611 0.5512747 0.7218324 0.6222688 0.5424615 0.448806 0.630739 0.6709429 0.4518455 0.6439451 0.4381186 0.3185891 0.7796279 0.7328534 0.4611174 0.5076529 0.4146177 0.7358525 0.4704451 0.9174571 0.9321168 0.4553446 0.83134 +2.5% 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 4.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000 4.0000000 2.0000000 1.000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.9500000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.00000 +97.5% 3.0000000 3.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000 4.0000000 4.0000000 3.000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.000000 4.0000000 4.0000000 4.0000000 3.0000000 3.000000 4.000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.00000 + k[601] k[602] k[603] k[604] k[605] k[606] k[607] k[608] k[609] k[610] k[611] k[612] k[613] k[614] k[615] k[616] k[617] k[618] k[619] k[620] k[621] k[622] k[623] k[624] k[625] k[626] k[627] k[628] k[629] k[630] k[631] k[632] k[633] k[634] k[635] k[636] k[637] k[638] k[639] k[640] k[641] k[642] k[643] k[644] k[645] k[646] k[647] k[648] k[649] k[650] k[651] k[652] k[653] k[654] k[655] k[656] k[657] k[658] k[659] k[660] k[661] k[662] k[663] k[664] k[665] k[666] k[667] k[668] k[669] k[670] k[671] k[672] k[673] k[674] k[675] k[676] k[677] k[678] k[679] k[680] k[681] k[682] k[683] k[684] k[685] k[686] k[687] k[688] k[689] k[690] k[691] k[692] k[693] k[694] k[695] k[696] k[697] k[698] k[699] k[700] +mean 2.8880000 2.6120000 2.4540000 2.4020000 2.3820000 2.4920000 2.8220000 2.7940000 2.5880000 2.8280000 2.5980000 2.4500000 2.7720000 2.4980000 3.5960000 2.4760000 3.3780000 2.6140000 2.2760000 2.6120000 2.8160000 2.8220000 2.5300000 2.4460000 2.3780000 2.4780000 2.334000 2.6840000 2.3880000 2.3700000 2.8160000 2.4740000 2.3240000 2.8280000 2.8000000 2.7260000 2.7280000 2.4300000 3.1200000 2.7800000 2.7500000 3.5960000 2.50000 2.6840000 3.5600000 2.658000 2.7100000 2.5920000 2.8020000 2.3900000 2.7060000 2.5020000 3.7880000 2.3640000 1.3120000 2.8220000 3.6860000 2.6220000 2.5460000 2.7160000 3.3480000 2.7700000 2.626000 3.1600000 2.3380000 2.3780000 2.7040000 2.7040000 3.3560000 2.7020000 2.3860000 2.6060000 2.7960000 3.4240000 2.7940000 3.506000 3.9080000 3.9000000 2.5460000 2.7040000 2.5080000 2.4540000 2.8260000 2.7300000 2.4860000 2.5000000 2.7300000 2.550 2.6360000 2.8300000 2.4000000 2.8020000 2.5340000 2.4440000 3.4080000 2.3520000 2.3540000 2.6780000 2.4800000 2.6140000 +sd 0.9866118 0.5079094 0.5590251 0.6075642 0.7163115 0.8362623 0.3932242 0.4471733 0.7638212 0.4034123 0.5068627 0.5367273 0.4609131 0.5353215 0.8037886 0.5532053 0.9256509 0.7497521 0.5553745 0.7659173 0.5006449 0.3880944 0.5114912 0.5437604 0.6541956 0.5568988 0.677763 0.9390995 0.7031678 0.7225955 0.4030744 0.5346922 0.5964356 0.4503572 0.5220585 0.4553446 0.4630819 0.5382187 0.9937682 0.5293017 0.4559779 0.8037886 0.82666 0.4739126 0.8293223 0.487351 0.4586073 0.7737787 0.5052787 0.5818738 0.4604215 0.5537231 0.6162918 0.6421282 0.7236735 0.3932242 0.7269532 0.7538252 0.8748799 0.4688108 0.9384334 0.4306634 0.734304 0.9860754 0.6360543 0.5897556 0.4741663 0.4656368 0.9354218 0.4750235 0.5879587 0.5131185 0.4928182 0.9065692 0.4335203 0.862236 0.4193898 0.4363264 0.5102987 0.4613129 0.8386553 0.5481651 0.4818593 0.4577325 0.8241131 0.8339009 0.4444041 0.502 0.7460678 0.4664066 0.7490809 0.5092294 0.8569204 0.5323823 0.9117008 0.5483588 0.6208502 0.4803931 0.5533646 0.7603685 +2.5% 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.00000 2.0000000 2.0000000 2.000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 +97.5% 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.00000 3.0000000 4.0000000 3.000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.5250000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 + k[701] k[702] k[703] k[704] k[705] k[706] k[707] k[708] k[709] k[710] k[711] k[712] k[713] k[714] k[715] k[716] k[717] k[718] k[719] k[720] k[721] k[722] k[723] k[724] k[725] k[726] k[727] k[728] k[729] k[730] k[731] k[732] k[733] k[734] k[735] k[736] k[737] k[738] k[739] k[740] k[741] k[742] k[743] k[744] k[745] k[746] k[747] k[748] k[749] k[750] k[751] k[752] k[753] k[754] k[755] k[756] k[757] k[758] k[759] k[760] k[761] k[762] k[763] k[764] k[765] k[766] k[767] k[768] k[769] k[770] k[771] k[772] k[773] k[774] k[775] k[776] k[777] k[778] k[779] k[780] k[781] k[782] k[783] k[784] k[785] k[786] k[787] k[788] k[789] k[790] k[791] k[792] k[793] k[794] k[795] k[796] k[797] k[798] k[799] k[800] +mean 3.8240000 3.146000 2.7080000 2.7940000 2.3440000 2.3740000 3.4260000 2.3580000 1.9680000 2.4920000 3.9320000 2.3300000 3.1600000 2.8180000 2.6200000 2.3880000 2.7120000 2.7220000 2.3500000 2.3640000 3.1760000 2.4180000 3.9000000 2.3460000 2.3460000 1.966000 2.4540000 2.0200000 2.6920000 2.3480000 2.6140000 2.8260000 2.3500000 2.3420000 2.5720000 2.9020000 2.3700000 2.8120000 2.8160000 2.3700000 2.3520000 2.5000000 1.2580000 2.6980000 2.7600000 2.7380000 3.5600000 2.7960000 2.7980000 2.4480000 2.5940000 2.5900000 2.2920000 1.9400000 1.2500000 2.7320000 2.6220000 3.0960000 2.5580000 1.9580000 2.3640000 2.6060000 2.3500000 3.2160000 2.698000 2.5940000 2.3900000 2.4480000 3.7400000 2.6960000 2.576000 2.4180000 2.5780000 2.5760000 2.5860000 2.3800000 4 2.7760000 2.8180000 2.5520000 2.780000 2.7200000 3.8440000 1.9420000 2.7260000 2.4780000 2.5820000 2.5840000 2.856000 2.3840000 2.6960000 1.9540000 2.5860000 2.3360000 2.7360000 2.4860000 2.6260000 2.8200000 3.7360000 2.8180000 +sd 0.5671572 0.989263 0.4638603 0.5177839 0.6975939 0.7205292 0.9045222 0.6653732 0.9964747 0.5463818 0.3628213 0.6826654 0.9860754 0.4661659 0.7488134 0.5745574 0.4620422 0.4529087 0.6931529 0.5658129 0.9833402 0.7618641 0.4363264 0.5503542 0.6951622 0.989344 0.8007286 0.9907387 0.4664452 0.5861597 0.5034988 0.4901065 0.6295177 0.6767214 0.8776997 0.9891171 0.5672244 0.4254621 0.4080159 0.7085931 0.6394637 0.5537267 0.6665768 0.4639251 0.9551044 0.4446926 0.8293223 0.4227783 0.5154876 0.5476274 0.5193298 0.5161714 0.6028789 0.9911431 0.6575445 0.4478584 0.7511621 0.9943649 0.7923962 0.9930803 0.6483399 0.7719818 0.5622564 0.9753187 0.476708 0.5115539 0.7286713 0.5512747 0.6732805 0.4690672 0.502722 0.7512688 0.7906746 0.5066927 0.7898428 0.5729367 0 0.5535674 0.4064559 0.4977867 0.525502 0.4626317 0.5368803 0.9882253 0.9469517 0.5532886 0.5097015 0.5054255 0.414255 0.5702582 0.4690672 0.9868291 0.5050883 0.6292025 0.9508482 0.5607431 0.7451406 0.4899798 0.6776595 0.4912827 +2.5% 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 4 1.0000000 2.0000000 2.0000000 1.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 1.0000000 +97.5% 4.0000000 4.000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.5250000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4 3.0000000 3.0000000 3.0000000 3.000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 + k[801] k[802] k[803] k[804] k[805] k[806] k[807] k[808] k[809] k[810] k[811] k[812] k[813] k[814] k[815] k[816] k[817] k[818] k[819] k[820] k[821] k[822] k[823] k[824] k[825] k[826] k[827] k[828] k[829] k[830] k[831] k[832] k[833] k[834] k[835] k[836] k[837] k[838] k[839] k[840] k[841] k[842] k[843] k[844] k[845] k[846] k[847] k[848] k[849] k[850] k[851] k[852] k[853] k[854] k[855] k[856] k[857] k[858] k[859] k[860] k[861] k[862] k[863] k[864] k[865] k[866] k[867] k[868] k[869] k[870] k[871] k[872] k[873] k[874] k[875] k[876] k[877] k[878] k[879] k[880] k[881] k[882] k[883] k[884] k[885] k[886] k[887] k[888] k[889] k[890] k[891] k[892] k[893] k[894] k[895] k[896] k[897] k[898] k[899] k[900] +mean 2.3780000 2.594000 3.7960000 2.4280000 2.3760000 3.888000 2.5100000 1.9120000 2.7840000 2.436000 2.4980000 2.4560000 2.762000 2.3680000 2.3720000 2.6080000 3.1940000 2.7500000 2.8240000 3.8760000 2.6140000 2.3100000 2.7980000 2.4000000 2.3000000 2.7080000 2.4580000 2.472000 2.6120000 2.5680000 2.7060000 2.3900000 2.750000 2.7120000 2.4720000 2.6900000 2.3680000 2.882000 2.8080000 2.5740000 1.9360000 2.8040000 2.8400000 2.4280000 3.3740000 2.8700000 2.6200000 2.3620000 2.7260000 2.3040000 2.8880000 2.7120000 2.7900000 2.7720000 2.8000000 2.6800000 2.5800000 1.9320000 2.7420000 2.4620000 2.840000 2.8040000 2.3520000 3.6520000 2.6560000 2.4460000 3.8040000 2.9600000 1.9940000 2.6000000 2.5780000 2.830000 2.5000000 1.9680000 2.3800000 2.7140000 2.776000 2.5960000 2.8060000 2.8100000 2.6040000 2.7940000 2.3780000 2.8240000 2.7060000 2.4660000 2.7980000 2.8040000 2.8140000 3.3760000 2.6420000 2.4320000 2.3700000 2.3500000 3.6120000 2.3540000 2.3560000 2.7320000 2.6480000 2.8080000 +sd 0.7267547 0.791465 0.6059028 0.7602447 0.6689686 0.460304 0.8386338 0.9890462 0.5233467 0.527694 0.8410772 0.5372535 0.440163 0.6491678 0.6248174 0.5048224 0.9789181 0.5658094 0.3966594 0.4827941 0.7577284 0.5887217 0.4117464 0.5938901 0.6022669 0.4808309 0.5374138 0.534591 0.5196114 0.5117615 0.4690458 0.5921159 0.959866 0.4620422 0.5493811 0.4672651 0.5702863 0.988955 0.4976256 0.5109267 0.9848064 0.5197348 0.9696806 0.7628761 0.9272776 0.9874463 0.5060753 0.5653275 0.9490656 0.6263934 0.9886409 0.9459628 0.4407818 0.4521337 0.4944583 0.9334574 0.5100198 0.9886004 0.4646157 0.5451149 0.984041 0.4312261 0.6518787 0.7589783 0.9248647 0.7773626 0.5952248 0.9921536 0.9939516 0.5104126 0.5063881 0.421254 0.8410796 0.9883976 0.5658979 0.4567507 0.523806 0.7810275 0.4298063 0.5161714 0.4976739 0.4288728 0.5897556 0.4258953 0.4690458 0.5228678 0.5036898 0.4122182 0.4978954 0.9275477 0.9227888 0.5195342 0.6557133 0.6544897 0.7916498 0.7026175 0.5383713 0.4523109 0.7301797 0.4283725 +2.5% 2.0000000 1.000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 1.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 1.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 1.000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 +97.5% 4.0000000 3.000000 4.0000000 4.0000000 4.0000000 4.000000 4.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 3.000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 4.0000000 4.000000 3.0000000 3.5250000 3.0000000 4.0000000 4.000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.000000 4.0000000 3.0000000 4.0000000 3.0000000 3.000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 + k[901] k[902] k[903] k[904] k[905] k[906] k[907] k[908] k[909] k[910] k[911] k[912] k[913] k[914] k[915] k[916] k[917] k[918] k[919] k[920] k[921] k[922] k[923] k[924] k[925] k[926] k[927] k[928] k[929] k[930] k[931] k[932] k[933] k[934] k[935] k[936] k[937] k[938] k[939] k[940] k[941] k[942] k[943] k[944] k[945] k[946] k[947] k[948] k[949] k[950] k[951] k[952] k[953] k[954] k[955] k[956] k[957] k[958] k[959] k[960] k[961] k[962] k[963] k[964] k[965] k[966] k[967] k[968] k[969] k[970] k[971] k[972] k[973] k[974] k[975] k[976] k[977] k[978] k[979] k[980] k[981] k[982] k[983] k[984] k[985] k[986] k[987] k[988] k[989] k[990] k[991] k[992] k[993] k[994] k[995] k[996] k[997] k[998] k[999] k[1000] +mean 2.8140000 2.5460000 1.9320000 2.5060000 2.5620000 2.7240000 2.4380000 1.2720000 2.492000 2.7560000 2.7340000 2.3340000 2.8120000 2.3780000 1.2460000 1.2820000 2.4800000 2.8140000 2.8320000 2.7060000 2.8160000 2.8060000 2.5540000 2.8420000 2.4420000 2.7960000 2.3460000 2.8060000 3.3020000 2.5940000 2.5840000 3.848000 2.8160000 2.9820000 2.6220000 2.7020000 3.4120000 1.3240000 2.3800000 2.8520000 3.91600 2.3720000 2.7380000 2.5840000 3.4320000 3.4320000 2.788000 2.734000 2.7300000 2.564000 2.6640000 2.8100000 2.3620000 2.3500000 2.4600000 1.2620000 2.718000 2.5920000 2.5580000 2.3680000 2.5320000 3.1680000 2.8000000 2.6440000 2.6920000 2.734000 2.4740000 1.2980000 2.3920000 2.6480000 2.7200000 2.5620000 2.464000 2.8340000 2.6900000 2.3980000 2.3700000 1.2440000 2.6980000 2.7800000 2.6420000 2.4540000 2.7960000 2.5040000 2.6920000 2.5540000 2.5280000 2.8040000 2.7900000 2.8160000 2.8400000 3.1100000 2.6080000 2.722000 2.3320000 2.5900000 2.4020000 1.3640000 2.5500000 2.606000 +sd 0.4095601 0.8057185 0.9845378 0.8505356 0.5125245 0.4563337 0.5392008 0.6862637 0.829042 0.4526298 0.4468146 0.6125311 0.4159351 0.7156398 0.6560066 0.6953006 0.5569744 0.4095601 0.4649822 0.4604215 0.4760223 0.4155832 0.5095127 0.4532625 0.7614431 0.4274921 0.6156639 0.3958249 0.9532121 0.5154565 0.7694987 0.530527 0.4177236 0.9958209 0.7538252 0.4665097 0.9120964 0.7349178 0.6726849 0.4364367 0.40158 0.5781735 0.9545566 0.5014448 0.9027768 0.9027768 0.418816 0.955547 0.4577325 0.869146 0.9321168 0.4222043 0.5399436 0.6295177 0.5771188 0.6739915 0.450424 0.7737787 0.8764544 0.6460734 0.8569976 0.9827041 0.5024988 0.7282339 0.4621289 0.955547 0.5458203 0.7114833 0.7371722 0.9240149 0.4494486 0.5125245 0.548914 0.4034471 0.4715344 0.5901632 0.6526499 0.6552272 0.4595851 0.4560877 0.7395471 0.5370856 0.4458493 0.8362911 0.9396285 0.5134308 0.8594727 0.4122182 0.5083472 0.4717936 0.3723941 0.9898786 0.5048224 0.457312 0.6344928 0.5122742 0.7466666 0.7698632 0.8004883 0.505247 +2.5% 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 1.0000000 2.00000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.0000000 2.0000000 2.0000000 2.000000 2.0000000 2.0000000 2.0000000 1.0000000 1.0000000 2.000000 +97.5% 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 4.000000 3.0000000 4.0000000 3.0000000 3.0000000 4.0000000 3.0000000 4.0000000 3.0000000 4.00000 4.0000000 4.0000000 3.0000000 4.0000000 4.0000000 3.000000 4.000000 3.0000000 4.000000 4.0000000 3.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.000000 3.0000000 4.0000000 4.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 4.000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.525000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.0000000 3.0000000 4.0000000 3.0000000 3.000000 4.0000000 3.0000000 4.0000000 3.0000000 3.0000000 3.000000 + mu[1] mu[2] mu[3] mu[4] mu0 p[1] p[2] p[3] p[4] tau +mean 0.8209168 8.6597267 4.4918555 15.0084753 7.016495 0.06747702 0.37526273 0.41374130 0.14351895 12.373505 +sd 0.2889807 0.7413355 0.4636712 0.9118818 7.736189 0.01840643 0.06683624 0.06972413 0.04132904 11.453268 +2.5% 0.3159641 7.1459652 3.5498788 13.5019293 -8.957681 0.03483103 0.26917087 0.27076168 0.07899303 3.944092 +97.5% 1.3443089 9.6706930 5.2035513 17.0296198 23.036467 0.10139157 0.51800835 0.51760937 0.22929627 44.391267 ===== Finished MCMC test for basic mixture model without conjugacy. ===== diff --git a/packages/nimble/tests/testthat/test-dynamicIndexing.R b/packages/nimble/tests/testthat/test-dynamicIndexing.R index 172a17216..0a41415f2 100644 --- a/packages/nimble/tests/testthat/test-dynamicIndexing.R +++ b/packages/nimble/tests/testthat/test-dynamicIndexing.R @@ -460,9 +460,9 @@ test_that('basic mixture model without conjugacy', { n <- 1000; d <- 4 set.seed(2) mns <- c(8, 15, 0.5, 4) - mu_tol <- c(1.5, 1, .5, .8) + mu_tol <- c(2, 1.5, .5, 1) p <- c(.45, .14, .05, .36) - p_tol <- c(.12, .05, .03, .12) + p_tol <- c(.12, .05, .03, .15) k <- sample(1:d, n, replace = TRUE, prob = p) y <- rpois(n, mns[k]) code <- nimbleCode({ diff --git a/packages/nimble/tests/testthat/test-mcmc.R b/packages/nimble/tests/testthat/test-mcmc.R index 6827bf59d..5c84337de 100644 --- a/packages/nimble/tests/testthat/test-mcmc.R +++ b/packages/nimble/tests/testthat/test-mcmc.R @@ -2865,8 +2865,8 @@ test_that('Categorical sampler issues a warning for invalid model likelihood val expect_true(length(conf$getSamplers()) == 1) expect_true(conf$getSamplers()[[1]]$name == 'categorical') Rmcmc <- buildMCMC(conf) - expect_output(samples <- runMCMC(Rmcmc, 10), 'encountered an invalid model density, and sampling results are likely invalid') - expect_true(all(samples == 2)) + expect_output(samples <- runMCMC(Rmcmc, 10), 'encountered a log probability density value of infinity') + ## code <- nimbleCode({ x ~ dcat(prob = a[1:3]) @@ -2882,8 +2882,7 @@ test_that('Categorical sampler issues a warning for invalid model likelihood val expect_true(length(conf$getSamplers()) == 1) expect_true(conf$getSamplers()[[1]]$name == 'categorical') Rmcmc <- buildMCMC(conf) - expect_output(suppressWarnings(samples <- runMCMC(Rmcmc, 10)), 'encountered an invalid model density, and sampling results are likely invalid') - expect_true(all(samples == 2)) + expect_output(expect_error(suppressWarnings(samples <- runMCMC(Rmcmc, 10)), 'all log probability density values are negative infinity')) # Use of `expect_output` prevents warnings about NAs from going into gold file. }) test_that('prior_samples sampler operates correctly', { diff --git a/packages/nimble/tests/testthat/truncTestLog_Correct.Rout b/packages/nimble/tests/testthat/truncTestLog_Correct.Rout index 76354c07e..e68983819 100644 --- a/packages/nimble/tests/testthat/truncTestLog_Correct.Rout +++ b/packages/nimble/tests/testthat/truncTestLog_Correct.Rout @@ -1,14 +1,14 @@ ===== Starting MCMC test for kidney. ===== - alpha b[1] b[2] b[3] b[4] b[5] b[6] b[7] b[8] b[9] b[10] b[11] b[12] b[13] b[14] b[15] b[16] b[17] b[18] b[19] b[20] b[21] b[22] b[23] b[24] b[25] b[26] b[27] b[28] b[29] b[30] b[31] b[32] b[33] b[34] b[35] b[36] b[37] b[38] beta.age beta.disease[1] beta.disease[2] beta.disease[3] beta.disease[4] beta.sex r t[1, 1] t[2, 1] t[3, 1] t[4, 1] t[5, 1] t[6, 1] t[7, 1] t[8, 1] t[9, 1] t[10, 1] t[11, 1] t[12, 1] t[13, 1] t[14, 1] t[15, 1] t[16, 1] t[17, 1] t[18, 1] t[19, 1] t[20, 1] t[21, 1] t[22, 1] t[23, 1] t[24, 1] t[25, 1] t[26, 1] t[27, 1] t[28, 1] t[29, 1] t[30, 1] t[31, 1] t[32, 1] t[33, 1] t[34, 1] t[35, 1] t[36, 1] t[37, 1] t[38, 1] t[1, 2] t[2, 2] t[3, 2] t[4, 2] t[5, 2] t[6, 2] t[7, 2] t[8, 2] t[9, 2] t[10, 2] -mean -3.9226393 0.05547905 0.01767812 -0.001921859 -0.0693537 0.001550257 0.01116105 0.07085133 -0.05558437 -0.03177299 -0.08815777 -0.05410009 -0.03441628 0.02166594 -0.1016576 -0.05292516 0.05794775 -0.03219619 0.01772038 -0.03122362 -0.008372468 -0.1630361 -0.01237864 0.05849138 -0.007041602 -0.05605571 -0.09726622 0.009043637 0.05651236 0.09692378 0.02790271 0.04141561 0.04383984 0.05076644 -0.00304005 0.0310409 0.01376763 0.07147147 0.03374306 -0.001535526 0 0.1516226 0.58880005 -1.0350831 -1.4326192 1.04085179 8 23 22 447 30 24 7 511 53 15 7 141 96 243.50061 536 17 185 292 155.26187 15 152 402 13 39 12 234.5793 132 34 2 130 27 85.580205 152 190 119 228.9529 297.71331 63 16 154.6748 28 318 12 245 9 30 196 154 -sd 0.4862867 0.22504320 0.16932300 0.207495826 0.1868792 0.169144319 0.22180163 0.22959890 0.18665070 0.19540119 0.23261904 0.16436528 0.22950773 0.15262536 0.2013406 0.16525815 0.17611211 0.18924305 0.18038289 0.18544915 0.157329548 0.2812180 0.19222432 0.20184822 0.165383628 0.20896941 0.19467508 0.166000453 0.17543362 0.21204530 0.18131655 0.19989667 0.18263765 0.18469844 0.19637142 0.1990072 0.19584684 0.21751406 0.19792653 0.006190054 0 0.3630461 0.28295668 0.4731745 0.3573388 0.06798536 0 0 0 0 0 0 0 0 0 0 0 0 0 90.56496 0 0 0 0 108.02038 0 0 0 0 0 0 106.6388 0 0 0 0 0 66.884651 0 0 0 192.6215 195.91808 0 0 115.4044 0 0 0 0 0 0 0 0 -2.5% -4.8256640 -0.33374036 -0.34335392 -0.574419499 -0.4598602 -0.360604323 -0.42370372 -0.32167427 -0.49893630 -0.45071815 -0.71331032 -0.40786611 -0.72655728 -0.28218908 -0.5880680 -0.39180304 -0.24156887 -0.53668108 -0.26226807 -0.45530724 -0.339899432 -1.0865571 -0.37846823 -0.27058127 -0.363697860 -0.62386345 -0.66137024 -0.310244084 -0.28436558 -0.29246573 -0.36481997 -0.38274867 -0.29592354 -0.31411850 -0.42742287 -0.4860661 -0.49994231 -0.28881092 -0.46690058 -0.011590881 0 -0.4994164 0.02663209 -1.9535166 -1.9904524 0.92582483 8 23 22 447 30 24 7 511 53 15 7 141 96 153.00907 536 17 185 292 27.99501 15 152 402 13 39 12 117.2629 132 34 2 130 27 6.877066 152 190 119 59.0714 42.51935 63 16 19.9082 28 318 12 245 9 30 196 154 -97.5% -2.9868207 0.68660325 0.35605085 0.357310342 0.2798740 0.279710718 0.47983832 0.70798188 0.26162097 0.33895171 0.26251570 0.27058528 0.32344355 0.36534463 0.1888238 0.24848679 0.44553761 0.31165385 0.43417203 0.36320525 0.267916914 0.1460256 0.36486312 0.58765853 0.326992439 0.30477013 0.15418527 0.439937283 0.47643996 0.59674536 0.39674776 0.46641321 0.42921464 0.47502316 0.43669455 0.4172089 0.48211354 0.66793505 0.47528269 0.009771780 0 0.7788608 1.12052313 -0.0842178 -0.6363802 1.19117071 8 23 22 447 30 24 7 511 53 15 7 141 96 484.65129 536 17 185 292 405.34200 15 152 402 13 39 12 482.1125 132 34 2 130 27 254.827612 152 190 119 812.7766 803.92830 63 16 464.4037 28 318 12 245 9 30 196 154 - t[11, 2] t[12, 2] t[13, 2] t[14, 2] t[15, 2] t[16, 2] t[17, 2] t[18, 2] t[19, 2] t[20, 2] t[21, 2] t[22, 2] t[23, 2] t[24, 2] t[25, 2] t[26, 2] t[27, 2] t[28, 2] t[29, 2] t[30, 2] t[31, 2] t[32, 2] t[33, 2] t[34, 2] t[35, 2] t[36, 2] t[37, 2] t[38, 2] tau -mean 333 168.26163 38 157.26392 114.76625 27.958397 177 114 287.2828 225.8098 562 145.20884 66 150.73135 40 201 156 30 25 26 58 43 30 87.726544 8 166.64033 78 102.69815 71.082969 -sd 0 138.99994 0 81.80295 83.20201 22.754922 0 0 141.6508 108.4067 0 84.35446 0 86.09441 0 0 0 0 0 0 0 0 0 68.151488 0 103.13209 0 83.90129 76.432956 -2.5% 333 12.82343 38 73.58530 26.71139 4.649097 177 114 162.3288 110.2412 562 29.70974 66 47.07429 40 201 156 30 25 26 58 43 30 8.550706 8 27.28927 78 11.32323 4.995558 -97.5% 333 533.85669 38 398.03978 320.10714 78.197394 177 114 733.6627 520.5622 562 310.10387 66 336.53491 40 201 156 30 25 26 58 43 30 262.586023 8 389.34810 78 330.78556 295.308861 + alpha b[1] b[2] b[3] b[4] b[5] b[6] b[7] b[8] b[9] b[10] b[11] b[12] b[13] b[14] b[15] b[16] b[17] b[18] b[19] b[20] b[21] b[22] b[23] b[24] b[25] b[26] b[27] b[28] b[29] b[30] b[31] b[32] b[33] b[34] b[35] b[36] b[37] b[38] beta.age beta.disease[1] beta.disease[2] beta.disease[3] beta.disease[4] beta.sex r t[1, 1] t[2, 1] t[3, 1] t[4, 1] t[5, 1] t[6, 1] t[7, 1] t[8, 1] t[9, 1] t[10, 1] t[11, 1] t[12, 1] t[13, 1] t[14, 1] t[15, 1] t[16, 1] t[17, 1] t[18, 1] t[19, 1] t[20, 1] t[21, 1] t[22, 1] t[23, 1] t[24, 1] t[25, 1] t[26, 1] t[27, 1] t[28, 1] t[29, 1] t[30, 1] t[31, 1] t[32, 1] t[33, 1] t[34, 1] t[35, 1] t[36, 1] t[37, 1] t[38, 1] t[1, 2] t[2, 2] t[3, 2] t[4, 2] t[5, 2] t[6, 2] t[7, 2] t[8, 2] t[9, 2] t[10, 2] t[11, 2] t[12, 2] t[13, 2] +mean -4.3572986 0.1273258 0.4056518 0.08605571 -0.2331241 0.2003569 0.05840531 0.4784641 -0.2714397 -0.06509931 -0.3564752 -0.1233159 0.02524117 0.2578457 -0.4366354 -0.1857666 0.001478039 -0.1583969 0.006738363 -0.2300052 0.02086172 -0.5179173 -0.3653617 0.2589427 0.1077793 -0.1414732 -0.5088389 0.02096921 0.2993306 0.1450799 0.2232591 0.3548336 0.1497962 0.2742183 0.03645658 0.3819942 -0.1475119 0.1969909 -0.04147252 0.0101732226 0 -0.07957162 0.3752814 -1.2297023 -1.6249971 1.07655390 8 23 22 447 30 24 7 511 53 15 7 141 96 287.1069 536 17 185 292 263.37533 15 152 402 13 39 12 595.2287 132 34 2 130 27 132.65560 152 190 119 176.38236 301.83481 63 16 99.80336 28 318 12 245 9 30 196 154 333 181.70903 38 +sd 0.6285428 0.4795039 0.5866106 0.36314297 0.4636534 0.5109698 0.35050237 0.5556657 0.4255329 0.32511967 0.4010273 0.4195240 0.32461752 0.6212637 0.4296960 0.3928929 0.401353007 0.4762044 0.365411250 0.4532410 0.44869599 0.6544804 0.4102038 0.5012898 0.4424145 0.4034260 0.6284313 0.46307371 0.4878445 0.3350508 0.3766801 0.4677066 0.4660109 0.6034381 0.38252487 0.4308516 0.4638074 0.4286854 0.39200282 0.0065489883 0 0.39340682 0.3995054 0.6044772 0.3650600 0.08260233 0 0 0 0 0 0 0 0 0 0 0 0 0 134.4918 0 0 0 0 238.22188 0 0 0 0 0 0 696.1469 0 0 0 0 0 116.54571 0 0 0 129.95116 284.95855 0 0 83.05415 0 0 0 0 0 0 0 0 0 155.37754 0 +2.5% -5.4365955 -0.7491897 -0.4960168 -0.68986858 -1.4998538 -0.7369047 -0.66574663 -0.3367865 -1.0528326 -0.73472136 -1.2593480 -1.2185100 -0.54631337 -0.6700447 -1.2879249 -1.1255188 -0.843471870 -1.3570007 -0.773545553 -1.1722287 -0.83873459 -1.9456912 -1.3524965 -0.5970198 -0.9856667 -0.9946059 -1.7932244 -1.03056239 -0.4941178 -0.4218358 -0.4740711 -0.4703750 -0.7339319 -0.6011050 -0.83246822 -0.2546816 -1.3180861 -0.5806595 -1.12306259 -0.0006920071 0 -0.81751239 -0.4835968 -2.5606791 -2.4118652 0.89296376 8 23 22 447 30 24 7 511 53 15 7 141 96 152.0971 536 17 185 292 30.81708 15 152 402 13 39 12 115.5104 132 34 2 130 27 10.44091 152 190 119 55.88275 16.04015 63 16 15.85318 28 318 12 245 9 30 196 154 333 17.80534 38 +97.5% -3.2645560 1.1691802 1.7939266 0.88214259 0.5469411 1.6773448 0.73601388 1.7132980 0.6122695 0.66475840 0.1745154 0.6775325 0.77191603 1.9031991 0.1996253 0.4575834 0.839882878 0.5605879 0.651181324 0.6473906 1.04080909 0.3780920 0.1984943 1.3606793 0.9959739 0.6771604 0.2011160 0.83878600 1.2633537 0.9873560 1.0600620 1.4896181 1.2624929 1.9789074 1.05990921 1.3591682 0.6589799 1.1359890 0.59765345 0.0257316036 0 0.72987823 1.3211114 -0.1670724 -0.9216093 1.23573076 8 23 22 447 30 24 7 511 53 15 7 141 96 638.1981 536 17 185 292 967.81370 15 152 402 13 39 12 2250.3496 132 34 2 130 27 418.30241 152 190 119 549.43113 973.54730 63 16 337.83608 28 318 12 245 9 30 196 154 333 566.99172 38 + t[14, 2] t[15, 2] t[16, 2] t[17, 2] t[18, 2] t[19, 2] t[20, 2] t[21, 2] t[22, 2] t[23, 2] t[24, 2] t[25, 2] t[26, 2] t[27, 2] t[28, 2] t[29, 2] t[30, 2] t[31, 2] t[32, 2] t[33, 2] t[34, 2] t[35, 2] t[36, 2] t[37, 2] t[38, 2] tau +mean 261.98792 265.53467 25.11264 177 114 391.5189 212.1214 562 368.03781 66 174.22178 40 201 156 30 25 26 58 43 30 189.25695 8 302.65332 78 117.82250 41.4938137 +sd 179.61583 191.58241 28.62745 0 0 252.6668 130.0171 0 282.49642 0 129.32313 0 0 0 0 0 0 0 0 0 125.55027 0 222.40551 0 106.76266 77.6177121 +2.5% 75.13172 39.58107 4.18648 177 114 165.3512 108.9636 562 38.90611 66 54.10319 40 201 156 30 25 26 58 43 30 11.14644 8 25.86317 78 10.22662 0.9973042 +97.5% 731.11246 649.10806 112.83307 177 114 1079.6445 676.6717 562 957.85067 66 608.09693 40 201 156 30 25 26 58 43 30 422.48722 8 738.67220 78 411.59546 292.9646795 ===== Finished MCMC test for kidney. ===== ===== Starting MCMC test for litters. ===== mu[1] mu[2] p[1, 1] p[2, 1] p[1, 2] p[2, 2] p[1, 3] p[2, 3] p[1, 4] p[2, 4] p[1, 5] p[2, 5] p[1, 6] p[2, 6] p[1, 7] p[2, 7] p[1, 8] p[2, 8] p[1, 9] p[2, 9] p[1, 10] p[2, 10] p[1, 11] p[2, 11] p[1, 12] p[2, 12] p[1, 13] p[2, 13] p[1, 14] p[2, 14] p[1, 15] p[2, 15] p[1, 16] p[2, 16] theta[1] theta[2] @@ -60,258 +60,258 @@ sd 0.2920298 0.2832608 97.5% 2.0854313 -0.2160456 ===== Finished MCMC test for test of dconstraint. ===== ===== Starting MCMC test for test of ordering constraint. ===== -Coverage for test of ordering constraint is 93.51536 %. +Coverage for test of ordering constraint is 95.22184 %. True values with 95% posterior interval: trueVals 2.5% 97.5% covered -beta 1.063546856 0.2798393 1.4280920 1 -pi -0.238687545 -0.6355222 0.1597958 1 -kappa 0.233436240 0.2021559 1.1358455 1 -a0[1] 0.709900664 0.4511084 0.9465161 1 -a0[2] 3.906621908 3.4540714 4.7521009 1 -a0[3] 5.240003140 4.6208573 6.6279439 1 -tau 0.781189294 0.4466356 3.3069373 1 -b[1] 1.428925180 -0.9965955 2.6180321 1 -b[2] -0.369105255 -1.7373929 1.6712948 1 -b[3] 1.504554578 -1.0676065 2.6922600 1 -b[4] 1.439645377 -1.7307843 1.5573733 1 -b[5] 0.469131459 -0.9764672 2.6472995 1 -b[6] -1.742322282 -3.1626682 0.6045802 1 -b[7] -1.050594494 -1.8875177 1.6499891 1 -b[8] -0.333451078 -0.9975854 2.6083351 1 -b[9] -0.006525065 -1.7223586 1.6116818 1 -b[10] 2.720660453 -1.0367526 2.5987038 0 -b[11] 0.863940949 -1.7284155 1.5602816 1 -b[12] -0.904010896 -2.5420746 0.9176541 1 -b[13] -1.298476135 -2.5724034 0.9747184 1 -b[14] -0.327501111 -1.0092714 2.5614219 1 -b[15] -0.338536415 -1.7722529 1.6802803 1 -b[16] -0.465589450 -1.7401132 1.7411176 1 -b[17] 0.285369344 -2.4681408 1.2012325 1 -b[18] -1.009132771 -3.2249139 0.6696850 1 -b[19] 0.492938537 -1.7652484 1.6002530 1 -b[20] -1.400169296 -2.4734586 1.2387887 1 -b[21] -0.253740006 -1.7188059 1.5716519 1 -b[22] 0.426991022 -1.6741708 1.6256519 1 -b[23] 0.150858733 -1.0174232 2.5691116 1 -b[24] 0.909871920 -1.0851448 2.7191691 1 -b[25] -0.064611450 -1.7606689 1.6832329 1 -b[26] 0.569789517 -1.8444203 1.5673347 1 -b[27] 1.228455535 -1.0186723 2.5200966 1 -b[28] -0.781755406 -1.8080577 1.8063745 1 -b[29] -1.453414732 -2.4905371 0.8586795 1 -b[30] 0.052866683 -2.5547738 0.8011700 1 -b[31] -0.266681888 -1.0903428 2.5750810 1 -b[32] -0.614231811 -2.5304468 0.8497874 1 -b[33] -0.490253710 -1.0346233 2.6572628 1 -b[34] -0.734821839 -1.7304406 1.5735519 1 -b[35] 0.822256558 -1.7224205 1.7284594 1 -b[36] 1.303290016 -1.8586175 1.6360190 1 -b[37] 1.122544930 -1.7450990 1.7236420 1 -b[38] -0.485957492 -1.0506761 2.8784562 1 -b[39] 1.401035597 -1.0982354 2.6231420 1 -b[40] -0.316056520 -2.5324143 0.7810915 1 -b[41] 1.988917587 -0.9944710 2.6990741 1 -b[42] 0.634436431 -1.0695064 2.5471165 1 -b[43] -0.512286492 -0.8791722 2.8280871 1 -b[44] -0.941386106 -2.6567842 0.8486119 1 -b[45] -1.319875192 -2.5705138 0.8645881 1 -b[46] -1.205624962 -1.9664719 1.6312487 1 -b[47] -1.769286170 -2.4923361 0.7611930 1 -b[48] 1.308523085 -1.6610307 1.5175444 1 -b[49] 0.941390443 -0.9813848 2.8613448 1 -b[50] -0.257203048 -1.7575265 1.6971211 1 -b[51] 0.301111752 -1.8666500 1.7508831 1 -b[52] -0.426207034 -1.7388399 1.6091987 1 -b[53] 2.762196094 -1.0797799 2.7045001 0 -b[54] -0.899858455 -1.0983099 2.6760087 1 -b[55] -0.062089186 -1.8967178 1.5784276 1 -b[56] 0.283013597 -1.1191160 2.7367108 1 -b[57] 0.699489618 -1.0023152 2.5526573 1 -b[58] -0.195308787 -1.0118385 2.6815712 1 -b[59] -2.516153702 -1.8277746 1.5966180 0 -b[60] -1.429672027 -2.6114922 1.0552914 1 -b[61] 0.405871185 -1.1822769 2.4516967 1 -b[62] -0.012497018 -1.9777890 1.3631197 1 -b[63] -1.064264392 -2.8856324 0.7665791 1 -b[64] -0.131046485 -2.1761112 1.5808706 1 -b[65] -0.922067666 -2.8424804 0.6856668 1 -b[66] 0.274100490 -1.9973680 1.4062319 1 -b[67] -1.612377427 -2.8737962 0.6861677 1 -b[68] 0.414031206 -1.2061057 2.5458191 1 -b[69] 0.281057750 -2.1959257 1.4340299 1 -b[70] 0.073868016 -2.0229890 1.5429305 1 -b[71] 0.021673825 -1.2382411 2.4273530 1 -b[72] 0.291156451 -2.0291360 1.5755146 1 -b[73] -0.734299613 -2.1012440 1.4722079 1 -b[74] -0.134829302 -1.2730987 2.3557994 1 -b[75] 0.751412966 -1.9231220 1.4746628 1 -b[76] 1.245652746 -1.2184279 2.4850367 1 -b[77] 0.162665182 -1.9915487 1.4227611 1 -b[78] -0.133228165 -1.8390844 1.4411613 1 -b[79] -1.031927657 -2.0200355 1.4392258 1 -b[80] -1.626506361 -2.0630514 1.4318266 1 -b[81] -0.901838892 -2.8500157 0.7063592 1 -b[82] 1.418888198 -1.2126236 2.6111257 1 -b[83] 0.873613103 -1.3189064 2.4802998 1 -b[84] -0.248363231 -2.0466110 1.3846515 1 -b[85] -0.480636645 -1.1947716 2.4900431 1 -b[86] -0.474040289 -2.0105968 1.4262808 1 -b[87] 1.128005698 -1.1797423 2.5052186 1 -b[88] -0.312019346 -1.2549933 2.4374423 1 -b[89] 1.421078289 -1.9325989 1.4210204 0 -b[90] 0.731656982 -1.1759547 2.5396563 1 -b[91] 1.470061180 -2.1271379 1.5024906 1 -b[92] -0.988021685 -1.1652667 2.4628987 1 -b[93] 0.009471028 -1.2087897 2.5447143 1 -b[94] -0.996631312 -2.0910512 1.4198513 1 -b[95] 0.674616281 -1.1772172 2.5754211 1 -b[96] 0.135450312 -2.2178788 1.3164100 1 -b[97] -0.319255703 -1.3156667 2.6122888 1 -b[98] 1.647326838 -1.2535080 2.3794995 1 -b[99] 0.259116156 -2.8791585 0.6490692 1 -b[100] 1.127504558 -1.2340290 2.5406081 1 -b[101] 0.884607060 -2.1168150 1.4861585 1 -b[102] -0.878856573 -2.7917046 1.0334198 1 -b[103] -0.696940103 -2.0455572 1.4575561 1 -b[104] 0.052701644 -1.9804851 1.4689584 1 -b[105] -1.278935208 -3.8678515 0.2597985 1 -b[106] 0.652508170 -2.0802662 1.5526618 1 -b[107] -1.449058873 -2.7661557 0.7902098 1 -b[108] 1.839055149 -1.2493868 2.5488333 1 -b[109] -0.566495543 -1.1425239 2.5443370 1 -b[110] 1.898850314 -1.3478415 2.4931052 1 -b[111] -0.466731109 -1.9981000 1.5866316 1 -b[112] -1.100059724 -1.9523459 1.4020308 1 -b[113] 0.028718552 -1.9613186 1.5444234 1 -b[114] 0.031086003 -1.2915261 2.4489837 1 -b[115] -1.900983613 -2.8831615 0.7033491 1 -b[116] 1.192229331 -2.2021374 1.4433002 1 -b[117] -1.266731006 -2.0531772 1.5881890 1 -b[118] 0.379722281 -2.0885006 1.4431242 1 -b[119] 0.559819258 -1.9139432 1.5537311 1 -b[120] 0.156194879 -1.2970937 2.7249925 1 -b[121] -0.134403057 -1.2516566 2.4648329 1 -b[122] 0.223662902 -1.9828097 1.4997548 1 -b[123] -1.209134759 -3.2341662 0.5452668 1 -b[124] -0.908767328 -1.7923878 1.5918714 1 -b[125] -1.260130368 -2.5188506 0.6955953 1 -b[126] 1.787739129 -1.0839762 2.6106561 1 -b[127] 1.694654326 -1.7975294 1.5708323 0 -b[128] 0.297160961 -1.0978863 2.5943387 1 -b[129] -1.394922674 -1.8918144 1.6419638 1 -b[130] -0.004212861 -1.9043574 1.6504428 1 -b[131] 1.710328406 -1.0724704 2.6292986 1 -b[132] -0.538212083 -2.5893395 0.8028708 1 -b[133] 0.902774473 -1.0361109 2.7585797 1 -b[134] -1.102000921 -3.3342803 0.4995117 1 -b[135] 0.779966479 -1.7876327 1.5627237 1 -b[136] -1.081450516 -1.0624873 2.5524409 0 -b[137] -1.393571605 -2.5212355 0.8789523 1 -b[138] -1.082641645 -1.0978420 2.6265524 1 -b[139] -0.984085223 -2.5665283 0.8097669 1 -b[140] -1.030357610 -2.3951127 1.2471002 1 -b[141] 0.838690989 -0.9959536 2.5794390 1 -b[142] 0.077514963 -1.6821751 1.6676070 1 -b[143] -0.366296398 -1.0803987 2.6554314 1 -b[144] -1.229285637 -2.5882187 0.8883812 1 -b[145] -1.149437055 -1.0461929 2.6658484 0 -b[146] -0.868689185 -1.8988037 1.7020892 1 -b[147] -1.266867858 -1.1694251 2.8192275 0 -b[148] -0.507070968 -1.7315290 1.4880743 1 -b[149] 0.533729520 -1.6647972 1.5888004 1 -b[150] -1.335624640 -1.7027639 1.6295547 1 -b[151] 1.663470539 -1.8960383 1.6490465 0 -b[152] -1.483760677 -1.8717767 1.6267617 1 -b[153] -0.109209727 -1.0905324 2.6444031 1 -b[154] 2.681136195 -1.0629654 2.6346740 0 -b[155] 1.007667984 -1.9875145 1.6482291 1 -b[156] -0.285323763 -3.2715365 0.4911025 1 -b[157] -0.979537933 -2.5390188 0.8799133 1 -b[158] 0.659146427 -2.0021408 1.4509138 1 -b[159] -0.014175889 -2.0287598 1.3931023 1 -b[160] -0.424116229 -1.1018624 2.5606361 1 -b[161] 0.359660628 -1.1657921 2.5612674 1 -b[162] -0.553041934 -1.2433421 2.6961258 1 -b[163] 3.008045062 -1.2647961 2.4914747 0 -b[164] 1.901091643 -1.2176845 2.4486512 1 -b[165] 0.882032892 -1.3112124 2.3420230 1 -b[166] 0.806970886 -2.0505079 1.3574695 1 -b[167] -0.614224663 -1.2409450 2.5123785 1 -b[168] 1.002182769 -1.2801121 2.6038662 1 -b[169] -0.394405188 -1.1392041 2.5621907 1 -b[170] -1.140527877 -1.1352137 2.4952747 0 -b[171] 2.130660615 -0.9866984 2.6538471 1 -b[172] -1.051051636 -0.9372763 2.5327884 0 -b[173] -0.332858224 -0.9993948 2.5983856 1 -b[174] -0.695763843 -2.6420270 0.8443161 1 -b[175] -1.071535575 -2.5362303 1.0107467 1 -b[176] 0.677689354 -3.2071328 0.4470334 0 -b[177] -1.723840440 -2.4468539 1.3056823 1 -b[178] -0.233285290 -2.5308100 0.8825931 1 -b[179] -0.649766336 -1.7904203 1.6574818 1 -b[180] -1.572854440 -2.5473303 0.8813950 1 -b[181] -0.079671269 -1.7473972 1.6333209 1 -b[182] -0.487503480 -1.8295659 1.8929907 1 -b[183] -0.670052557 -3.1533356 0.4496255 1 -b[184] 1.110049352 -1.7594391 1.5712702 1 -b[185] 0.602375830 -1.1445189 2.7200322 1 -b[186] -0.102343398 -1.8396629 1.6033264 1 -b[187] 0.177055660 -1.1842245 2.5508625 1 -b[188] -0.834205365 -1.8725916 1.4635615 1 -b[189] -0.227800422 -2.8734186 0.6101611 1 -b[190] 1.247018921 -1.9855828 1.4744083 1 -b[191] -0.018949225 -1.3732176 2.5954438 1 -b[192] 0.183050056 -1.9992650 1.4475492 1 -b[193] 2.290845020 -1.3040067 2.5824257 1 -b[194] -0.796170099 -1.2863526 2.4423913 1 -b[195] 1.087054731 -1.3431705 2.7204130 1 -b[196] 2.025781304 -1.1426258 2.4380992 1 -b[197] -1.204012224 -2.0436393 1.5385551 1 -b[198] 0.019954250 -1.2496780 2.5521942 1 -b[199] -0.441148397 -3.3995272 0.6485512 1 -b[200] -0.555335444 -3.4582132 0.5341913 1 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0.7422947 1 +b[229] 0.452749450 -1.0810367 2.8268890 1 +b[230] 2.482635050 -0.9893523 2.7089515 1 +b[231] -1.686631278 -2.6946958 0.9130368 1 +b[232] -0.068720947 -2.6300611 0.7790647 1 +b[233] -0.164009252 -0.8990419 2.8231807 1 +b[234] 1.716807015 -1.7603326 1.6153962 0 +b[235] -1.093451141 -0.9611157 2.6160765 0 +b[236] 0.553018113 -0.9856655 2.6607774 1 +b[237] 1.889819099 -0.9508358 2.7384449 1 +b[238] -0.290371002 -1.9114704 1.7496504 1 +b[239] -0.786162816 -1.7151204 1.7163700 1 +b[240] -0.179497687 -1.9350040 1.6936066 1 +b[241] 0.558300921 -0.9444116 2.7604134 1 +b[242] -1.128736274 -2.6476995 0.8977150 1 +b[243] 1.798281285 -1.1143304 2.7469463 1 [ reached getOption("max.print") -- omitted 43 rows ] ===== Finished MCMC test for test of ordering constraint. =====