Nothing
R/apollo_modelOutput.R
In apollo: Tools for Choice Model Estimation and Application
Defines functions
apollo_modelOutput
Documented in
apollo_modelOutput
#' Prints estimation results to console
#'
#' Prints estimation results to console. Amount of information presented can be adjusted through arguments.
#'
#' Prints to screen the output of a model previously estimated by apollo_estimate()
#' @param model Model object. Estimated model object as returned by function \link{apollo_estimate}.
#' @param modelOutput_settings List. Contains settings for this function. User input is required for all settings except those with a default or marked as optional.
#' \itemize{
#' \item \strong{\code{printChange}}: Logical. TRUE for printing difference between starting values and estimates. FALSE by default.
#' \item \strong{\code{printClassical}}: Logical. TRUE for printing classical standard errors. TRUE by default.
#' \item \strong{\code{printCorr}}: Boolean. TRUE for printing parameters correlation matrix. If \code{printClassical=TRUE}, both classical and robust matrices are printed. For Bayesian estimation, this setting is used for the covariane of random parameters. FALSE by default.
#' \item \strong{\code{printCovar}}: Boolean. TRUE for printing parameters covariance matrix. If \code{printClassical=TRUE}, both classical and robust matrices are printed. For Bayesian estimation, this setting is used for the correlation of random parameters. FALSE by default.
#' \item \strong{\code{printDataReport}}: Logical. TRUE for printing summary of choices in database and other diagnostics. FALSE by default.
#' \item \strong{\code{printFixed}}: Logical. TRUE for printing fixed parameters among estimated parameter. TRUE by default.
#' \item \strong{\code{printFunctions}}: Logical. TRUE for printing apollo_control, apollo_randCoeff (when available), apollo_lcPars (when available) and apollo_probabilities. FALSE by default.
#' \item \strong{\code{printHBconvergence}}: Boolean. TRUE for printing Geweke convergence tests. FALSE by default.
#' \item \strong{\code{printHBiterations}}: Boolean. TRUE for printing an iterations report for HB estimation. TRUE by default.
#' \item \strong{\code{printModelStructure}}: Logical. TRUE for printing model structure. TRUE by default.
#' \item \strong{\code{printOutliers}}: Logical or Scalar. TRUE for printing 20 individuals with worst average fit across observations. FALSE by default. If Scalar is given, this replaces the default of 20.
#' \item \strong{\code{printPVal}}: Logical or Scalar. TRUE or 1 for printing p-values for one-sided test, 2 for printing p-values for two-sided test, FALSE for not printing p-values. FALSE by default.
#' \item \strong{\code{printT1}}: Logical. If TRUE, t-test for H0: apollo_beta=1 are printed. FALSE by default.
#' }
#' @return A matrix of coefficients, s.d. and t-tests (invisible)
#' @export
#' @importFrom coda geweke.diag
#' @importFrom utils capture.output
apollo_modelOutput=function(model, modelOutput_settings=NA){
if(length(modelOutput_settings)==1 && is.na(modelOutput_settings)) modelOutput_settings=list()
if(is.null(modelOutput_settings[["printModelStructure"]])) modelOutput_settings[["printModelStructure"]] = TRUE
if(is.null(modelOutput_settings[["printDataReport"]])) modelOutput_settings[["printDataReport"]] = FALSE
if(!is.null(modelOutput_settings[["printDiagnostics"]])){
modelOutput_settings[["printModelStructure"]] = modelOutput_settings[["printDiagnostics"]]
modelOutput_settings[["printDataReport"]] = modelOutput_settings[["printDiagnostics"]]
}
if(is.null(modelOutput_settings[["printClassical"]])) modelOutput_settings[["printClassical"]] = TRUE
if(is.null(modelOutput_settings[["printPVal"]])) modelOutput_settings[["printPVal"]] = FALSE
if(is.null(modelOutput_settings[["printT1"]])) modelOutput_settings[["printT1"]] = FALSE
if(is.null(modelOutput_settings[["printCovar"]])) modelOutput_settings[["printCovar"]] = FALSE
if(is.null(modelOutput_settings[["printCorr"]])) modelOutput_settings[["printCorr"]] = FALSE
if(is.null(modelOutput_settings[["printHBconvergence"]])) modelOutput_settings[["printHBconvergence"]] = FALSE
if(is.null(modelOutput_settings[["printHBiterations"]])) modelOutput_settings[["printHBiterations"]] = FALSE
if(is.null(modelOutput_settings[["printOutliers"]])) modelOutput_settings[["printOutliers"]] = FALSE
if(is.null(modelOutput_settings[["printChange"]])) modelOutput_settings[["printChange"]] = FALSE
if(is.null(modelOutput_settings[["printFunctions"]])) modelOutput_settings[["printFunctions"]] = FALSE
if(is.null(modelOutput_settings[["printFixed"]])) modelOutput_settings[["printFixed"]] = TRUE
printClassical = modelOutput_settings[["printClassical"]]
printPVal = modelOutput_settings[["printPVal"]]
printT1 = modelOutput_settings[["printT1"]]
printDiagnostics = modelOutput_settings[["printDiagnostics"]]
printCovar = modelOutput_settings[["printCovar"]]
printCorr = modelOutput_settings[["printCorr"]]
printHBconvergence = modelOutput_settings[["printHBconvergence"]]
printHBiterations = modelOutput_settings[["printHBiterations"]]
printOutliers = modelOutput_settings[["printOutliers"]]
printChange = modelOutput_settings[["printChange"]]
printFunctions = modelOutput_settings[["printFunctions"]]
printFixed = modelOutput_settings[["printFixed"]]
test <- !is.null(model$manualScaling) && length(model$manualScaling)==1 && is.logical(model$manualScaling)
if(test) scaling_used <- model$manualScaling else {
test <- !is.null(model$scaling) && is.vector(model$scaling) && is.numeric(model$scaling) && !all(model$scaling==1)
if(test) scaling_used <- TRUE else scaling_used <- FALSE
}
# ####################### #
#### MODEL DESCRIPTION ####
# ####################### #
apollo_control <- model$apollo_control
#nParams <- length(model$apollo_beta)
#nFreeParams <- nParams
#if(!is.null(model$apollo_fixed)) nFreeParams <- nFreeParams - length(model$apollo_fixed)
# Printing model information
if("package:apollo" %in% search()){
apolloVersion <- tryCatch(utils::packageDescription("apollo", fields = "Version"),
warning=function(w) return("alpha"),
error=function(e) return("alpha"))
} else apolloVersion <- "alpha"
#cat('Model run using Apollo for R, version', apolloVersion, 'on', Sys.info()['sysname'], 'by', Sys.info()['user'], '\n')
cat("Model run by ", Sys.info()['user'], " using Apollo ", apolloVersion,
" on R ", paste0(version$major, ".", version$minor),
" for ", Sys.info()['sysname'], ".\n", sep="")
cat("Please acknowledge the use of Apollo by citing Hess & Palma (2019)\n")
cat(" DOI 10.1016/j.jocm.2019.100170\n")
cat(" www.ApolloChoiceModelling.com\n\n")
cat("Model name : ", model$apollo_control$modelName,"\n", sep="")
cat("Model description : ", model$apollo_control$modelDescr,"\n", sep="")
cat("Model run at : ", paste(model$startTime),"\n", sep="")
cat("Estimation method : ", model$estimationRoutine, "\n", sep="")
if(!apollo_control$HB){
cat("Model diagnosis : ",model$message,"\n", sep="")
if(!is.null(model$hessianEigenValue)){
isC <- is.complex(model$hessianEigenValue)
anyZ <- !isC && any(model$hessianEigenValue==0)
anyN <- !isC && any(model$hessianEigenValue<0)
anyP <- !isC && any(model$hessianEigenValue>0)
if(isC) txt <- c("Non-symmetrical hessian", "Complex eigenvalues")
if(!isC && all(model$hessianEigenValue<0)) txt <- c("Maximum found", "Negative definite")
if(!isC && anyZ) txt <- c("Inconclusive test", "Some eigenvalues are zero")
if(!isC && !anyZ && anyN && anyP) txt <- c("Saddle point found", "Some eigenvalues are positive and others negative")
if(!isC && all(model$hessianEigenValue>0)) txt <- c("Minimum found", "Positive definite")
if(isC) txt[3] <- "NA" else txt[3] <- as.character(round(max(model$hessianEigenValue),6))
if(!isC && all(model$hessianEigenValue<0)){
txt[4] <- as.character(signif(max(model$hessianEigenValue)/min(model$hessianEigenValue),6))
}else{
txt[4] <- "not calculated (Hessian is not negative definite)"
}
cat("Optimisation diagnosis : ", txt[1], "\n",
" hessian properties : ", txt[2], "\n",
" maximum eigenvalue : ", txt[3], "\n",
" reciprocal of condition number : ", txt[4], "\n",
sep="")
rm(isC, anyZ, anyN, anyP, txt)
} else if(!is.null(model$eigValue) && !anyNA(model$eigValue)){
cat("Min abs eigenvalue of Hessian : ",round(min(abs(model$eigValue)),6),"\n")
if(any(model$eigValue>0)) apollo_print("Some eigenvalues of Hessian are positive, indicating potential problems!")
}
}
cat("Number of individuals : ", model$nIndivs,"\n", sep="")
cat("Number of rows in database : ", model$nObs,"\n", sep="")
if(!is.null(model$nObsTot)){
cat('Number of modelled outcomes : ', sum(model$nObsTot), '\n', sep='')
if(length(model$nObsTot)>1) for(i in names(model$nObsTot)){
if(nchar(i)>26) txt <- substr(i, 1, 26) else txt <- i # max 26 characters for the component name
if(nchar(txt)<26) txt <- paste0(c(rep(' ', 26 - nchar(txt)), txt), collapse='')
cat(' ', txt, ' : ', model$nObsTot[i], '\n', sep='')
}; cat('\n')
}
cat("Number of cores used : ",model$apollo_control$nCores,"\n")
if(model$apollo_control$mixing){
d <- model$apollo_draws
if(d$interNDraws>0 && length(c(d$interUnifDraws, d$interNormDraws))>0){
cat("Number of inter-individual draws : ", d$interNDraws, ' (', d$interDrawsType, ')', "\n", sep='')
}
if(d$intraNDraws>0 && length(c(d$intraUnifDraws, d$intraNormDraws))>0){
cat("Number of intra-individual draws : ", d$intraNDraws, ' (', d$intraDrawsType, ')', "\n", sep='')
}
if(!model$apollo_control$panelData & model$apollo_control$mixing & d$interNDraws>0){
cat("WARNING: Inter-individual draws were used\n")
cat(" without a panel data structure.\n")
}
rm(d)
} else { if(!apollo_control$HB) cat("Model without mixing\n") }
cat("\n")
# ####################### #
#### HB OUTPUT ####
# ####################### #
if(apollo_control$HB){
apollo_HB <- model$apollo_HB
cat("Estimation carried out using RSGHB\n")
cat("Burn-in iterations : ",model$gNCREP,"\n", sep="")
cat("Post burn-in iterations : ",model$gNEREP,"\n", sep="")
#cat("LL(start) : ",model$LLStart,"\n", sep="")
#cat("LL(0) : ",model$LL0[1],"\n", sep="")
#cat("Average post. LL post burn-in : ",mean(colSums(log(model$cmcLLout))),"\n",sep="")
#cat("Average post. RLH post burn-in : ",round(mean(colMeans((model$cmcRLHout))),4),"\n",sep="")
cat("\n")
apollo_print("Classical model fit statistics were calculated at parameter values obtained using averaging across the post burn-in iterations.")
cat("LL(start) : ",round(model$LLStart,2),"\n", sep="")
if(length(model$LLout)==1) cat("LL at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL(final) : ",round(model$maximum,2),"\n",sep="")
if(length(model$LLout)>1 ) cat("LL(final, whole model) : ",round(model$maximum,2),"\n",sep="")
cat("Rho-squared vs equal shares : ", ifelse(!is.numeric(model$rho2_0 ), "Not applicable", round(model$rho2_0 , 4)),"\n")
cat("Adj.Rho-squared vs equal shares : ", ifelse(!is.numeric(model$adjRho2_0), "Not applicable", round(model$adjRho2_0, 4)),"\n")
cat("Rho-squared vs observed shares : ", ifelse(!is.numeric(model$rho2_C ), "Not applicable", round(model$rho2_C , 4)),"\n")
cat("Adj.Rho-squared vs observed shares : ", ifelse(!is.numeric(model$adjRho2_C), "Not applicable", round(model$adjRho2_C, 4)),"\n")
cat("AIC : ",round(model$AIC, 2),"\n")
cat("BIC : ",round(model$BIC, 2),"\n")
cat("\n")
cat("Equiv. estimated parameters : ", model$nFreeParams,"\n", sep="")
if(model$HB_n_nonrandom>0){
cat(" (non-random parameters : ", model$HB_n_nonrandom,")\n", sep="")
}
if(model$HB_n_random_means>0){
cat(" (means of random parameters : ", model$HB_n_random_means,")\n", sep="")
}
if(model$HB_n_random_covar>0){
cat(" (covariance matrix terms : ", model$HB_n_random_covar,")\n", sep="")
}
if(length(model$LLout)>1){
cat("\n")
for(j in 2:length(model$LLout)){
nam <- names(model$LLout)[j]
sp1 <- ifelse(6+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-6), collapse=""), "")
sp2 <- ifelse(9+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-10), collapse=""), "")
cat("LL(0,", nam, ")", sp1, ": ", ifelse(is.finite(model$LL0[j]), round(model$LL0[j],2), "Not applicable"),"\n", sep="")
#cat("LL(final,", nam, ")", sp2, ": ", model$LLout[j], sep="")
txt <- paste0("LL(final,", nam, ")", sp2, ": ", round(model$LLout[j],2))
if(!is.finite(model$LLout[j])) cat(txt, " Likelihood equal to zero for at least\n",
paste0(rep(" ", nchar(txt)), collapse=""),
" one individual in this component.", sep="") else cat(txt)
cat('\n')
}; rm(nam, sp1, sp2)
}
cat("\n")
f <- function(t){
tmpH <- floor(t/60^2)
tmpM <- floor((t-tmpH*60^2)/60)
tmpS <- round(t-tmpH*60^2-tmpM*60,2)
paste(formatC(tmpH,width=2,format='d',flag=0),
formatC(tmpM,width=2,format='d',flag=0),
tmpS,sep=':')
}
cat("Time taken (hh:mm:ss) : ",f(model$timeTaken),"\n")
cat(" pre-estimation : ",f(model$timePre),"\n")
cat(" estimation : ",f(model$timeEst),"\n")
cat(" post-estimation : ",f(model$timePost),"\n")
cat("\n\n")
cat("Summary of parameter chains\n\n")
ans <- list()
if(length(apollo_HB$gVarNamesFixed)>0 | length(model$apollo_fixed)>0){
cat("Non-random coefficients","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n\n")
print(round(model$HB_chains_non_random,4))
cat("\n")
ans[["non_random"]] <- model$HB_chains_non_random
}
apollo_HB$gVarNamesFixed <- model$HB_names_nonrandom_params
apollo_HB$gVarNamesNormal <- model$HB_names_random_params
if(any(!is.null(apollo_HB$gVarNamesNormal)) && length(apollo_HB$gVarNamesNormal)>0){
cat("Results for posterior means for random coefficients","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_posterior_means_summary,4))
cat("\n")
cat("Summary of distributions of random coeffients (after distributional transforms)","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_random_params_mean_sd,4))
cat("\n")
if(length(apollo_HB$gVarNamesNormal)>1){
if(printCovar){
cat("Covariance matrix of random coeffients (after distributional transforms)","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_random_params_covar,4))
cat("\n")
}
if(printCorr){
cat("Correlation matrix of random coeffients (after distributional transforms)","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_random_params_corr,4))
cat("\n")
}
}
cat("Upper level model results for mean parameters for underlying Normals","\n")
if(scaling_used) cat("These outputs have NOT had the scaling used in estimation applied to them\n")
print(round(model$HB_chains_normals_means,4))
cat("\n")
if(printCovar){
cat("Upper level model results for covariance matrix for underlying Normals (means across iterations)","\n")
if(scaling_used) cat("These outputs have NOT had the scaling used in estimation applied to them\n")
print(round(model$HB_chains_normals_mean_of_covar,4))
cat("\n")
cat("Upper level model results for covariance matrix for underlying Normals (SD across iterations)","\n")
if(scaling_used) cat("These outputs have NOT had the scaling used in estimation applied to them\n")
print(round(model$HB_chains_normals_sd_of_covar,4))
cat("\n")
}
if(printHBconvergence){
cat("Chain convergence report (Geweke test)\n\n")
if(!is.null(model[["HB_Geweke_test_non_random"]])){
cat("Fixed (non random) parameters (t-test value for Geweke test)\n")
#tmp <- coda::geweke.diag(model$HB_iterations_non_random[,2:(ncol(model$HB_iterations_non_random))], frac1=0.1, frac2=0.5)[[1]]
#names(tmp) <- model$HB_names_nonrandom_params
#print( round(tmp, 4) )
print( round(model$HB_Geweke_test_non_random, 4) )
cat("\n")
}
if(!is.null(model[["HB_Geweke_test_means"]])){
cat("Random parameters (t-test value for Geweke test)\n")
#tmp <- coda::geweke.diag(model$HB_iterations_means[,2:(ncol(model$HB_iterations_means))], frac1=0.1, frac2=0.5)[[1]]
#print( round(tmp, 4) )
print( round(model$HB_Geweke_test_means, 4) )
cat("\n")
}
if(!is.null(model[["HB_Geweke_test_covar"]])){
cat("Covariances of random parameters (t-test value for Geweke test)\n")
# This assumes the matrix is square
#tmp <- c()
#for(i in 1:dim(model$HB_iterations_covar)[1]) for(j in 1:i){
# if(i==1 & j==1) Dmatrix <- as.matrix(model$HB_iterations_covar[i,j,]) else Dmatrix <- cbind(Dmatrix, as.vector(model$HB_iterations_covar[i,j,]))
# tmp <- c(tmp, paste(colnames(model$HB_iterations_means)[i+1],colnames(model$HB_iterations_means)[j+1], sep="_"))
#}
#colnames(Dmatrix) <- tmp
#tmp <- coda::geweke.diag(Dmatrix, frac1=0.1, frac2=0.5)[[1]]
#print( round(tmp, 4) )
print( round(model$HB_Geweke_test_covar, 4) )
}
cat("\n\n")
}
if(printHBiterations){
cat("\nIteration details (overview)")
cat("\n----------------------------\n")
print(model[["HB_iterations_detail"]], row.names = FALSE)
}
### 3 Feb
if(printFunctions){
cat("\nSettings and functions used in model definition:\n")
cat("\napollo_control")
cat("\n--------------\n")
tmp=model$apollo_control
tmp$cpp=NULL
tmp$matrixMult=NULL
tmp$subMaxV=NULL
txt=t(data.frame(tmp))
colnames(txt)="Value"
print(txt)
cat("\napollo_HB")
cat("\n---------\n")
print(model$apollo_HB)
### start changes 12 April 2023
if (length(model[["HB_names_nonrandom_params"]]) > 0) {
cat("\nNon-random parameters:")
cat("\n----------------------n")
cat(paste0(model[["HB_names_nonrandom_params"]], "\n", collapse = ""))
}
if (length(model[["HB_names_random_params"]]) > 0) {
cat("\nRandom parameters (Distribution):")
cat("\n---------------------------------n")
cat(paste0(paste(model[["HB_names_random_params"]], "(", model[["distributions"]],
")"), "\n"), collapse = "", sep = "")
}
if (!is.null(model[["constraints"]])) {
cond <- c("<", ">")
cat("\nConstraints applied to random parameters (param1 - inequality - param2):")
cat("\n------------------------------------------------------------------------\n")
for (i in 1:length(model[["constraints"]])) {
if (model[["constraints"]][[i]][3] == 0) {
cat(model[["HB_names_random_params"]][model[["constraints"]][[i]][1]],
cond[model[["constraints"]][[i]][2]], 0,
"\n")
}
if (model[["constraints"]][[i]][3] != 0) {
cat(model[["HB_names_random_params"]][model[["constraints"]][[i]][1]],
cond[model[["constraints"]][[i]][2]], model[["HB_names_random_params"]][model[["constraints"]][[i]][3]],
"\n")
}
}
}
if (!is.null(model[["pv"]])) {
cat("\nPrior Variance-Covariance Matrix:")
cat("\n---------------------------------\n")
print(model[["pv"]])
}
### end changes 12 April 2023
cat("\n")
cat("\n\napollo_probabilities")
cat("\n----------------------\n")
txt=capture.output(print(model$apollo_probabilities))
cat(txt, sep="\n")
}
### end 3 Feb
ans[["random_mean"]] <- model$HB_chains_normals_means
ans[["random_cov_mean"]] <- model$HB_chains_normals_mean_of_covar
ans[["random_cov_sd"]] <- model$HB_chains_normals_sd_of_covar
ans[["HB_random_params_mean_sd"]] <- model$HB_random_params_mean_sd
ans[["posterior"]] <- model$HB_posterior_means_summary
ans[["HB_random_params_covar"]] <- model$HB_random_params_covar
ans[["HB_random_params_corr"]] <- model$HB_random_params_corr
}
return(invisible(ans))
}
# ####################### #
#### CLASSICAL OUTPUT ####
# ####################### #
### change 7 August (next line, and then replacing 2 by multiplier in several lines belwo)
if(printPVal==2) pMult <- 2 else pMult <- 1
output=cbind(model$estimate,
model$se,
model$estimate/model$se,
pMult*(1-stats::pnorm(abs(model$estimate/model$se))),
(model$estimate-1)/model$se,
pMult*(1-stats::pnorm(abs((model$estimate-1)/model$se))),
model$robse,
model$estimate/model$robse,
pMult*(1-stats::pnorm(abs(model$estimate/model$robse))),
(model$estimate-1)/model$robse,
pMult*(1-stats::pnorm(abs((model$estimate-1)/model$robse))))
#output <- signif(output,4)
### change 7
if(pMult==2){
colnames(output) <- c('Estimate',
's.e.', 't.rat.(0)', 'p(2-sided)','t.rat(1)','p(2-sided)',
'Rob.s.e.','Rob.t.rat.(0)','p(2-sided)','Rob.t.rat.(1)','p(2-sided)')
} else {
colnames(output) <- c('Estimate',
's.e.', 't.rat.(0)', 'p(1-sided)','t.rat(1)','p(1-sided)',
'Rob.s.e.','Rob.t.rat.(0)','p(1-sided)','Rob.t.rat.(1)','p(1-sided)')
}
rownames(output) <- names(model$estimate)
# If there is a bootstrap covariance matrix
if(!is.null(model$bootvarcov)){
tmp <- model$bootse
tmp <- cbind(`Bootstrap.s.e.` = tmp,
`Bootstrap.t.rat.(0)` = model$estimate/tmp,
`p(2-sided)` = 2*(1-stats::pnorm(abs(model$estimate/tmp))),
`p(1-sided)` = 1*(1-stats::pnorm(abs(model$estimate/tmp))),
`Bootstrap.t.rat.(1)` = (model$estimate-1)/tmp,
`p(2-sided)` = 2*(1-stats::pnorm(abs((model$estimate-1)/tmp))),
`p(1-sided)` = 1*(1-stats::pnorm(abs((model$estimate-1)/tmp))) )
if(!printPVal) tmp <- tmp[,-grep('p(', colnames(tmp), fixed=TRUE)]
if(printPVal & pMult==1) tmp <- tmp[,-grep('p(2', colnames(tmp), fixed=TRUE)]
if(printPVal & pMult==2) tmp <- tmp[,-grep('p(1', colnames(tmp), fixed=TRUE)]
if(!printT1) tmp <- tmp[,-grep('t.rat.(1', colnames(tmp), fixed=TRUE)]
output <- cbind(output, tmp)
}
dropcolumns = NULL
if(printClassical==FALSE) dropcolumns = c(dropcolumns,2,3,4,5,6)
if(printT1==FALSE) dropcolumns = c(dropcolumns,5,6,10,11,15,16)
if(printPVal==FALSE) dropcolumns = c(dropcolumns,4,6,9,11,14,16)
dropcolumns = unique(dropcolumns)
if(length(dropcolumns)>0) output = output[,-dropcolumns, drop=FALSE]
cat("LL(start) : ",round(model$LLStart,2),"\n", sep="")
if(length(model$LLout)==1) cat("LL at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL(final) : ",round(model$maximum,2),"\n",sep="")
if(length(model$LLout)>1 ) cat("LL(final, whole model) : ",round(model$maximum,2),"\n",sep="")
cat("Rho-squared vs equal shares : ", ifelse(!is.numeric(model$rho2_0 ), "Not applicable", round(model$rho2_0 , 4)),"\n")
cat("Adj.Rho-squared vs equal shares : ", ifelse(!is.numeric(model$adjRho2_0), "Not applicable", round(model$adjRho2_0, 4)),"\n")
cat("Rho-squared vs observed shares : ", ifelse(!is.numeric(model$rho2_C ), "Not applicable", round(model$rho2_C , 4)),"\n")
cat("Adj.Rho-squared vs observed shares : ", ifelse(!is.numeric(model$adjRho2_C), "Not applicable", round(model$adjRho2_C, 4)),"\n")
cat("AIC : ",round(model$AIC, 2),"\n")
cat("BIC : ",round(model$BIC, 2),"\n")
if(length(model$LLout)>1){
cat("\n")
for(j in 2:length(model$LLout)){
nam <- names(model$LLout)[j]
sp1 <- ifelse(6+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-6), collapse=""), "")
sp2 <- ifelse(9+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-10), collapse=""), "")
cat("LL(0,", nam, ")", sp1, ": ", ifelse(is.finite(model$LL0[j]), round(model$LL0[j],2), "Not applicable"),"\n", sep="")
#cat("LL(final,", nam, ")", sp2, ": ", model$LLout[j], sep="")
txt <- paste0("LL(final,", nam, ")", sp2, ": ", round(model$LLout[j],2))
if(!is.finite(model$LLout[j])) cat(txt, " Likelihood equal to zero for at least\n",
paste0(rep(" ", nchar(txt)), collapse=""),
" one individual in this component.", sep="") else cat(txt)
cat('\n')
}; rm(nam, sp1, sp2)
}
cat("\n")
cat("Estimated parameters : ", model$nFreeParams,"\n", sep="")
#cat("Norm of the gradient at optimum : ",round( sqrt(sum(model$gradient^2)),2), "\n\n")
f <- function(t){
tmpH <- floor(t/60^2)
tmpM <- floor((t-tmpH*60^2)/60)
tmpS <- round(t-tmpH*60^2-tmpM*60,2)
paste(formatC(tmpH,width=2,format='d',flag=0),
formatC(tmpM,width=2,format='d',flag=0),
tmpS,sep=':')
}
cat("Time taken (hh:mm:ss) : ",f(model$timeTaken),"\n")
cat(" pre-estimation : ",f(model$timePre),"\n")
cat(" estimation : ",f(model$timeEst),"\n")
if(!is.null(model$nIterPrescaling) && (model$nIterPrescaling>0 & model$nIterPostscaling>0)){
cat(" initial estimation : ",f(model$timeEstPrescaling), "\n")
cat(" estimation after rescaling : ",f(model$timeEstPostscaling), "\n")
}
cat(" post-estimation : ",f(model$timePost),"\n")
if(!is.null(model$successfulEstimation) && !model$successfulEstimation) tmp <- paste0("(",model$message,")") else tmp <- ""
cat("Iterations : ",model$nIter, tmp, "\n")
if(!is.null(model$nIterPrescaling) && (model$nIterPrescaling>0 & model$nIterPostscaling>0)){
cat(" initial estimation : ",model$nIterPrescaling, "\n")
cat(" estimation after rescaling : ",model$nIterPostscaling, "\n")
}
if(model$bootstrapSE>0) cat("Number of bootstrap repetitions : ", model$bootstrapSE, "\n")
#if(!is.null(model$eigValue) && !anyNA(model$eigValue)){
# cat("Min abs eigenvalue of Hessian : ",round(min(abs(model$eigValue)),6),"\n")
# if(any(model$eigValue>0)) apollo_print("Some eigenvalues of Hessian are positive, indicating potential problems!")
#}
# Printing of constraints, if used
cat('\n')
if(is.null(model$constraints)) cat("Unconstrained optimisation.\n") else {
cat("Constrained optimisation:\n")
if( is.null(model$apollo_constraints)) cat(" Please refer to original model script to see the constraints.")
if(!is.null(model$apollo_constraints)) cat(paste0(" ", model$apollo_constraints), sep="\n")
cat("\n")
}; rm(test)
cat("\n")
if(!printClassical & anyNA(model$se[!(names(model$estimate) %in% model$apollo_fixed)]) ){
apollo_print('Classical standard errors could not be calculated for some parameters. This could point to an identification or estimation problem.', type="w")
}
if(scaling_used) apollo_print("These outputs have had the scaling used in estimation applied to them.")
cat("Estimates:\n")
if(nrow(output)>options("max.print")) options(max.print=nrow(output)+100)
if(!printFixed && length(model$apollo_fixed)>0) output <- output[!(rownames(output) %in% model$apollo_fixed),]
apollo_print(output) #print(output, digits=4)
cat('\n')
### Print diagnostics
# Model structure
if(!is.null(model$componentReport)) for(r in model$componentReport){
test <- is.list(r) && !is.null(r$param) && length(r$param)>0 && modelOutput_settings$printModelStructure
if(test) for(j in r$param) cat(j, '\n', sep='')
if(test) cat('\n')
}
if(!is.null(model$componentReport)) for(r in model$componentReport){
test <- is.list(r) && !is.null(r$data) && length(r$data)>0 && modelOutput_settings$printDataReport
if(test) for(j in r$data ) cat(j, '\n', sep='')
if(test) cat('\n')
}
#if(exists('r')) rm(r)
#if(exists('j')) rm(j)
### Fill shorter param names with spaces
longNames <- names( model$estimate )
if(length(model$apollo_fixed)>0) longNames <- longNames[-which(longNames %in% model$apollo_fixed)]
maxLen <- max(nchar(longNames))
for(i in 1:length(longNames)) longNames[i] <- paste0(paste0(rep(" ", maxLen-nchar(longNames[i])), collapse=""), longNames[i])
if(printCovar){
if(printClassical==TRUE){
cat("\n")
cat("Classical covariance matrix:\n")
tmp <- model$varcov
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
cat("\n")
cat("Robust covariance matrix:\n")
tmp <- model$robvarcov
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
if(model$bootstrapSE>0){
cat("\n")
cat("Bootstrap covariance matrix:\n")
tmp <- model$bootvarcov
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
}
if(printCorr){
if(printClassical==TRUE){
cat("\n")
cat("Classical correlation matrix:\n")
tmp <- model$corrmat
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
cat("\n")
cat("Robust correlation matrix:\n")
tmp <- model$robcorrmat
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
if(model$bootstrapSE>0){
cat("\n")
cat("Bootstrap correlation matrix:\n")
tmp <- model$bootcorrmat
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
}
if(printOutliers>0){
outliers <- data.frame(ID=names(model$avgCP), avgChoiceProb=model$avgCP)
colnames(outliers) <- c("ID","Avg prob per choice")
if(!model$apollo_control$panelData) colnames(outliers) <- c("row", "Avg prob per choice")
outliers <- outliers[order(outliers[,2]),]
if(printOutliers==TRUE) printOutliers=20
printOutliers=floor(min(printOutliers,nrow(outliers)))
cat("\n",printOutliers,"most extreme outliers in terms of lowest average per choice prediction:\n")
print(outliers[(1:printOutliers),], row.names=FALSE)
}
if(printChange){
cat("\nChanges in parameter estimates from starting values:\n")
tmp <- cbind(model$apollo_beta, model$estimate[names(model$apollo_beta)],
model$estimate[names(model$apollo_beta)]-model$apollo_beta)
colnames(tmp) <- c("Initial", "Estimate", "Difference")
apollo_print(tmp)
}
if(printFunctions){
cat("\nSettings and functions used in model definition:\n")
cat("\napollo_control")
cat("\n--------------\n")
### change 27 July
##txt=t(data.frame(model$apollo_control))
tmp=model$apollo_control
tmp$cpp=NULL
##tmp$analyticGrad=NULL
tmp$matrixMult=NULL
tmp$subMaxV=NULL
txt=t(data.frame(tmp))
### end change 27 July
colnames(txt)="Value"
print(txt)
cat("\nHessian routines attempted")
cat("\n--------------------------\n")
cat(model$hessianMethodsAttempted)
cat("\n")
if(!is.null(model$scaling) && !all(model$scaling==1)){
cat("\nScaling in estimation")
cat("\n---------------------\n")
txt=as.matrix(model$scaling)
colnames(txt)="Value"
print(txt)}
if(!is.null(model$hessianScaling) && !all(model$hessianScaling==1)){
cat("\nScaling used in computing Hessian")
cat("\n---------------------------------\n")
txt=as.matrix(model$hessianScaling)
colnames(txt)="Value"
print(txt)}
if(is.function(model$apollo_randCoeff)){
cat("\n\napollo_randCoeff")
cat("\n------------------\n")
txt=capture.output(print(model$apollo_randCoeff))
#cat(txt[1:(length(txt)-1)],sep="\n")}
cat(txt, sep="\n")}
if(is.function(model$apollo_lcPars)){
cat("\n\napollo_lcPars")
cat("\n---------------\n")
txt=capture.output(print(model$apollo_lcPars))
#cat(txt[1:(length(txt)-1)],sep="\n")}
cat(txt, sep="\n")}
cat("\n\napollo_probabilities")
cat("\n----------------------\n")
txt=capture.output(print(model$apollo_probabilities))
#cat(txt[1:(length(txt)-1)],sep="\n")
cat(txt, sep="\n")
}
invisible(output)
}
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R Package Documentation
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Defines functions apollo_modelOutput
Documented in apollo_modelOutput
#' Prints estimation results to console
#'
#' Prints estimation results to console. Amount of information presented can be adjusted through arguments.
#'
#' Prints to screen the output of a model previously estimated by apollo_estimate()
#' @param model Model object. Estimated model object as returned by function \link{apollo_estimate}.
#' @param modelOutput_settings List. Contains settings for this function. User input is required for all settings except those with a default or marked as optional.
#' \itemize{
#' \item \strong{\code{printChange}}: Logical. TRUE for printing difference between starting values and estimates. FALSE by default.
#' \item \strong{\code{printClassical}}: Logical. TRUE for printing classical standard errors. TRUE by default.
#' \item \strong{\code{printCorr}}: Boolean. TRUE for printing parameters correlation matrix. If \code{printClassical=TRUE}, both classical and robust matrices are printed. For Bayesian estimation, this setting is used for the covariane of random parameters. FALSE by default.
#' \item \strong{\code{printCovar}}: Boolean. TRUE for printing parameters covariance matrix. If \code{printClassical=TRUE}, both classical and robust matrices are printed. For Bayesian estimation, this setting is used for the correlation of random parameters. FALSE by default.
#' \item \strong{\code{printDataReport}}: Logical. TRUE for printing summary of choices in database and other diagnostics. FALSE by default.
#' \item \strong{\code{printFixed}}: Logical. TRUE for printing fixed parameters among estimated parameter. TRUE by default.
#' \item \strong{\code{printFunctions}}: Logical. TRUE for printing apollo_control, apollo_randCoeff (when available), apollo_lcPars (when available) and apollo_probabilities. FALSE by default.
#' \item \strong{\code{printHBconvergence}}: Boolean. TRUE for printing Geweke convergence tests. FALSE by default.
#' \item \strong{\code{printHBiterations}}: Boolean. TRUE for printing an iterations report for HB estimation. TRUE by default.
#' \item \strong{\code{printModelStructure}}: Logical. TRUE for printing model structure. TRUE by default.
#' \item \strong{\code{printOutliers}}: Logical or Scalar. TRUE for printing 20 individuals with worst average fit across observations. FALSE by default. If Scalar is given, this replaces the default of 20.
#' \item \strong{\code{printPVal}}: Logical or Scalar. TRUE or 1 for printing p-values for one-sided test, 2 for printing p-values for two-sided test, FALSE for not printing p-values. FALSE by default.
#' \item \strong{\code{printT1}}: Logical. If TRUE, t-test for H0: apollo_beta=1 are printed. FALSE by default.
#' }
#' @return A matrix of coefficients, s.d. and t-tests (invisible)
#' @export
#' @importFrom coda geweke.diag
#' @importFrom utils capture.output
apollo_modelOutput=function(model, modelOutput_settings=NA){
if(length(modelOutput_settings)==1 && is.na(modelOutput_settings)) modelOutput_settings=list()
if(is.null(modelOutput_settings[["printModelStructure"]])) modelOutput_settings[["printModelStructure"]] = TRUE
if(is.null(modelOutput_settings[["printDataReport"]])) modelOutput_settings[["printDataReport"]] = FALSE
if(!is.null(modelOutput_settings[["printDiagnostics"]])){
modelOutput_settings[["printModelStructure"]] = modelOutput_settings[["printDiagnostics"]]
modelOutput_settings[["printDataReport"]] = modelOutput_settings[["printDiagnostics"]]
}
if(is.null(modelOutput_settings[["printClassical"]])) modelOutput_settings[["printClassical"]] = TRUE
if(is.null(modelOutput_settings[["printPVal"]])) modelOutput_settings[["printPVal"]] = FALSE
if(is.null(modelOutput_settings[["printT1"]])) modelOutput_settings[["printT1"]] = FALSE
if(is.null(modelOutput_settings[["printCovar"]])) modelOutput_settings[["printCovar"]] = FALSE
if(is.null(modelOutput_settings[["printCorr"]])) modelOutput_settings[["printCorr"]] = FALSE
if(is.null(modelOutput_settings[["printHBconvergence"]])) modelOutput_settings[["printHBconvergence"]] = FALSE
if(is.null(modelOutput_settings[["printHBiterations"]])) modelOutput_settings[["printHBiterations"]] = FALSE
if(is.null(modelOutput_settings[["printOutliers"]])) modelOutput_settings[["printOutliers"]] = FALSE
if(is.null(modelOutput_settings[["printChange"]])) modelOutput_settings[["printChange"]] = FALSE
if(is.null(modelOutput_settings[["printFunctions"]])) modelOutput_settings[["printFunctions"]] = FALSE
if(is.null(modelOutput_settings[["printFixed"]])) modelOutput_settings[["printFixed"]] = TRUE
printClassical = modelOutput_settings[["printClassical"]]
printPVal = modelOutput_settings[["printPVal"]]
printT1 = modelOutput_settings[["printT1"]]
printDiagnostics = modelOutput_settings[["printDiagnostics"]]
printCovar = modelOutput_settings[["printCovar"]]
printCorr = modelOutput_settings[["printCorr"]]
printHBconvergence = modelOutput_settings[["printHBconvergence"]]
printHBiterations = modelOutput_settings[["printHBiterations"]]
printOutliers = modelOutput_settings[["printOutliers"]]
printChange = modelOutput_settings[["printChange"]]
printFunctions = modelOutput_settings[["printFunctions"]]
printFixed = modelOutput_settings[["printFixed"]]
test <- !is.null(model$manualScaling) && length(model$manualScaling)==1 && is.logical(model$manualScaling)
if(test) scaling_used <- model$manualScaling else {
test <- !is.null(model$scaling) && is.vector(model$scaling) && is.numeric(model$scaling) && !all(model$scaling==1)
if(test) scaling_used <- TRUE else scaling_used <- FALSE
}
# ####################### #
#### MODEL DESCRIPTION ####
# ####################### #
apollo_control <- model$apollo_control
#nParams <- length(model$apollo_beta)
#nFreeParams <- nParams
#if(!is.null(model$apollo_fixed)) nFreeParams <- nFreeParams - length(model$apollo_fixed)
# Printing model information
if("package:apollo" %in% search()){
apolloVersion <- tryCatch(utils::packageDescription("apollo", fields = "Version"),
warning=function(w) return("alpha"),
error=function(e) return("alpha"))
} else apolloVersion <- "alpha"
#cat('Model run using Apollo for R, version', apolloVersion, 'on', Sys.info()['sysname'], 'by', Sys.info()['user'], '\n')
cat("Model run by ", Sys.info()['user'], " using Apollo ", apolloVersion,
" on R ", paste0(version$major, ".", version$minor),
" for ", Sys.info()['sysname'], ".\n", sep="")
cat("Please acknowledge the use of Apollo by citing Hess & Palma (2019)\n")
cat(" DOI 10.1016/j.jocm.2019.100170\n")
cat(" www.ApolloChoiceModelling.com\n\n")
cat("Model name : ", model$apollo_control$modelName,"\n", sep="")
cat("Model description : ", model$apollo_control$modelDescr,"\n", sep="")
cat("Model run at : ", paste(model$startTime),"\n", sep="")
cat("Estimation method : ", model$estimationRoutine, "\n", sep="")
if(!apollo_control$HB){
cat("Model diagnosis : ",model$message,"\n", sep="")
if(!is.null(model$hessianEigenValue)){
isC <- is.complex(model$hessianEigenValue)
anyZ <- !isC && any(model$hessianEigenValue==0)
anyN <- !isC && any(model$hessianEigenValue<0)
anyP <- !isC && any(model$hessianEigenValue>0)
if(isC) txt <- c("Non-symmetrical hessian", "Complex eigenvalues")
if(!isC && all(model$hessianEigenValue<0)) txt <- c("Maximum found", "Negative definite")
if(!isC && anyZ) txt <- c("Inconclusive test", "Some eigenvalues are zero")
if(!isC && !anyZ && anyN && anyP) txt <- c("Saddle point found", "Some eigenvalues are positive and others negative")
if(!isC && all(model$hessianEigenValue>0)) txt <- c("Minimum found", "Positive definite")
if(isC) txt[3] <- "NA" else txt[3] <- as.character(round(max(model$hessianEigenValue),6))
if(!isC && all(model$hessianEigenValue<0)){
txt[4] <- as.character(signif(max(model$hessianEigenValue)/min(model$hessianEigenValue),6))
}else{
txt[4] <- "not calculated (Hessian is not negative definite)"
}
cat("Optimisation diagnosis : ", txt[1], "\n",
" hessian properties : ", txt[2], "\n",
" maximum eigenvalue : ", txt[3], "\n",
" reciprocal of condition number : ", txt[4], "\n",
sep="")
rm(isC, anyZ, anyN, anyP, txt)
} else if(!is.null(model$eigValue) && !anyNA(model$eigValue)){
cat("Min abs eigenvalue of Hessian : ",round(min(abs(model$eigValue)),6),"\n")
if(any(model$eigValue>0)) apollo_print("Some eigenvalues of Hessian are positive, indicating potential problems!")
}
}
cat("Number of individuals : ", model$nIndivs,"\n", sep="")
cat("Number of rows in database : ", model$nObs,"\n", sep="")
if(!is.null(model$nObsTot)){
cat('Number of modelled outcomes : ', sum(model$nObsTot), '\n', sep='')
if(length(model$nObsTot)>1) for(i in names(model$nObsTot)){
if(nchar(i)>26) txt <- substr(i, 1, 26) else txt <- i # max 26 characters for the component name
if(nchar(txt)<26) txt <- paste0(c(rep(' ', 26 - nchar(txt)), txt), collapse='')
cat(' ', txt, ' : ', model$nObsTot[i], '\n', sep='')
}; cat('\n')
}
cat("Number of cores used : ",model$apollo_control$nCores,"\n")
if(model$apollo_control$mixing){
d <- model$apollo_draws
if(d$interNDraws>0 && length(c(d$interUnifDraws, d$interNormDraws))>0){
cat("Number of inter-individual draws : ", d$interNDraws, ' (', d$interDrawsType, ')', "\n", sep='')
}
if(d$intraNDraws>0 && length(c(d$intraUnifDraws, d$intraNormDraws))>0){
cat("Number of intra-individual draws : ", d$intraNDraws, ' (', d$intraDrawsType, ')', "\n", sep='')
}
if(!model$apollo_control$panelData & model$apollo_control$mixing & d$interNDraws>0){
cat("WARNING: Inter-individual draws were used\n")
cat(" without a panel data structure.\n")
}
rm(d)
} else { if(!apollo_control$HB) cat("Model without mixing\n") }
cat("\n")
# ####################### #
#### HB OUTPUT ####
# ####################### #
if(apollo_control$HB){
apollo_HB <- model$apollo_HB
cat("Estimation carried out using RSGHB\n")
cat("Burn-in iterations : ",model$gNCREP,"\n", sep="")
cat("Post burn-in iterations : ",model$gNEREP,"\n", sep="")
#cat("LL(start) : ",model$LLStart,"\n", sep="")
#cat("LL(0) : ",model$LL0[1],"\n", sep="")
#cat("Average post. LL post burn-in : ",mean(colSums(log(model$cmcLLout))),"\n",sep="")
#cat("Average post. RLH post burn-in : ",round(mean(colMeans((model$cmcRLHout))),4),"\n",sep="")
cat("\n")
apollo_print("Classical model fit statistics were calculated at parameter values obtained using averaging across the post burn-in iterations.")
cat("LL(start) : ",round(model$LLStart,2),"\n", sep="")
if(length(model$LLout)==1) cat("LL at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL(final) : ",round(model$maximum,2),"\n",sep="")
if(length(model$LLout)>1 ) cat("LL(final, whole model) : ",round(model$maximum,2),"\n",sep="")
cat("Rho-squared vs equal shares : ", ifelse(!is.numeric(model$rho2_0 ), "Not applicable", round(model$rho2_0 , 4)),"\n")
cat("Adj.Rho-squared vs equal shares : ", ifelse(!is.numeric(model$adjRho2_0), "Not applicable", round(model$adjRho2_0, 4)),"\n")
cat("Rho-squared vs observed shares : ", ifelse(!is.numeric(model$rho2_C ), "Not applicable", round(model$rho2_C , 4)),"\n")
cat("Adj.Rho-squared vs observed shares : ", ifelse(!is.numeric(model$adjRho2_C), "Not applicable", round(model$adjRho2_C, 4)),"\n")
cat("AIC : ",round(model$AIC, 2),"\n")
cat("BIC : ",round(model$BIC, 2),"\n")
cat("\n")
cat("Equiv. estimated parameters : ", model$nFreeParams,"\n", sep="")
if(model$HB_n_nonrandom>0){
cat(" (non-random parameters : ", model$HB_n_nonrandom,")\n", sep="")
}
if(model$HB_n_random_means>0){
cat(" (means of random parameters : ", model$HB_n_random_means,")\n", sep="")
}
if(model$HB_n_random_covar>0){
cat(" (covariance matrix terms : ", model$HB_n_random_covar,")\n", sep="")
}
if(length(model$LLout)>1){
cat("\n")
for(j in 2:length(model$LLout)){
nam <- names(model$LLout)[j]
sp1 <- ifelse(6+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-6), collapse=""), "")
sp2 <- ifelse(9+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-10), collapse=""), "")
cat("LL(0,", nam, ")", sp1, ": ", ifelse(is.finite(model$LL0[j]), round(model$LL0[j],2), "Not applicable"),"\n", sep="")
#cat("LL(final,", nam, ")", sp2, ": ", model$LLout[j], sep="")
txt <- paste0("LL(final,", nam, ")", sp2, ": ", round(model$LLout[j],2))
if(!is.finite(model$LLout[j])) cat(txt, " Likelihood equal to zero for at least\n",
paste0(rep(" ", nchar(txt)), collapse=""),
" one individual in this component.", sep="") else cat(txt)
cat('\n')
}; rm(nam, sp1, sp2)
}
cat("\n")
f <- function(t){
tmpH <- floor(t/60^2)
tmpM <- floor((t-tmpH*60^2)/60)
tmpS <- round(t-tmpH*60^2-tmpM*60,2)
paste(formatC(tmpH,width=2,format='d',flag=0),
formatC(tmpM,width=2,format='d',flag=0),
tmpS,sep=':')
}
cat("Time taken (hh:mm:ss) : ",f(model$timeTaken),"\n")
cat(" pre-estimation : ",f(model$timePre),"\n")
cat(" estimation : ",f(model$timeEst),"\n")
cat(" post-estimation : ",f(model$timePost),"\n")
cat("\n\n")
cat("Summary of parameter chains\n\n")
ans <- list()
if(length(apollo_HB$gVarNamesFixed)>0 | length(model$apollo_fixed)>0){
cat("Non-random coefficients","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n\n")
print(round(model$HB_chains_non_random,4))
cat("\n")
ans[["non_random"]] <- model$HB_chains_non_random
}
apollo_HB$gVarNamesFixed <- model$HB_names_nonrandom_params
apollo_HB$gVarNamesNormal <- model$HB_names_random_params
if(any(!is.null(apollo_HB$gVarNamesNormal)) && length(apollo_HB$gVarNamesNormal)>0){
cat("Results for posterior means for random coefficients","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_posterior_means_summary,4))
cat("\n")
cat("Summary of distributions of random coeffients (after distributional transforms)","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_random_params_mean_sd,4))
cat("\n")
if(length(apollo_HB$gVarNamesNormal)>1){
if(printCovar){
cat("Covariance matrix of random coeffients (after distributional transforms)","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_random_params_covar,4))
cat("\n")
}
if(printCorr){
cat("Correlation matrix of random coeffients (after distributional transforms)","\n")
if(scaling_used) cat("These outputs have had the scaling used in estimation applied to them\n")
print(round(model$HB_random_params_corr,4))
cat("\n")
}
}
cat("Upper level model results for mean parameters for underlying Normals","\n")
if(scaling_used) cat("These outputs have NOT had the scaling used in estimation applied to them\n")
print(round(model$HB_chains_normals_means,4))
cat("\n")
if(printCovar){
cat("Upper level model results for covariance matrix for underlying Normals (means across iterations)","\n")
if(scaling_used) cat("These outputs have NOT had the scaling used in estimation applied to them\n")
print(round(model$HB_chains_normals_mean_of_covar,4))
cat("\n")
cat("Upper level model results for covariance matrix for underlying Normals (SD across iterations)","\n")
if(scaling_used) cat("These outputs have NOT had the scaling used in estimation applied to them\n")
print(round(model$HB_chains_normals_sd_of_covar,4))
cat("\n")
}
if(printHBconvergence){
cat("Chain convergence report (Geweke test)\n\n")
if(!is.null(model[["HB_Geweke_test_non_random"]])){
cat("Fixed (non random) parameters (t-test value for Geweke test)\n")
#tmp <- coda::geweke.diag(model$HB_iterations_non_random[,2:(ncol(model$HB_iterations_non_random))], frac1=0.1, frac2=0.5)[[1]]
#names(tmp) <- model$HB_names_nonrandom_params
#print( round(tmp, 4) )
print( round(model$HB_Geweke_test_non_random, 4) )
cat("\n")
}
if(!is.null(model[["HB_Geweke_test_means"]])){
cat("Random parameters (t-test value for Geweke test)\n")
#tmp <- coda::geweke.diag(model$HB_iterations_means[,2:(ncol(model$HB_iterations_means))], frac1=0.1, frac2=0.5)[[1]]
#print( round(tmp, 4) )
print( round(model$HB_Geweke_test_means, 4) )
cat("\n")
}
if(!is.null(model[["HB_Geweke_test_covar"]])){
cat("Covariances of random parameters (t-test value for Geweke test)\n")
# This assumes the matrix is square
#tmp <- c()
#for(i in 1:dim(model$HB_iterations_covar)[1]) for(j in 1:i){
# if(i==1 & j==1) Dmatrix <- as.matrix(model$HB_iterations_covar[i,j,]) else Dmatrix <- cbind(Dmatrix, as.vector(model$HB_iterations_covar[i,j,]))
# tmp <- c(tmp, paste(colnames(model$HB_iterations_means)[i+1],colnames(model$HB_iterations_means)[j+1], sep="_"))
#}
#colnames(Dmatrix) <- tmp
#tmp <- coda::geweke.diag(Dmatrix, frac1=0.1, frac2=0.5)[[1]]
#print( round(tmp, 4) )
print( round(model$HB_Geweke_test_covar, 4) )
}
cat("\n\n")
}
if(printHBiterations){
cat("\nIteration details (overview)")
cat("\n----------------------------\n")
print(model[["HB_iterations_detail"]], row.names = FALSE)
}
### 3 Feb
if(printFunctions){
cat("\nSettings and functions used in model definition:\n")
cat("\napollo_control")
cat("\n--------------\n")
tmp=model$apollo_control
tmp$cpp=NULL
tmp$matrixMult=NULL
tmp$subMaxV=NULL
txt=t(data.frame(tmp))
colnames(txt)="Value"
print(txt)
cat("\napollo_HB")
cat("\n---------\n")
print(model$apollo_HB)
### start changes 12 April 2023
if (length(model[["HB_names_nonrandom_params"]]) > 0) {
cat("\nNon-random parameters:")
cat("\n----------------------n")
cat(paste0(model[["HB_names_nonrandom_params"]], "\n", collapse = ""))
}
if (length(model[["HB_names_random_params"]]) > 0) {
cat("\nRandom parameters (Distribution):")
cat("\n---------------------------------n")
cat(paste0(paste(model[["HB_names_random_params"]], "(", model[["distributions"]],
")"), "\n"), collapse = "", sep = "")
}
if (!is.null(model[["constraints"]])) {
cond <- c("<", ">")
cat("\nConstraints applied to random parameters (param1 - inequality - param2):")
cat("\n------------------------------------------------------------------------\n")
for (i in 1:length(model[["constraints"]])) {
if (model[["constraints"]][[i]][3] == 0) {
cat(model[["HB_names_random_params"]][model[["constraints"]][[i]][1]],
cond[model[["constraints"]][[i]][2]], 0,
"\n")
}
if (model[["constraints"]][[i]][3] != 0) {
cat(model[["HB_names_random_params"]][model[["constraints"]][[i]][1]],
cond[model[["constraints"]][[i]][2]], model[["HB_names_random_params"]][model[["constraints"]][[i]][3]],
"\n")
}
}
}
if (!is.null(model[["pv"]])) {
cat("\nPrior Variance-Covariance Matrix:")
cat("\n---------------------------------\n")
print(model[["pv"]])
}
### end changes 12 April 2023
cat("\n")
cat("\n\napollo_probabilities")
cat("\n----------------------\n")
txt=capture.output(print(model$apollo_probabilities))
cat(txt, sep="\n")
}
### end 3 Feb
ans[["random_mean"]] <- model$HB_chains_normals_means
ans[["random_cov_mean"]] <- model$HB_chains_normals_mean_of_covar
ans[["random_cov_sd"]] <- model$HB_chains_normals_sd_of_covar
ans[["HB_random_params_mean_sd"]] <- model$HB_random_params_mean_sd
ans[["posterior"]] <- model$HB_posterior_means_summary
ans[["HB_random_params_covar"]] <- model$HB_random_params_covar
ans[["HB_random_params_corr"]] <- model$HB_random_params_corr
}
return(invisible(ans))
}
# ####################### #
#### CLASSICAL OUTPUT ####
# ####################### #
### change 7 August (next line, and then replacing 2 by multiplier in several lines belwo)
if(printPVal==2) pMult <- 2 else pMult <- 1
output=cbind(model$estimate,
model$se,
model$estimate/model$se,
pMult*(1-stats::pnorm(abs(model$estimate/model$se))),
(model$estimate-1)/model$se,
pMult*(1-stats::pnorm(abs((model$estimate-1)/model$se))),
model$robse,
model$estimate/model$robse,
pMult*(1-stats::pnorm(abs(model$estimate/model$robse))),
(model$estimate-1)/model$robse,
pMult*(1-stats::pnorm(abs((model$estimate-1)/model$robse))))
#output <- signif(output,4)
### change 7
if(pMult==2){
colnames(output) <- c('Estimate',
's.e.', 't.rat.(0)', 'p(2-sided)','t.rat(1)','p(2-sided)',
'Rob.s.e.','Rob.t.rat.(0)','p(2-sided)','Rob.t.rat.(1)','p(2-sided)')
} else {
colnames(output) <- c('Estimate',
's.e.', 't.rat.(0)', 'p(1-sided)','t.rat(1)','p(1-sided)',
'Rob.s.e.','Rob.t.rat.(0)','p(1-sided)','Rob.t.rat.(1)','p(1-sided)')
}
rownames(output) <- names(model$estimate)
# If there is a bootstrap covariance matrix
if(!is.null(model$bootvarcov)){
tmp <- model$bootse
tmp <- cbind(`Bootstrap.s.e.` = tmp,
`Bootstrap.t.rat.(0)` = model$estimate/tmp,
`p(2-sided)` = 2*(1-stats::pnorm(abs(model$estimate/tmp))),
`p(1-sided)` = 1*(1-stats::pnorm(abs(model$estimate/tmp))),
`Bootstrap.t.rat.(1)` = (model$estimate-1)/tmp,
`p(2-sided)` = 2*(1-stats::pnorm(abs((model$estimate-1)/tmp))),
`p(1-sided)` = 1*(1-stats::pnorm(abs((model$estimate-1)/tmp))) )
if(!printPVal) tmp <- tmp[,-grep('p(', colnames(tmp), fixed=TRUE)]
if(printPVal & pMult==1) tmp <- tmp[,-grep('p(2', colnames(tmp), fixed=TRUE)]
if(printPVal & pMult==2) tmp <- tmp[,-grep('p(1', colnames(tmp), fixed=TRUE)]
if(!printT1) tmp <- tmp[,-grep('t.rat.(1', colnames(tmp), fixed=TRUE)]
output <- cbind(output, tmp)
}
dropcolumns = NULL
if(printClassical==FALSE) dropcolumns = c(dropcolumns,2,3,4,5,6)
if(printT1==FALSE) dropcolumns = c(dropcolumns,5,6,10,11,15,16)
if(printPVal==FALSE) dropcolumns = c(dropcolumns,4,6,9,11,14,16)
dropcolumns = unique(dropcolumns)
if(length(dropcolumns)>0) output = output[,-dropcolumns, drop=FALSE]
cat("LL(start) : ",round(model$LLStart,2),"\n", sep="")
if(length(model$LLout)==1) cat("LL at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at equal shares, LL(0) : ",ifelse(is.numeric(model$LL0[1]),round(model$LL0[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)>1 ) cat("LL (whole model) at observed shares, LL(C) : ",ifelse(is.numeric(model$LLC[1]),round(model$LLC[1],2),"Not applicable"),"\n", sep="")
if(length(model$LLout)==1) cat("LL(final) : ",round(model$maximum,2),"\n",sep="")
if(length(model$LLout)>1 ) cat("LL(final, whole model) : ",round(model$maximum,2),"\n",sep="")
cat("Rho-squared vs equal shares : ", ifelse(!is.numeric(model$rho2_0 ), "Not applicable", round(model$rho2_0 , 4)),"\n")
cat("Adj.Rho-squared vs equal shares : ", ifelse(!is.numeric(model$adjRho2_0), "Not applicable", round(model$adjRho2_0, 4)),"\n")
cat("Rho-squared vs observed shares : ", ifelse(!is.numeric(model$rho2_C ), "Not applicable", round(model$rho2_C , 4)),"\n")
cat("Adj.Rho-squared vs observed shares : ", ifelse(!is.numeric(model$adjRho2_C), "Not applicable", round(model$adjRho2_C, 4)),"\n")
cat("AIC : ",round(model$AIC, 2),"\n")
cat("BIC : ",round(model$BIC, 2),"\n")
if(length(model$LLout)>1){
cat("\n")
for(j in 2:length(model$LLout)){
nam <- names(model$LLout)[j]
sp1 <- ifelse(6+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-6), collapse=""), "")
sp2 <- ifelse(9+nchar(nam)<33, paste0(rep(" ",33-nchar(nam)-10), collapse=""), "")
cat("LL(0,", nam, ")", sp1, ": ", ifelse(is.finite(model$LL0[j]), round(model$LL0[j],2), "Not applicable"),"\n", sep="")
#cat("LL(final,", nam, ")", sp2, ": ", model$LLout[j], sep="")
txt <- paste0("LL(final,", nam, ")", sp2, ": ", round(model$LLout[j],2))
if(!is.finite(model$LLout[j])) cat(txt, " Likelihood equal to zero for at least\n",
paste0(rep(" ", nchar(txt)), collapse=""),
" one individual in this component.", sep="") else cat(txt)
cat('\n')
}; rm(nam, sp1, sp2)
}
cat("\n")
cat("Estimated parameters : ", model$nFreeParams,"\n", sep="")
#cat("Norm of the gradient at optimum : ",round( sqrt(sum(model$gradient^2)),2), "\n\n")
f <- function(t){
tmpH <- floor(t/60^2)
tmpM <- floor((t-tmpH*60^2)/60)
tmpS <- round(t-tmpH*60^2-tmpM*60,2)
paste(formatC(tmpH,width=2,format='d',flag=0),
formatC(tmpM,width=2,format='d',flag=0),
tmpS,sep=':')
}
cat("Time taken (hh:mm:ss) : ",f(model$timeTaken),"\n")
cat(" pre-estimation : ",f(model$timePre),"\n")
cat(" estimation : ",f(model$timeEst),"\n")
if(!is.null(model$nIterPrescaling) && (model$nIterPrescaling>0 & model$nIterPostscaling>0)){
cat(" initial estimation : ",f(model$timeEstPrescaling), "\n")
cat(" estimation after rescaling : ",f(model$timeEstPostscaling), "\n")
}
cat(" post-estimation : ",f(model$timePost),"\n")
if(!is.null(model$successfulEstimation) && !model$successfulEstimation) tmp <- paste0("(",model$message,")") else tmp <- ""
cat("Iterations : ",model$nIter, tmp, "\n")
if(!is.null(model$nIterPrescaling) && (model$nIterPrescaling>0 & model$nIterPostscaling>0)){
cat(" initial estimation : ",model$nIterPrescaling, "\n")
cat(" estimation after rescaling : ",model$nIterPostscaling, "\n")
}
if(model$bootstrapSE>0) cat("Number of bootstrap repetitions : ", model$bootstrapSE, "\n")
#if(!is.null(model$eigValue) && !anyNA(model$eigValue)){
# cat("Min abs eigenvalue of Hessian : ",round(min(abs(model$eigValue)),6),"\n")
# if(any(model$eigValue>0)) apollo_print("Some eigenvalues of Hessian are positive, indicating potential problems!")
#}
# Printing of constraints, if used
cat('\n')
if(is.null(model$constraints)) cat("Unconstrained optimisation.\n") else {
cat("Constrained optimisation:\n")
if( is.null(model$apollo_constraints)) cat(" Please refer to original model script to see the constraints.")
if(!is.null(model$apollo_constraints)) cat(paste0(" ", model$apollo_constraints), sep="\n")
cat("\n")
}; rm(test)
cat("\n")
if(!printClassical & anyNA(model$se[!(names(model$estimate) %in% model$apollo_fixed)]) ){
apollo_print('Classical standard errors could not be calculated for some parameters. This could point to an identification or estimation problem.', type="w")
}
if(scaling_used) apollo_print("These outputs have had the scaling used in estimation applied to them.")
cat("Estimates:\n")
if(nrow(output)>options("max.print")) options(max.print=nrow(output)+100)
if(!printFixed && length(model$apollo_fixed)>0) output <- output[!(rownames(output) %in% model$apollo_fixed),]
apollo_print(output) #print(output, digits=4)
cat('\n')
### Print diagnostics
# Model structure
if(!is.null(model$componentReport)) for(r in model$componentReport){
test <- is.list(r) && !is.null(r$param) && length(r$param)>0 && modelOutput_settings$printModelStructure
if(test) for(j in r$param) cat(j, '\n', sep='')
if(test) cat('\n')
}
if(!is.null(model$componentReport)) for(r in model$componentReport){
test <- is.list(r) && !is.null(r$data) && length(r$data)>0 && modelOutput_settings$printDataReport
if(test) for(j in r$data ) cat(j, '\n', sep='')
if(test) cat('\n')
}
#if(exists('r')) rm(r)
#if(exists('j')) rm(j)
### Fill shorter param names with spaces
longNames <- names( model$estimate )
if(length(model$apollo_fixed)>0) longNames <- longNames[-which(longNames %in% model$apollo_fixed)]
maxLen <- max(nchar(longNames))
for(i in 1:length(longNames)) longNames[i] <- paste0(paste0(rep(" ", maxLen-nchar(longNames[i])), collapse=""), longNames[i])
if(printCovar){
if(printClassical==TRUE){
cat("\n")
cat("Classical covariance matrix:\n")
tmp <- model$varcov
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
cat("\n")
cat("Robust covariance matrix:\n")
tmp <- model$robvarcov
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
if(model$bootstrapSE>0){
cat("\n")
cat("Bootstrap covariance matrix:\n")
tmp <- model$bootvarcov
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
}
if(printCorr){
if(printClassical==TRUE){
cat("\n")
cat("Classical correlation matrix:\n")
tmp <- model$corrmat
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
cat("\n")
cat("Robust correlation matrix:\n")
tmp <- model$robcorrmat
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
if(model$bootstrapSE>0){
cat("\n")
cat("Bootstrap correlation matrix:\n")
tmp <- model$bootcorrmat
colnames(tmp) <- longNames
apollo_print(tmp) #print(tmp, digits=4)
}
}
if(printOutliers>0){
outliers <- data.frame(ID=names(model$avgCP), avgChoiceProb=model$avgCP)
colnames(outliers) <- c("ID","Avg prob per choice")
if(!model$apollo_control$panelData) colnames(outliers) <- c("row", "Avg prob per choice")
outliers <- outliers[order(outliers[,2]),]
if(printOutliers==TRUE) printOutliers=20
printOutliers=floor(min(printOutliers,nrow(outliers)))
cat("\n",printOutliers,"most extreme outliers in terms of lowest average per choice prediction:\n")
print(outliers[(1:printOutliers),], row.names=FALSE)
}
if(printChange){
cat("\nChanges in parameter estimates from starting values:\n")
tmp <- cbind(model$apollo_beta, model$estimate[names(model$apollo_beta)],
model$estimate[names(model$apollo_beta)]-model$apollo_beta)
colnames(tmp) <- c("Initial", "Estimate", "Difference")
apollo_print(tmp)
}
if(printFunctions){
cat("\nSettings and functions used in model definition:\n")
cat("\napollo_control")
cat("\n--------------\n")
### change 27 July
##txt=t(data.frame(model$apollo_control))
tmp=model$apollo_control
tmp$cpp=NULL
##tmp$analyticGrad=NULL
tmp$matrixMult=NULL
tmp$subMaxV=NULL
txt=t(data.frame(tmp))
### end change 27 July
colnames(txt)="Value"
print(txt)
cat("\nHessian routines attempted")
cat("\n--------------------------\n")
cat(model$hessianMethodsAttempted)
cat("\n")
if(!is.null(model$scaling) && !all(model$scaling==1)){
cat("\nScaling in estimation")
cat("\n---------------------\n")
txt=as.matrix(model$scaling)
colnames(txt)="Value"
print(txt)}
if(!is.null(model$hessianScaling) && !all(model$hessianScaling==1)){
cat("\nScaling used in computing Hessian")
cat("\n---------------------------------\n")
txt=as.matrix(model$hessianScaling)
colnames(txt)="Value"
print(txt)}
if(is.function(model$apollo_randCoeff)){
cat("\n\napollo_randCoeff")
cat("\n------------------\n")
txt=capture.output(print(model$apollo_randCoeff))
#cat(txt[1:(length(txt)-1)],sep="\n")}
cat(txt, sep="\n")}
if(is.function(model$apollo_lcPars)){
cat("\n\napollo_lcPars")
cat("\n---------------\n")
txt=capture.output(print(model$apollo_lcPars))
#cat(txt[1:(length(txt)-1)],sep="\n")}
cat(txt, sep="\n")}
cat("\n\napollo_probabilities")
cat("\n----------------------\n")
txt=capture.output(print(model$apollo_probabilities))
#cat(txt[1:(length(txt)-1)],sep="\n")
cat(txt, sep="\n")
}
invisible(output)
}
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