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### summary.calibrateType1.R ---
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: apr 23 2018 (12:58)
## Version:
## Last-Updated: Jan 11 2022 (16:48)
## By: Brice Ozenne
## Update #: 72
##----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
##----------------------------------------------------------------------
##
### Code:
## * method summary.modelsearch2
#' @title Display the Type 1 Error Rate
#' @description Display the type 1 error rate from the simulation results.
#'
#' @param object output of the \code{calibrateType1} function.
#' @param alpha [numeric, 0-1] the confidence levels.
#' @param robust [character] should the results be displayed for both model-based and robust standard errors (\code{TRUE}),
#' only model-based standard error (\code{FALSE}), or only robust standard error (\code{"only"})?
#' @param type [character] should the type 1 error rate be diplayed (\code{"type1error"}) or the bias (\code{"bias")}.
#' @param digits [integer >0] the number of decimal places to use when displaying the summary.
#' @param log.transform [logical] should the confidence intervals be computed on the logit scale.
#' @param print should the summary be printed in the terminal.
#' @param ... [internal] only used by the generic method.
#'
#' @method summary calibrateType1
#' @export
summary.calibrateType1 <- function(object, robust = FALSE, type = "type1error",
alpha = 0.05, log.transform = TRUE,
digits = 5, print = TRUE, ...){
type <- match.arg(type, c("type1error","bias"))
if(is.logical(robust)){robust <- as.character(robust)}
robust <- match.arg(robust, c("TRUE","FALSE","only"))
## ** compute bias
if(type == "bias"){
stop("not implemented yet!")
}
## ** compute type 1 error
if(type == "type1error"){
dfLong <- melt(object$p.value,
measure.vars = grep("^p.",names(object$p.value),value = TRUE),
value.name = "p.value",
variable.name = "method")
dfS <- stats::aggregate(dfLong$p.value,
by = list(n = dfLong$n, method = dfLong$method, link = dfLong$link),
FUN = function(x){c(n.rep = length(x), type1error = mean(x<=alpha, na.rm = TRUE))},
simplify = FALSE)
dfS <- cbind(dfS[,c("n","method","link")],
do.call(rbind,dfS[,"x"]))
## binom::binom.confint(x = round(dfS$type1error*dfS$n.rep), dfS$n.rep, method = "logit")
logit.p <- log(dfS$type1error/(1-dfS$type1error))
if(log.transform){
## delta method: ln(x/(1-x))' = ln(x)' - ln(1-x)' = 1/x + 1/(1-x) = 1/(x(1-x))
var.logit.p <- 1/(dfS$n.rep*dfS$type1error*(1-dfS$type1error))
## on logit scale
dfS$ci.inf <- logit.p + stats::qnorm(0.025) * sqrt(var.logit.p)
dfS$ci.sup <- logit.p + stats::qnorm(0.975) * sqrt(var.logit.p)
## on original scale
dfS$ci.inf <- 1/(1+exp(-dfS$ci.inf))
dfS$ci.sup <- 1/(1+exp(-dfS$ci.sup))
}else{
dfS$ci.inf <- dfS$type1error + stats::qnorm(0.025) * sqrt(dfS$type1error*(1-dfS$type1error)/dfS$n.rep)
dfS$ci.sup <- dfS$type1error + stats::qnorm(0.975) * sqrt(dfS$type1error*(1-dfS$type1error)/dfS$n.rep)
}
## names
rownames(dfS) <- NULL
dfS$correction <- sapply(as.character(dfS$method),switch,
p.Ztest = "Gaussian approx.",
p.Satt = "Satterthwaite approx.",
p.SSC = "small sample correction",
p.KR = "Satterthwaite approx. with small sample correction",
p.robustZtest = "Gaussian approx.",
p.robustSatt = "Satterthwaite approx.",
p.robustSSC = "small sample correction",
p.robustKR = "Satterthwaite approx. with small sample correction")
dfS$statistic <- sapply(as.character(dfS$method),switch,
p.Ztest = "Wald",
p.Satt = "Wald",
p.SSC = "Wald",
p.KR = "Wald",
p.robustZtest = "robust Wald",
p.robustSatt = "robust Wald",
p.robustSSC = "robust Wald",
p.robustKR = "robust Wald")
## display
if(print){
seqN <- unique(dfS$n)
seqRep <- stats::setNames(dfS$n.rep[duplicated(dfS$n) == FALSE],seqN)
vecTrans <- c("Gaus" = "Gaussian approx.",
"Satt" = "Satterthwaite approx.",
"SSC" = "small sample correction",
"SSC + Satt" = "Satterthwaite approx. with small sample correction")
ls.print <- lapply(seqN, function(iN){ # iN <- 73
df.tempo <- dfS[dfS$n==iN, c("link","statistic","correction","type1error","ci.inf","ci.sup")]
if(!is.null(digits)){
df.tempo$type1error <- round(df.tempo$type1error, digits = digits)
df.tempo$ci.inf <- round(df.tempo$ci.inf, digits = digits)
df.tempo$ci.sup <- round(df.tempo$ci.sup, digits = digits)
}
df.tempo$CI <- paste0("[",df.tempo$ci.inf," ; ",df.tempo$ci.sup,"]")
df.tempo$ci.inf <- NULL
df.tempo$ci.sup <- NULL
if(robust == "FALSE"){
df.tempo <- df.tempo[df.tempo$statistic=="Wald",,drop=FALSE]
}else if(robust == "only"){
df.tempo <- df.tempo[df.tempo$statistic=="robust Wald",,drop=FALSE]
}
df.tempo <- df.tempo[order(df.tempo$link,df.tempo$statistic),,drop=FALSE]
df.tempo$statistic[duplicated(cbind(df.tempo$link,df.tempo$statistic))] <- ""
df.tempo$link[duplicated(df.tempo$link)] <- ""
rownames(df.tempo) <- NULL
df.tempo$correction <- factor(df.tempo$correction,
levels = as.character(vecTrans),
labels = names(vecTrans))
## df.tempo$correction <- NULL
return(df.tempo)
})
names(ls.print) <- as.character(seqN)
cat("Estimated type 1 error rate [95% confidence interval] \n")
lapply(seqN, function(iN){
cat(" > sample size: ",iN," | number of simulations: ",seqRep[as.character(iN)],"\n",sep="")
print(ls.print[[as.character(iN)]],row.names = FALSE)
})
cat("\n")
cat("Corrections: Gaus = Gaussian approximation \n",
" SSC = small sample correction \n",
" Satt = Satterthwaite approximation \n",sep="")
}
}
return(invisible(dfS))
}
######################################################################
### summary.calibrateType1.R ends here
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