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#######################################################################
# #
# Package: lcc #
# #
# File: lccSummary.R #
# Contains: lccSummary function #
# #
# Written by Thiago de Paula Oliveira #
# copyright (c) 2017-18, Thiago P. Oliveira #
# #
# First version: 11/10/2017 #
# Last update: 29/07/2019 #
# License: GNU General Public License version 2 (June, 1991) or later #
# #
#######################################################################
##' @title Internal Function to Summarize Fitted and Sampled Values for
##' \code{lcc} Objects
##'
##' @description This is an internally called function used to summarize
##' fitted and sampled values, and the concordance correlation
##' coefficient between them for \code{lcc} objects.
##'
##' @usage NULL
##' @return No return value, called for side effects
##' @author Thiago de Paula Oliveira, \email{thiago.paula.oliveira@@alumni.usp.br}
##'
##' @importFrom stats predict
##'
##' @keywords internal
lccSummary<-function(model, q_f, diffbeta, tk,
tk.plot, tk.plot2, rho, ENV.LCC,
rho.pearson, ENV.LPC, Cb, ENV.Cb,
ldb, ci, components){
if(components==FALSE){
if(ci==FALSE){
CCC<-CCC_lin(dataset=model$data, resp="resp", subject="subject",
method="method", time="time")
if(ldb==1){
comp <- paste0(levels(model$data$method)[2], " vs. ",
levels(model$data$method)[1])
LCC.data<-data.frame("Time"=tk.plot,"LCC"=rho)
CCC.data<-data.frame("Time" = tk.plot2, "CCC" = CCC)
colnames(CCC.data) <- c("Time", "CCC")
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=LCC.data,"sampled"=CCC.data,
"gof" = GF, "comp"=comp)
}else{
LCC.data<-list()
comp <- list()
for(i in 1:ldb) {
comp[[i]] <- paste0(levels(model$data$method)[i+1],
" vs. ", levels(model$data$method)[1])
LCC.data[[i]]<-data.frame("Time"=tk.plot,"LCC"=rho[[i]])
CCC.data<-data.frame("Time" = tk.plot2, "CCC" = CCC)
colnames(CCC.data) <- c("Time", "CCC")
}
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=LCC.data,"sampled"=CCC.data, "gof" = GF,
"comp"=comp)
}
}else{
if(ldb==1){
comp = paste0(levels(model$data$method)[2],
" vs. ", levels(model$data$method)[1])
CCC<-CCC_lin(dataset=model$data, resp="resp",
subject="subject", method="method", time="time")
LCC.data<-data.frame("Time"=tk.plot,"LCC"=rho,
"Lower"=ENV.LCC[1,], "Upper"=ENV.LCC[2,])
CCC.data<-data.frame("Time" = tk.plot2, "CCC" = CCC)
colnames(CCC.data) <- c("Time", "CCC")
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=LCC.data,"sampled"=CCC.data, "gof" = GF,
"comp" = comp)
}else{
CCC<-CCC_lin(dataset=model$data, resp="resp",
subject="subject", method="method", time="time")
LCC.data<-list()
comp <- list()
for(i in 1:ldb) {
comp[[i]] <- paste0(levels(model$data$method)[i+1],
" vs. ", levels(model$data$method)[1])
LCC.data[[i]]<-data.frame("Time" = tk.plot,"LCC"=rho[[i]],
"Lower" = ENV.LCC[[i]][1,],
"Upper" = ENV.LCC[[i]][2,])
CCC.data<-data.frame("Time" = tk.plot2, "CCC" = CCC)
colnames(CCC.data) <- c("Time", "CCC")
}
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=LCC.data,"sampled"=CCC.data,
"gof" = GF,"comp" = comp)
}
}
}else{
if(ci==FALSE){
CCC<-CCC_lin(dataset=model$data, resp="resp",
subject="subject", method="method", time="time")
Pearson<-Pearson(dataset=model$data, resp="resp",
subject="subject", method="method", time="time")
if(ldb==1){
comp <- paste0(levels(model$data$method)[2], " vs. ",
levels(model$data$method)[1])
LA <- CCC[[1]]/Pearson[[1]]
LCC.data<-data.frame("Time"=tk.plot,"LCC"=rho,
"LPC"=rho.pearson, "LA"=Cb)
CCC.data<-data.frame("Time" = tk.plot2, "CCC" = CCC,
"Pearson" = Pearson, "Cb" = LA)
colnames(CCC.data) <- c("Time", "CCC", "Pearson", "Cb")
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=LCC.data,"sampled"=CCC.data,
"gof" = GF, "comp"=comp)
}else{
LCC.data <- list()
CCC.data <- list()
LA <- list()
comp <- list()
for(i in 1:ldb) {
comp[[i]] <- paste0(levels(model$data$method)[i+1],
" vs. ", levels(model$data$method)[1])
LA[[i]] <- CCC[[i]]/Pearson[[i]]
LCC.data[[i]] <- data.frame("Time"=tk.plot,"LCC"=rho[[i]],
"LPC"=rho.pearson[[i]],
"LA"=Cb[[i]])
CCC.data[[i]]<-data.frame("Time" = tk.plot2, "CCC" = CCC[[i]],
"Pearson" = Pearson[[i]], "Cb" = LA[[i]])
colnames(CCC.data[[i]]) <- c("Time", "CCC", "Pearson", "Cb")
}
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=LCC.data,"sampled"=CCC.data,
"gof" = GF, "comp" = comp)
}
}else{
if(ldb==1){
CCC<-CCC_lin(dataset=model$data, resp="resp", subject="subject",
method="method", time="time")
Pearson<-Pearson(dataset=model$data, resp="resp",
subject="subject", method="method", time="time")
LA<-CCC[[1]]/Pearson[[1]]
comp <- paste0(levels(model$data$method)[2],
" vs. ", levels(model$data$method)[1])
LCC.data<-data.frame("Time"=tk.plot,"LCC"=rho,
"Lower"=ENV.LCC[1,], "Upper"=ENV.LCC[2,])
LPC.data<-data.frame("Time"=tk.plot,"LPC"=rho.pearson,
"Lower"=ENV.LPC[1,], "Upper"=ENV.LPC[2,])
LA.data<-data.frame("Time"=tk.plot,"LA"=Cb, "Lower"=ENV.Cb[1,],
"Upper"=ENV.Cb[2,])
CCC.data<-data.frame("Time" = tk.plot2, "CCC" = CCC,
"Pearson" = Pearson, "Cb" = LA)
colnames(CCC.data) <- c("Time", "CCC", "Pearson", "Cb")
fit<-list("LCC" = LCC.data, "LPC" = LPC.data, "LA" = LA.data)
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=fit,"sampled" = CCC.data,
"gof" = GF, "comp" = comp)
}else{
CCC<-CCC_lin(dataset=model$data, resp="resp", subject="subject",
method="method", time="time")
Pearson<-Pearson(dataset=model$data, resp="resp",
subject="subject", method="method",
time="time")
LA<-list()
CCC.data <- list()
LCC.data <- list()
LPC.data <- list()
LA.data <- list()
comp <- list()
for(i in 1:ldb) {
comp[[i]] <- paste0(levels(model$data$method)[i+1], " vs. ",
levels(model$data$method)[1])
LA[[i]]<-CCC[[i]]/Pearson[[i]]
LCC.data[[i]]<-data.frame("Time"=tk.plot,"LCC"=rho[[i]],
"Lower"=ENV.LCC[[i]][1,],
"Upper"=ENV.LCC[[i]][2,])
LPC.data[[i]]<-data.frame("Time"=tk.plot,
"LPC"=rho.pearson[[i]],
"Lower"=ENV.LPC[[i]][1,],
"Upper"=ENV.LPC[[i]][2,])
LA.data[[i]]<-data.frame("Time"=tk.plot,"LA"=Cb[[i]],
"Lower"=ENV.Cb[[i]][1,],
"Upper"=ENV.Cb[[i]][2,])
CCC.data[[i]]<-data.frame("Time" = tk.plot2, "CCC" = CCC[[i]],
"Pearson" = Pearson[[i]], "LA" = LA[[i]])
colnames(CCC.data[[i]]) <- c("Time", "CCC", "Pearson", "Cb")
}
fit<-list("LCC" = LCC.data, "LPC" = LPC.data, "LA" = LA.data)
GF<-CCC(predict(model), model$data$resp)
plot.data<-list("fitted"=fit, "sampled" = CCC.data,
"gof" = GF, "comp" = comp)
}
}
}
return(invisible(plot.data))
}
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