R/extractAlignmentSim.R
In MaksimRudnev/MIE.package: Measurement Invariance Explorer (MIE)
Defines functions
getSavedParams
extractParameters
extractAlignmentSim
Documented in
extractAlignmentSim
#' Extracts summaries of alignment simulations from Mplus output file
#'
#' @param sim.outputs a character vector of file names containing simulation results of alignment ran in Mplus.
#' @param silent Logical. Used for debugging.
#' @details Best used as part of \code{\link[MIE]{runAlignment}}
#'
#' @examples
#' \dontrun{
#' align.sim.summ <- extractAlignmentSim (c("sim500.out", "sim100.out", "sim1000.out"))
#' }
#'
#' @return Invisibly returns a summary table.
#' @seealso \code{\link[MIE]{runAlignment}} and \code{\link[MIE]{extractAlignment}}
#' @export
extractAlignmentSim <- function(sim.outputs = c("sim500.out", "sim100.out", "sim1000.out"), silent = FALSE, manual = F) {
otp <- lapply(sim.outputs, function(x) {
f <- paste(readLines(x), collapse="\n")
mplus.version <- sub(".*Mplus VERSION *(.*?) *\n.*", "\\1", f)
mplus.version <- trimws(gsub("(\\(.*\\))", "", mplus.version))
if(!grepl("CORRELATIONS AND MEAN SQUARE ERROR OF POPULATION AND ESTIMATE VALUES", f) & !manual) {
#stop("Correlations output is not found.")
message("Correlations output is not found. Calculating correlations manually")
manual <- TRUE
}
if(manual) {
# extract parameters and correlate averages
partable <- MIE:::extractParameters(string = f)
partable.means <- partable[partable$L2 == "Means",c("Population", "parameter", "ESTIMATES_Average")]
corr.of.averages = sapply(setNames(nm=unique(partable.means$parameter)), function(latent.factor)
cor(partable.means$ESTIMATES_Average[partable.means$parameter == latent.factor],
partable.means$Population[partable.means$parameter == latent.factor]))
corr.of.averages.N = length(unique(partable$L1))
# extract each replication mean, correlate to population value, and then average
params.for.each.replication <- MIE:::getSavedParams(x)
means.for.each.replication <- lapply(params.for.each.replication, function(y) dplyr::filter(y, L2 == "alpha"))
#reshape2::melt(means.for.each.replication, level = "L1")
pop.values = partable[partable$L2=="Means" & !grepl("#",partable$parameter),
c("L1", "parameter", "Population")]
pop.values$L1 <- gsub("\\s", ".", toupper(gsub("Group ", "", pop.values$L1)))
merged.dats = lapply(means.for.each.replication, function(x) {
merge(x, pop.values,
by.x = c("L1", "Var2"),
by.y = c("L1", "parameter"))
})
cors.df = sapply(merged.dats,
function(y)
sapply(setNames(nm=unique(y$Var2)),
function(latent.factor)
cor(y$est[y$Var2 == latent.factor],
y$Population[y$Var2 == latent.factor])
)
)
# output
list(
"Correlations and mean square error of population and estimate values" =
list("Correlations Mean Average"={ if(is.data.frame(cors.df))
apply(cors.df, 1, mean) else mean(cors.df)},
"Correlations Variance Average"=NA,
"Correlations Mean SD" = { if(is.data.frame(cors.df))
apply(cors.df, 1, sd) else sd(cors.df)},
"Correlations Variance SD" = NA,
"MSE Mean Average" = NA,
"MSE Variance Average"= NA,
"MSE Mean SD" = NA,
"MSE Variance SD" = NA,
N = ncol(cors.df)),
"Correlation and mean square error of the average estimates" =
list("Correlation of average means with true" = corr.of.averages,
"Correlation of average variances with true" = NA,
"MSE of average means with true" = NA,
"MSE of average variances with true" = NA,
N = corr.of.averages.N
)
)
} else {
if(mplus.version == "8.10") {
cor.tab1 = sub(".*CORRELATIONS AND MEAN SQUARE ERROR OF POPULATION AND ESTIMATE VALUES(.*?)\n\n\n.*", "\\1", f)
cor.tab2 = sub(".*CORRELATION AND MEAN SQUARE ERROR OF THE AVERAGE ESTIMATES(.*?)\n\n\n.*", "\\1", f)
cor.tabs = list(cor.tab1, cor.tab2)
} else {
cor.tabs <- sub(".*CORRELATIONS AND MEAN SQUARE ERROR OF POPULATION AND ESTIMATE VALUES *(.*?)*\n\n\n\n.*", "\\1", f)
cor.tabs <- strsplit(cor.tabs, "CORRELATION AND MEAN SQUARE ERROR OF THE AVERAGE ESTIMATES")[[1]]
}
cor.tabs1 <- strsplit(cor.tabs[[1]], "\n")[[1]][-c(1:4)]
cor.tabs1 <- cor.tabs1[!cor.tabs1==""]
dt1 <-sapply(seq(1, length(cor.tabs1), by=3), function(f.id) unlist(read.table(text=cor.tabs1[f.id+1:2])[-1]))
colnames(dt1) = gsub(" ", "", cor.tabs1[seq(1, length(cor.tabs1), by=3)])
row.names(dt1)<-c("Correlations Mean Average", "Correlations Variance Average", "Correlations Mean SD", "Correlations Variance SD",
"MSE Mean Average", "MSE Variance Average", "MSE Mean SD", "MSE Variance SD")
cor.tabs2 <- strsplit(cor.tabs[[2]], "\n")[[1]]
cor.tabs2 <- cor.tabs2[!cor.tabs2==""]
dt2 <- read.table(text=gsub(".*(Mean|Variance) *", "", cor.tabs2))
dt2 <- sapply(seq(1, nrow(dt2), by=2), function(y) unlist(dt2[y:(y+1),]) )
colnames(dt2) <- gsub(" *", "", gsub("(Mean|Variance).* *", "", cor.tabs2)) [seq(1, length(cor.tabs2), by=2)]
rownames(dt2) <- c("Correlation of average means with true", "Correlation of average variances with true",
"MSE of average means with true","MSE of average variances with true")
list('Correlations and mean square error of population and estimate values' = dt1,
'Correlation and mean square error of the average estimates' = dt2)
}})
names(otp)<- sim.outputs
if(!silent) {
for(i in names(otp) ) {
cat("\n", "⎯ Output: ", i," ⎯ ")
cat("\n", "⎯⎯⎯= ",
names(otp[[i]])[[1]],
" =⎯ \n")
print(t(as.data.frame(otp[[i]][[1]])), digits = 2)
cat("\n", "⎯⎯⎯= ",
names(otp[[i]])[[2]],
" =⎯ \n")
print(otp[[i]][[2]], digits = 2)
}
}
invisible(otp)
}
extractParameters <- function(file = NULL, string = NULL) {
if(!is.null(file)) {
if(file.exists(file))
string <- paste(readLines(file), collapse="\n")
else
stop("File not found.")
}
# Reading parameter table
all.pars.string <- sub(".*MODEL RESULTS *(.*?) *QUALITY OF NUMERICAL RESULTS.*", "\\1", string)
all.pars.split <- strsplit(all.pars.string, "\n")[[1]]
#all.pars.split <- gsub("\\s+", " ", all.pars.split)
all.pars.split <- all.pars.split[nchar(all.pars.split)!=0]
# identifying sections
full.string.length = max(nchar(all.pars.split))
headers.ind = which(nchar(all.pars.split)!=full.string.length)
headers1.ind = headers.ind[c(headers.ind[-1] - headers.ind[-length(headers.ind)] == 1, F)]
headers2.ind = headers.ind[!headers.ind %in% headers1.ind]
# trimming space from sections names
all.pars.split[headers1.ind] <- trimws(gsub("\\s+", " ", all.pars.split[headers1.ind]))
all.pars.split[headers2.ind] <- trimws(gsub("\\s+", " ", all.pars.split[headers2.ind]))
# detecting the varnames (header of the partable)
varnames.string <- sub(".*\n\n(.*?) *\n\n.*", "\\1", all.pars.string)
if(grepl("\n", varnames.string)) {
twoliner = read.fwf(textConnection(varnames.string),
widths = c(17, 11, 11, 11, 11, 11, 6, 11))
varnames.vector <- trimws(paste(twoliner[1,], twoliner[2,]))
varnames.vector[[1]]<-"parameter"
} else {
varnames.vector <- strsplit(varnames.string, split = "\\s+{2}")[[1]]
varnames.vector[[1]]<-"parameter"
}
varnames.vector <- gsub("\\s+", "_", varnames.vector)
# making a list of parameters
all.pars.list <- list()
for(i in 1:length(headers1.ind)) {
begin.section = headers1.ind[i]+1
end.section = ifelse(i == length(headers1.ind),
length(all.pars.split),
headers1.ind[i+1]-1)
section = all.pars.split[begin.section:end.section]
headers2.in.section = headers2.ind[headers2.ind >= begin.section &
headers2.ind <= end.section]
subsection <- list()
for(k in 1:length(headers2.in.section)) {
begin.subsection = headers2.in.section[k]+1
end.subsection = ifelse(k == length(headers2.in.section),
end.section,
headers2.in.section[k+1]-1)
subsection[[ all.pars.split [[ headers2.in.section[[k]] ]] ]] <-
read.table(text = all.pars.split[begin.subsection:end.subsection],
col.names = varnames.vector, comment.char = "!")
}
all.pars.list[[all.pars.split[[headers1.ind[[i]]]]]] <- subsection
}
# list to partable
partable <- reshape2::melt(all.pars.list, id.vars = "parameter") %>%
reshape2::dcast(L1 + L2 + parameter ~ variable, value.var = "value")
return(partable)
}
getSavedParams <- function(file) {
simpar <- MplusAutomation::readModels(file, what = c("tech1", "output"))
tech1 <- simpar$tech1$parameterSpecification
tech1.df <- reshape2::melt(tech1) %>% dplyr::filter(!is.na(value)) %>% dplyr::filter(value!=0)
results.file <- sub(".*RESULTS SAVING INFORMATION(.*?) *Save file format.*", "\\1",
paste(simpar$output, collapse = "\n"))
results.file <- trimws(sub(".*Save file\n(.*?) *\n\n*", "\\1", results.file))
results.file <- ifelse(dirname(file)==".", results.file, paste0(dirname(file), "/", results.file))
if(file.exists(results.file)) {
results.string <- readLines(results.file)
} else {
warning("Results file was not found. Make sure you added 'RESULTS = filename.dat;' in the 'MONTECARLO:' section of the input file.")
}
replication.id.index <- which(!grepl("\\s", results.string))
parameter.vectors <-
lapply(1:length(replication.id.index), function(i) {
begin = replication.id.index[[i]] + 1
end = ifelse(i == length(replication.id.index),
length(results.string),
replication.id.index[[i + 1]]-1)
paste(results.string[begin:end], collapse = " ")
})
names(parameter.vectors) = results.string[replication.id.index]
# # find the location of ALPHA matrix in the technical output
# tech1.output <- substr(f, regexec("TECHNICAL OUTPUT", f)[[1]]+16, regexec("STARTING VALUES", f)[[1]])
#
# if(grepl("THE ROTATED SOLUTION", f)) {
# # tech1.output <- substr(f,
# # regexec("TECHNICAL 1 OUTPUT FOR THE ROTATED SOLUTION", f)[[1]]+16,
# # gregexec("STARTING VALUES", f)[[1]])
#
# tech1.output <- sub(".*TECHNICAL 1 OUTPUT FOR THE ROTATED SOLUTION(.*?) *STARTING VALUES.*", "\\1", f)
# }
#
# param.spec.output <- strsplit(tech1.output, "PARAMETER SPECIFICATION FOR")[[1]][-1]
# names(param.spec.output) <- unname(trimws(sapply(param.spec.output, function(x) strsplit(x, "\n\n\n")[[1]][[1]])))
#
# alpha.output <- lapply(1:length(param.spec.output), function(y) {
# y = param.spec.output[[y]]
# alpha.string = sub(".*ALPHA(.*?)\n\n.*", "\\1", y)
# alpha.table = read.table(text = alpha.string, header = T, comment.char = "#")
# alpha.table[-1,]
# })
# alpha.index <- reshape2::melt(alpha.output, level = "group", id.vars = NULL)
# means.for.each.replication =
# lapply(parameter.vectors, function(y) {
# #suppressMessages({
# param.vector = scan(text = y, what = numeric(), quiet = T)
# #})
# cbind(alpha.index, est = param.vector[as.numeric(alpha.index$value)])
# })
params.for.each.replication =
lapply(parameter.vectors, function(y) {
#suppressMessages({
param.vector = scan(text = y, what = numeric(), quiet = T)
#})
# cbind(alpha.index, est = param.vector[as.numeric(alpha.index$value)])
cbind(tech1.df,
est = param.vector[as.numeric(tech1.df$value)],
se = param.vector[as.numeric(tech1.df$value)+max(as.numeric(tech1.df$value))])
})
return(params.for.each.replication)
}
MaksimRudnev/MIE.package documentation built on March 5, 2024, 1:24 p.m.
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Defines functions getSavedParams extractParameters extractAlignmentSim
Documented in extractAlignmentSim
#' Extracts summaries of alignment simulations from Mplus output file
#'
#' @param sim.outputs a character vector of file names containing simulation results of alignment ran in Mplus.
#' @param silent Logical. Used for debugging.
#' @details Best used as part of \code{\link[MIE]{runAlignment}}
#'
#' @examples
#' \dontrun{
#' align.sim.summ <- extractAlignmentSim (c("sim500.out", "sim100.out", "sim1000.out"))
#' }
#'
#' @return Invisibly returns a summary table.
#' @seealso \code{\link[MIE]{runAlignment}} and \code{\link[MIE]{extractAlignment}}
#' @export
extractAlignmentSim <- function(sim.outputs = c("sim500.out", "sim100.out", "sim1000.out"), silent = FALSE, manual = F) {
otp <- lapply(sim.outputs, function(x) {
f <- paste(readLines(x), collapse="\n")
mplus.version <- sub(".*Mplus VERSION *(.*?) *\n.*", "\\1", f)
mplus.version <- trimws(gsub("(\\(.*\\))", "", mplus.version))
if(!grepl("CORRELATIONS AND MEAN SQUARE ERROR OF POPULATION AND ESTIMATE VALUES", f) & !manual) {
#stop("Correlations output is not found.")
message("Correlations output is not found. Calculating correlations manually")
manual <- TRUE
}
if(manual) {
# extract parameters and correlate averages
partable <- MIE:::extractParameters(string = f)
partable.means <- partable[partable$L2 == "Means",c("Population", "parameter", "ESTIMATES_Average")]
corr.of.averages = sapply(setNames(nm=unique(partable.means$parameter)), function(latent.factor)
cor(partable.means$ESTIMATES_Average[partable.means$parameter == latent.factor],
partable.means$Population[partable.means$parameter == latent.factor]))
corr.of.averages.N = length(unique(partable$L1))
# extract each replication mean, correlate to population value, and then average
params.for.each.replication <- MIE:::getSavedParams(x)
means.for.each.replication <- lapply(params.for.each.replication, function(y) dplyr::filter(y, L2 == "alpha"))
#reshape2::melt(means.for.each.replication, level = "L1")
pop.values = partable[partable$L2=="Means" & !grepl("#",partable$parameter),
c("L1", "parameter", "Population")]
pop.values$L1 <- gsub("\\s", ".", toupper(gsub("Group ", "", pop.values$L1)))
merged.dats = lapply(means.for.each.replication, function(x) {
merge(x, pop.values,
by.x = c("L1", "Var2"),
by.y = c("L1", "parameter"))
})
cors.df = sapply(merged.dats,
function(y)
sapply(setNames(nm=unique(y$Var2)),
function(latent.factor)
cor(y$est[y$Var2 == latent.factor],
y$Population[y$Var2 == latent.factor])
)
)
# output
list(
"Correlations and mean square error of population and estimate values" =
list("Correlations Mean Average"={ if(is.data.frame(cors.df))
apply(cors.df, 1, mean) else mean(cors.df)},
"Correlations Variance Average"=NA,
"Correlations Mean SD" = { if(is.data.frame(cors.df))
apply(cors.df, 1, sd) else sd(cors.df)},
"Correlations Variance SD" = NA,
"MSE Mean Average" = NA,
"MSE Variance Average"= NA,
"MSE Mean SD" = NA,
"MSE Variance SD" = NA,
N = ncol(cors.df)),
"Correlation and mean square error of the average estimates" =
list("Correlation of average means with true" = corr.of.averages,
"Correlation of average variances with true" = NA,
"MSE of average means with true" = NA,
"MSE of average variances with true" = NA,
N = corr.of.averages.N
)
)
} else {
if(mplus.version == "8.10") {
cor.tab1 = sub(".*CORRELATIONS AND MEAN SQUARE ERROR OF POPULATION AND ESTIMATE VALUES(.*?)\n\n\n.*", "\\1", f)
cor.tab2 = sub(".*CORRELATION AND MEAN SQUARE ERROR OF THE AVERAGE ESTIMATES(.*?)\n\n\n.*", "\\1", f)
cor.tabs = list(cor.tab1, cor.tab2)
} else {
cor.tabs <- sub(".*CORRELATIONS AND MEAN SQUARE ERROR OF POPULATION AND ESTIMATE VALUES *(.*?)*\n\n\n\n.*", "\\1", f)
cor.tabs <- strsplit(cor.tabs, "CORRELATION AND MEAN SQUARE ERROR OF THE AVERAGE ESTIMATES")[[1]]
}
cor.tabs1 <- strsplit(cor.tabs[[1]], "\n")[[1]][-c(1:4)]
cor.tabs1 <- cor.tabs1[!cor.tabs1==""]
dt1 <-sapply(seq(1, length(cor.tabs1), by=3), function(f.id) unlist(read.table(text=cor.tabs1[f.id+1:2])[-1]))
colnames(dt1) = gsub(" ", "", cor.tabs1[seq(1, length(cor.tabs1), by=3)])
row.names(dt1)<-c("Correlations Mean Average", "Correlations Variance Average", "Correlations Mean SD", "Correlations Variance SD",
"MSE Mean Average", "MSE Variance Average", "MSE Mean SD", "MSE Variance SD")
cor.tabs2 <- strsplit(cor.tabs[[2]], "\n")[[1]]
cor.tabs2 <- cor.tabs2[!cor.tabs2==""]
dt2 <- read.table(text=gsub(".*(Mean|Variance) *", "", cor.tabs2))
dt2 <- sapply(seq(1, nrow(dt2), by=2), function(y) unlist(dt2[y:(y+1),]) )
colnames(dt2) <- gsub(" *", "", gsub("(Mean|Variance).* *", "", cor.tabs2)) [seq(1, length(cor.tabs2), by=2)]
rownames(dt2) <- c("Correlation of average means with true", "Correlation of average variances with true",
"MSE of average means with true","MSE of average variances with true")
list('Correlations and mean square error of population and estimate values' = dt1,
'Correlation and mean square error of the average estimates' = dt2)
}})
names(otp)<- sim.outputs
if(!silent) {
for(i in names(otp) ) {
cat("\n", "⎯ Output: ", i," ⎯ ")
cat("\n", "⎯⎯⎯= ",
names(otp[[i]])[[1]],
" =⎯ \n")
print(t(as.data.frame(otp[[i]][[1]])), digits = 2)
cat("\n", "⎯⎯⎯= ",
names(otp[[i]])[[2]],
" =⎯ \n")
print(otp[[i]][[2]], digits = 2)
}
}
invisible(otp)
}
extractParameters <- function(file = NULL, string = NULL) {
if(!is.null(file)) {
if(file.exists(file))
string <- paste(readLines(file), collapse="\n")
else
stop("File not found.")
}
# Reading parameter table
all.pars.string <- sub(".*MODEL RESULTS *(.*?) *QUALITY OF NUMERICAL RESULTS.*", "\\1", string)
all.pars.split <- strsplit(all.pars.string, "\n")[[1]]
#all.pars.split <- gsub("\\s+", " ", all.pars.split)
all.pars.split <- all.pars.split[nchar(all.pars.split)!=0]
# identifying sections
full.string.length = max(nchar(all.pars.split))
headers.ind = which(nchar(all.pars.split)!=full.string.length)
headers1.ind = headers.ind[c(headers.ind[-1] - headers.ind[-length(headers.ind)] == 1, F)]
headers2.ind = headers.ind[!headers.ind %in% headers1.ind]
# trimming space from sections names
all.pars.split[headers1.ind] <- trimws(gsub("\\s+", " ", all.pars.split[headers1.ind]))
all.pars.split[headers2.ind] <- trimws(gsub("\\s+", " ", all.pars.split[headers2.ind]))
# detecting the varnames (header of the partable)
varnames.string <- sub(".*\n\n(.*?) *\n\n.*", "\\1", all.pars.string)
if(grepl("\n", varnames.string)) {
twoliner = read.fwf(textConnection(varnames.string),
widths = c(17, 11, 11, 11, 11, 11, 6, 11))
varnames.vector <- trimws(paste(twoliner[1,], twoliner[2,]))
varnames.vector[[1]]<-"parameter"
} else {
varnames.vector <- strsplit(varnames.string, split = "\\s+{2}")[[1]]
varnames.vector[[1]]<-"parameter"
}
varnames.vector <- gsub("\\s+", "_", varnames.vector)
# making a list of parameters
all.pars.list <- list()
for(i in 1:length(headers1.ind)) {
begin.section = headers1.ind[i]+1
end.section = ifelse(i == length(headers1.ind),
length(all.pars.split),
headers1.ind[i+1]-1)
section = all.pars.split[begin.section:end.section]
headers2.in.section = headers2.ind[headers2.ind >= begin.section &
headers2.ind <= end.section]
subsection <- list()
for(k in 1:length(headers2.in.section)) {
begin.subsection = headers2.in.section[k]+1
end.subsection = ifelse(k == length(headers2.in.section),
end.section,
headers2.in.section[k+1]-1)
subsection[[ all.pars.split [[ headers2.in.section[[k]] ]] ]] <-
read.table(text = all.pars.split[begin.subsection:end.subsection],
col.names = varnames.vector, comment.char = "!")
}
all.pars.list[[all.pars.split[[headers1.ind[[i]]]]]] <- subsection
}
# list to partable
partable <- reshape2::melt(all.pars.list, id.vars = "parameter") %>%
reshape2::dcast(L1 + L2 + parameter ~ variable, value.var = "value")
return(partable)
}
getSavedParams <- function(file) {
simpar <- MplusAutomation::readModels(file, what = c("tech1", "output"))
tech1 <- simpar$tech1$parameterSpecification
tech1.df <- reshape2::melt(tech1) %>% dplyr::filter(!is.na(value)) %>% dplyr::filter(value!=0)
results.file <- sub(".*RESULTS SAVING INFORMATION(.*?) *Save file format.*", "\\1",
paste(simpar$output, collapse = "\n"))
results.file <- trimws(sub(".*Save file\n(.*?) *\n\n*", "\\1", results.file))
results.file <- ifelse(dirname(file)==".", results.file, paste0(dirname(file), "/", results.file))
if(file.exists(results.file)) {
results.string <- readLines(results.file)
} else {
warning("Results file was not found. Make sure you added 'RESULTS = filename.dat;' in the 'MONTECARLO:' section of the input file.")
}
replication.id.index <- which(!grepl("\\s", results.string))
parameter.vectors <-
lapply(1:length(replication.id.index), function(i) {
begin = replication.id.index[[i]] + 1
end = ifelse(i == length(replication.id.index),
length(results.string),
replication.id.index[[i + 1]]-1)
paste(results.string[begin:end], collapse = " ")
})
names(parameter.vectors) = results.string[replication.id.index]
# # find the location of ALPHA matrix in the technical output
# tech1.output <- substr(f, regexec("TECHNICAL OUTPUT", f)[[1]]+16, regexec("STARTING VALUES", f)[[1]])
#
# if(grepl("THE ROTATED SOLUTION", f)) {
# # tech1.output <- substr(f,
# # regexec("TECHNICAL 1 OUTPUT FOR THE ROTATED SOLUTION", f)[[1]]+16,
# # gregexec("STARTING VALUES", f)[[1]])
#
# tech1.output <- sub(".*TECHNICAL 1 OUTPUT FOR THE ROTATED SOLUTION(.*?) *STARTING VALUES.*", "\\1", f)
# }
#
# param.spec.output <- strsplit(tech1.output, "PARAMETER SPECIFICATION FOR")[[1]][-1]
# names(param.spec.output) <- unname(trimws(sapply(param.spec.output, function(x) strsplit(x, "\n\n\n")[[1]][[1]])))
#
# alpha.output <- lapply(1:length(param.spec.output), function(y) {
# y = param.spec.output[[y]]
# alpha.string = sub(".*ALPHA(.*?)\n\n.*", "\\1", y)
# alpha.table = read.table(text = alpha.string, header = T, comment.char = "#")
# alpha.table[-1,]
# })
# alpha.index <- reshape2::melt(alpha.output, level = "group", id.vars = NULL)
# means.for.each.replication =
# lapply(parameter.vectors, function(y) {
# #suppressMessages({
# param.vector = scan(text = y, what = numeric(), quiet = T)
# #})
# cbind(alpha.index, est = param.vector[as.numeric(alpha.index$value)])
# })
params.for.each.replication =
lapply(parameter.vectors, function(y) {
#suppressMessages({
param.vector = scan(text = y, what = numeric(), quiet = T)
#})
# cbind(alpha.index, est = param.vector[as.numeric(alpha.index$value)])
cbind(tech1.df,
est = param.vector[as.numeric(tech1.df$value)],
se = param.vector[as.numeric(tech1.df$value)+max(as.numeric(tech1.df$value))])
})
return(params.for.each.replication)
}
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