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6.RUNlca.R
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library(poLCA)
library(MplusAutomation)
ds_lc0 <- ISC_lvR %>%
dplyr::select(all_of(Id), all_of(sampleID), all_of(Scales), all_of(Scalesb), all_of(Man_cate), all_of(Man_cont))%>%
mutate(group = factor(paste0(COUNTRY,as.numeric(factor(ISC_lvR$cycle)))),
grgnd = factor(ifelse(!is.na(T_GENDER), paste0(COUNTRY,as.numeric(factor(ISC_lvR$cycle)),T_GENDER), NA)))
ds_lc0$IDSTUD <- as.numeric(ds_lc0$IDSTUD)
remlabclass <- function(ces){
for (each in colnames(ces)){
if ("labelled" %in% class(ces[[each]])){
class(ces[[each]]) = c("numeric")
attr(ces[[each]], "levels") <- NULL
}
attr(ces[[each]], "label") <- NULL
}
return(ces)
}
ds_lc0 <- remlabclass(ds_lc0)
Man_cateSignf <- Man_cate[!grepl(paste0(c("T_HIGHEDFA", "T_HISCED"), collapse = "|"), Man_cate)]
Man_contSignf <- Man_cont[!grepl(paste0(c("T_CITRESP", "T_NISB", "T_PROTES"), collapse = "|"), Man_cont)]
#Selection of variables to be used in models for availability
Man_cateC2C3 <- Man_cateSignf[!grepl(paste0(c("T_HIGHEDFA", "T_HISCED"), collapse = "|"), Man_cateSignf)]
Man_contC2C3 <- Man_contSignf[!grepl(paste0(c("T_NISB", "T_CITRESP", "T_PROTES"), collapse = "|"), Man_contSignf)]
#Special selection for cycle 1 for availability
Man_cateC1 <- Man_cateSignf[!grepl(paste0(c("T_HIGHEDEXP", "T_RELIG", "T_HISCED", "T_PROTES1"), collapse = "|"), Man_cateSignf)]
Man_contC1 <- Man_contSignf[!grepl(paste0(c("T_HISEI", "T_NISB", "T_PROTES", "T_CNTATT", "T_ELECPART", "T_LEGACT", "T_WIDEPART", "T_CITRESP"), collapse = "|"), Man_contSignf)]
Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] #Using only scales that are in all cycles
Scalesbc <- Scalesb[!grepl(paste0(c("bT_GNDREQ7", "bT_ETHNEQ5"), collapse = "|"), Scalesb)] #Using only scales that are in all cycles
#------------------------------------------------------------
#-----------------------Mplus code---------------------------
#------------------------------------------------------------
nam<-as.matrix(table(ds_lc0$group))
num<-data.frame(N=as.matrix(table(as.numeric(ds_lc0$group))))
num$nam <- rownames(nam)
num$c <- "!"
num$num <- rownames(num)
num[,c(3,2,1)]
dataAll <- ds_lc0 %>%
dplyr::select(all_of(sampleID), all_of(Scalesc), IDSTUD, group, grgnd) %>%
mutate_if(is.factor, ~ as.numeric(.x)) %>%
rename_with(~ gsub("T_ETHNEQ", "ETH", .x, fixed = TRUE)) %>%
rename_with(~ gsub("T_GNDREQ", "GND", .x, fixed = TRUE)) %>%
rename_with(~ gsub("T_IMMIEQ", "IMM", .x, fixed = TRUE)) %>%
data.frame()
prepareMplusData(df = dataAll,
filename = paste0("Mplus/Dta.dat"),interactive =FALSE)
#NOMINAL SCALE
# #----------------All scales together by cycle--------------------------------------
# for (j in 1:3) { #input file for each cycle 1:3
#
# # if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# # if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# # Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle) %>%
# mutate_if(is.factor, ~ as.numeric(.x)) %>%
# rename_with(~ gsub("T_ETHNEQ", "ETH", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_GNDREQ", "GND", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_IMMIEQ", "IMM", .x, fixed = TRUE)) %>%
# data.frame()
# prepareMplusData(df = data,
# filename = paste0("Mplus/DtaC",j,".dat"), interactive =FALSE)
#
# cas <- list()
#
# lapply(4:7, function(k) { #input file for different number of classes
# for(c in 1:k){
# cas[c] <- paste0("%c#",c,"%")
# }
# fileConn <- file(paste0("Mplus/lca_C",j,"cl",sprintf("%d", k),".inp"))
# writeLines(c(
# paste0("TITLE: LCA C", j," with ", k ," classes;"),
# "DATA: ",
# paste0("FILE = DtaC",j,".dat;"),
# "",
# "VARIABLE: ",
# paste0("NAMES = ", paste(colnames(data), collapse = "\n"),";"),
# "IDVARIABLE = IDSTUD;",
# paste0("USEVARIABLES = ", paste(colnames(data)[grepl('^GND|^IMM|^ETH', colnames(data))], collapse = "\n"),";"),
# paste0("CATEGORICAL = ", paste(colnames(data)[grepl('^GND|^IMM|^ETH', colnames(data))], collapse = "\n"),";"),
# "MISSING = .;",
# paste0("CLASSES = ",sprintf("c(%d);", k)),
# #paste0("CLASSES = g(",length(unique(data$IDCNTRY)),") ",sprintf("c(%d));", k)),
# #paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(data$IDCNTRY), collapse = "\n"),");"),
# "WEIGHT = SENWGT;",
# "STRATIFICATION = IDJK;",
# "CLUSTER = IDCL;",
# " ",
# "ANALYSIS:",
# "TYPE = COMPLEX MIXTURE;",
# "PROCESSORS = 4;",
# "STARTS = 100 50;",
# "STITERATIONS = 5;",
# "STSEED = 288;",
# "",
# "MODEL:",
# "%OVERALL%",
# " ",
# #"c ON g",
# paste(cas, collapse = "\n\n"),
# " ",
# "OUTPUT: ",
# "TECH10",#for bivariate model fit information
# "TECH11",
# #"SAMP",
# #"TECH14;", Time consuming tests
# #"CINTERVAL",
# #"SVALUES",
# ";",
# "",
# "SAVEDATA:",
# paste0("FILE = lca_C", j,"cl", k,".dat;"),
# "SAVE = CPROBABILITIES;"
#
# ), fileConn)
# close(fileConn)
# })
# }
# #----------------Each scale separately by cycle--------------------------------------------
# for (j in 1:3) { #input file for each cycle 1:3
#
# # if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# # if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# # Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle) %>%
# mutate_if(is.factor, ~ as.numeric(.x)) %>%
# rename_with(~ gsub("T_ETHNEQ", "ETHN", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_GNDREQ", "GNDR", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_IMMIEQ", "IMMI", .x, fixed = TRUE)) %>%
# data.frame()
# prepareMplusData(df = data,
# filename = paste0("Mplus/DtaC",j,".dat"),interactive =FALSE)
#
# for (l in c("ETHN", "GNDR", "IMMI")) { #3 scales
#
# f1 <- as.matrix(dplyr::select(data, starts_with(l)))~1
#
# cas <- list()
#
# lapply(2:4, function(k) { #input file for different number of classes 2-5
# for(c in 1:k){
# cas[c] <- paste0("%c#",c,"%")
# }
# fileConn <- file(paste0("Mplus/lca_","C", j, l, "cl", sprintf("%d", k), ".inp"))
# writeLines(c(
# paste0("TITLE: LCA C", j," ", l, " ", k ," classes;"),
# "DATA: ",
# paste0("FILE = DtaC",j,".dat;"),
# "",
# "VARIABLE: ",
# paste0("NAMES = ", paste(colnames(data), collapse = "\n"),";"),
# "IDVARIABLE = IDSTUD;",
# paste0("USEVARIABLES = ", paste(colnames(data)[grepl(paste0('^',l), colnames(data))], collapse = "\n"),";"),
# paste0("CATEGORICAL = ", paste(colnames(data)[grepl(paste0('^',l), colnames(data))], collapse = "\n"),";"),
# "MISSING = .;",
# paste0("CLASSES = ",sprintf("c(%d);", k)),
# #paste0("CLASSES = g(",length(unique(data$IDCNTRY)),") ",sprintf("c(%d));", k)),
# #paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(data$IDCNTRY), collapse = "\n"),");"),
# "WEIGHT = SENWGT;",
# "STRATIFICATION = IDJK;",
# "CLUSTER = IDCL;",
# " ",
# "ANALYSIS:",
# "TYPE = COMPLEX MIXTURE;",
# "PROCESSORS = 4;",
# "STARTS = 100 50;",
# "STITERATIONS = 5;",
# "STSEED = 288;",
# "",
# "MODEL:",
# "%OVERALL%",
# " ",
# #"c ON g",
# paste(cas, collapse = "\n\n"),
# " ",
# "OUTPUT: ",
# "TECH10",#for bivariate model fit information
# "TECH11",
# #"SAMP",
# #"TECH14;", Time consuming tests
# #"CINTERVAL",
# #"SVALUES",
# ";",
# "",
# "SAVEDATA:",
# paste0("FILE = lca_C", j, l, "cl", k,".dat;"),
# "SAVE = CPROBABILITIES;"
#
# ), fileConn)
# close(fileConn)
# })
# }
# }
#-------------------
#-------------------
####Binomial########
#-------------------
#-------------------
# #----------------All scales together by cycle--------------------------------------
for (j in 1:3) { #input file for each cycle 1:3
# if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# Scalesc <- Scales
data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesbc), -COUNTRY, -cycle) %>%
mutate_if(is.factor, ~ as.numeric(.x)) %>%
rename_with(~ gsub("bT_ETHNEQ", "ETH", .x, fixed = TRUE)) %>%
rename_with(~ gsub("bT_GNDREQ", "GND", .x, fixed = TRUE)) %>%
rename_with(~ gsub("bT_IMMIEQ", "IMM", .x, fixed = TRUE)) %>%
data.frame()
prepareMplusData(df = data,
filename = paste0("Mplus/BDtaC",j,".dat"), interactive =FALSE)
cas <- list()
lapply(4:7, function(k) { #input file for different number of classes
for(c in 1:k){
cas[c] <- paste0("%c#",c,"%")
}
fileConn <- file(paste0("Mplus/Blca_C",j,"cl",sprintf("%d", k),".inp"))
writeLines(c(
paste0("TITLE: LCA Binomial C", j," with ", k ," classes;"),
"DATA: ",
paste0("FILE = BDtaC",j,".dat;"),
"",
"VARIABLE: ",
paste0("NAMES = ", paste(colnames(data), collapse = "\n"),";"),
"IDVARIABLE = IDSTUD;",
paste0("USEVARIABLES = ", paste(colnames(data)[grepl('^GND|^IMM|^ETH', colnames(data))], collapse = "\n"),";"),
paste0("CATEGORICAL = ", paste(colnames(data)[grepl('^GND|^IMM|^ETH', colnames(data))], collapse = "\n"),";"),
"MISSING = .;",
paste0("CLASSES = ",sprintf("c(%d);", k)),
#paste0("CLASSES = g(",length(unique(data$IDCNTRY)),") ",sprintf("c(%d));", k)),
#paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(data$IDCNTRY), collapse = "\n"),");"),
"WEIGHT = SENWGT;",
"STRATIFICATION = IDJK;",
"CLUSTER = IDCL;",
" ",
"ANALYSIS:",
"TYPE = COMPLEX MIXTURE;",
"PROCESSORS = 4;",
"STARTS = 100 50;",
"STITERATIONS = 5;",
"STSEED = 288;",
"",
"MODEL:",
"%OVERALL%",
" ",
#"c ON g",
paste(cas, collapse = "\n\n"),
" ",
"OUTPUT: ",
"TECH10",#for bivariate model fit information
"TECH11",
#"SAMP",
#"TECH14;", Time consuming tests
#"CINTERVAL",
#"SVALUES",
";",
"",
"SAVEDATA:",
paste0("FILE = blca_C", j,"cl", k,".dat;"),
"SAVE = CPROBABILITIES;"
), fileConn)
close(fileConn)
})
}
# #----------------Each scale separately by cycle--------------------------------------------
# for (j in 1:3) { #input file for each cycle 1:3
#
# # if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# # if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# # Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle) %>%
# mutate_if(is.factor, ~ as.numeric(.x)) %>%
# rename_with(~ gsub("T_ETHNEQ", "ETHN", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_GNDREQ", "GNDR", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_IMMIEQ", "IMMI", .x, fixed = TRUE)) %>%
# data.frame()
# prepareMplusData(df = data,
# filename = paste0("Mplus/DtaC",j,".dat"),interactive =FALSE)
#
# for (l in c("ETHN", "GNDR", "IMMI")) { #3 scales
#
# f1 <- as.matrix(dplyr::select(data, starts_with(l)))~1
#
# cas <- list()
#
# lapply(2:4, function(k) { #input file for different number of classes 2-5
# for(c in 1:k){
# cas[c] <- paste0("%c#",c,"%")
# }
# fileConn <- file(paste0("Mplus/lca_","C", j, l, "cl", sprintf("%d", k), ".inp"))
# writeLines(c(
# paste0("TITLE: LCA C", j," ", l, " ", k ," classes;"),
# "DATA: ",
# paste0("FILE = DtaC",j,".dat;"),
# "",
# "VARIABLE: ",
# paste0("NAMES = ", paste(colnames(data), collapse = "\n"),";"),
# "IDVARIABLE = IDSTUD;",
# paste0("USEVARIABLES = ", paste(colnames(data)[grepl(paste0('^',l), colnames(data))], collapse = "\n"),";"),
# paste0("CATEGORICAL = ", paste(colnames(data)[grepl(paste0('^',l), colnames(data))], collapse = "\n"),";"),
# "MISSING = .;",
# paste0("CLASSES = ",sprintf("c(%d);", k)),
# #paste0("CLASSES = g(",length(unique(data$IDCNTRY)),") ",sprintf("c(%d));", k)),
# #paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(data$IDCNTRY), collapse = "\n"),");"),
# "WEIGHT = SENWGT;",
# "STRATIFICATION = IDJK;",
# "CLUSTER = IDCL;",
# " ",
# "ANALYSIS:",
# "TYPE = COMPLEX MIXTURE;",
# "PROCESSORS = 4;",
# "STARTS = 100 50;",
# "STITERATIONS = 5;",
# "STSEED = 288;",
# "",
# "MODEL:",
# "%OVERALL%",
# " ",
# #"c ON g",
# paste(cas, collapse = "\n\n"),
# " ",
# "OUTPUT: ",
# "TECH10",#for bivariate model fit information
# "TECH11",
# #"SAMP",
# #"TECH14;", Time consuming tests
# #"CINTERVAL",
# #"SVALUES",
# ";",
# "",
# "SAVEDATA:",
# paste0("FILE = lca_C", j, l, "cl", k,".dat;"),
# "SAVE = CPROBABILITIES;"
#
# ), fileConn)
# close(fileConn)
# })
# }
# }
#-------------------
#-------------------
####By subgroup########
#Nominal SCALE
#-------------------
#-------------------
# #----------------All scales together by cycle--------------------------------------
for (j in 1:3) { #input file for each cycle 1:3
# if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# Scalesc <- Scales
LAScalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)]
dataLA <- ds_lc0 %>% filter(cycle == paste0("C",j) & Subg == 2) %>%
dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle) %>%
mutate_if(is.factor, ~ as.numeric(.x)) %>%
rename_with(~ gsub("T_ETHNEQ", "ETH", .x, fixed = TRUE)) %>%
rename_with(~ gsub("T_GNDREQ", "GND", .x, fixed = TRUE)) %>%
rename_with(~ gsub("T_IMMIEQ", "IMM", .x, fixed = TRUE)) %>%
data.frame()
prepareMplusData(df = dataLA,
filename = paste0("Mplus/LADtaC",j,".dat"), interactive =FALSE)
EUScalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)]
dataEU <- ds_lc0 %>% filter(cycle == paste0("C",j) & Subg == 1) %>%
dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle) %>%
mutate_if(is.factor, ~ as.numeric(.x)) %>%
rename_with(~ gsub("T_ETHNEQ", "ETH", .x, fixed = TRUE)) %>%
rename_with(~ gsub("T_GNDREQ", "GND", .x, fixed = TRUE)) %>%
rename_with(~ gsub("T_IMMIEQ", "IMM", .x, fixed = TRUE)) %>%
data.frame()
prepareMplusData(df = dataEU,
filename = paste0("Mplus/EUDtaC",j,".dat"), interactive =FALSE)
cas <- list()
lapply(4:5, function(k) { #input file for different number of classes
for(c in 1:k){
cas[c] <- paste0("%c#",c,"%")
}
fileConn <- file(paste0("Mplus/LAlca_C",j,"cl",sprintf("%d", k),".inp"))
writeLines(c(
paste0("TITLE: LCA Binomial C", j," with ", k ," classes;"),
"DATA: ",
paste0("FILE = BDtaC",j,".dat;"),
"",
"VARIABLE: ",
paste0("NAMES = ", paste(colnames(data), collapse = "\n"),";"),
"IDVARIABLE = IDSTUD;",
paste0("USEVARIABLES = ", paste(colnames(data)[grepl('^GND|^IMM|^ETH', colnames(data))], collapse = "\n"),";"),
paste0("CATEGORICAL = ", paste(colnames(data)[grepl('^GND|^IMM|^ETH', colnames(data))], collapse = "\n"),";"),
"MISSING = .;",
paste0("CLASSES = ",sprintf("c(%d);", k)),
#paste0("CLASSES = g(",length(unique(data$IDCNTRY)),") ",sprintf("c(%d));", k)),
#paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(data$IDCNTRY), collapse = "\n"),");"),
"WEIGHT = SENWGT;",
"STRATIFICATION = IDJK;",
"CLUSTER = IDCL;",
" ",
"ANALYSIS:",
"TYPE = COMPLEX MIXTURE;",
"PROCESSORS = 4;",
"STARTS = 100 50;",
"STITERATIONS = 5;",
"STSEED = 288;",
"",
"MODEL:",
"%OVERALL%",
" ",
#"c ON g",
paste(cas, collapse = "\n\n"),
" ",
"OUTPUT: ",
"TECH10",#for bivariate model fit information
"TECH11",
#"SAMP",
#"TECH14;", Time consuming tests
#"CINTERVAL",
#"SVALUES",
";",
"",
"SAVEDATA:",
paste0("FILE = blca_C", j,"cl", k,".dat;"),
"SAVE = CPROBABILITIES;"
), fileConn)
close(fileConn)
})
}
# #----------------Each scale separately by cycle--------------------------------------------
# for (j in 1:3) { #input file for each cycle 1:3
#
# # if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# # if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# # Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle) %>%
# mutate_if(is.factor, ~ as.numeric(.x)) %>%
# rename_with(~ gsub("T_ETHNEQ", "ETHN", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_GNDREQ", "GNDR", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_IMMIEQ", "IMMI", .x, fixed = TRUE)) %>%
# data.frame()
# prepareMplusData(df = data,
# filename = paste0("Mplus/DtaC",j,".dat"),interactive =FALSE)
#
# for (l in c("ETHN", "GNDR", "IMMI")) { #3 scales
#
# f1 <- as.matrix(dplyr::select(data, starts_with(l)))~1
#
# cas <- list()
#
# lapply(2:4, function(k) { #input file for different number of classes 2-5
# for(c in 1:k){
# cas[c] <- paste0("%c#",c,"%")
# }
# fileConn <- file(paste0("Mplus/lca_","C", j, l, "cl", sprintf("%d", k), ".inp"))
# writeLines(c(
# paste0("TITLE: LCA C", j," ", l, " ", k ," classes;"),
# "DATA: ",
# paste0("FILE = DtaC",j,".dat;"),
# "",
# "VARIABLE: ",
# paste0("NAMES = ", paste(colnames(data), collapse = "\n"),";"),
# "IDVARIABLE = IDSTUD;",
# paste0("USEVARIABLES = ", paste(colnames(data)[grepl(paste0('^',l), colnames(data))], collapse = "\n"),";"),
# paste0("CATEGORICAL = ", paste(colnames(data)[grepl(paste0('^',l), colnames(data))], collapse = "\n"),";"),
# "MISSING = .;",
# paste0("CLASSES = ",sprintf("c(%d);", k)),
# #paste0("CLASSES = g(",length(unique(data$IDCNTRY)),") ",sprintf("c(%d));", k)),
# #paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(data$IDCNTRY), collapse = "\n"),");"),
# "WEIGHT = SENWGT;",
# "STRATIFICATION = IDJK;",
# "CLUSTER = IDCL;",
# " ",
# "ANALYSIS:",
# "TYPE = COMPLEX MIXTURE;",
# "PROCESSORS = 4;",
# "STARTS = 100 50;",
# "STITERATIONS = 5;",
# "STSEED = 288;",
# "",
# "MODEL:",
# "%OVERALL%",
# " ",
# #"c ON g",
# paste(cas, collapse = "\n\n"),
# " ",
# "OUTPUT: ",
# "TECH10",#for bivariate model fit information
# "TECH11",
# #"SAMP",
# #"TECH14;", Time consuming tests
# #"CINTERVAL",
# #"SVALUES",
# ";",
# "",
# "SAVEDATA:",
# paste0("FILE = lca_C", j, l, "cl", k,".dat;"),
# "SAVE = CPROBABILITIES;"
#
# ), fileConn)
# close(fileConn)
# })
# }
# }
###############################
#----------------All scales/cycles together--------------------------------------
# dataAll <- ds_lc0 %>%
# dplyr::select(all_of(sampleID), all_of(Scalesc), IDSTUD, group, grgnd) %>%
# mutate_if(is.factor, ~ as.numeric(.x)) %>%
# rename_with(~ gsub("T_ETHNEQ", "ETH", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_GNDREQ", "GND", .x, fixed = TRUE)) %>%
# rename_with(~ gsub("T_IMMIEQ", "IMM", .x, fixed = TRUE)) %>%
# data.frame()
#
#
# prepareMplusData(df = dataAll,
# filename = paste0("Mplus/Dta.dat"),interactive =FALSE)
# cas1 <- list()
#
# lapply(4:7, function(k) { #input file for different number of classes
# for(c in 1:k){
# cas1[c] <- paste0("%c#",c,"%")
# }
# fileConn <- file(paste0("Mplus/lca_cl",sprintf("%d", k),".inp"))
# writeLines(c(
# paste0("TITLE: LCA with ", k ," classes;"),
# "DATA: ",
# paste0("FILE = Dta.dat;"),
# "",
# "VARIABLE: ",
# paste0("NAMES = ", paste(colnames(dataAll), collapse = "\n"),";"),
# "IDVARIABLE = IDSTUD;",
# paste0("USEVARIABLES = ", paste(colnames(dataAll)[grepl('^GND|^IMM|^ETH', colnames(dataAll))], collapse = "\n"),";"),
# paste0("CATEGORICAL = ", paste(colnames(dataAll)[grepl('^GND|^IMM|^ETH', colnames(dataAll))], collapse = "\n"),";"),
# "MISSING = .;",
# paste0("CLASSES = ",sprintf("c(%d);", k)),
# #paste0("CLASSES = g(",length(unique(dataAll$IDCNTRY)),") ",sprintf("c(%d));", k)),
# #paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(dataAll$IDCNTRY), collapse = "\n"),");"),
# "WEIGHT = SENWGT;",
# "STRATIFICATION = IDJK;",
# "CLUSTER = IDCL;",
# " ",
# "ANALYSIS:",
# "TYPE = COMPLEX MIXTURE;",
# "PROCESSORS = 4;",
# "STARTS = 100 50;",
# "STITERATIONS = 5;",
# "STSEED = 288;",
# "",
# "MODEL:",
# "%OVERALL%",
# " ",
# #"c ON g",
# paste(cas1, collapse = "\n\n"),
# " ",
# "OUTPUT: ",
# "TECH10",#for bivariate model fit information
# "TECH11",
# #"SAMP",
# #"TECH14;", Time consuming tests
# #"CINTERVAL",
# #"SVALUES",
# ";",
# "",
# "SAVEDATA:",
# paste0("FILE = lca_cl", k,".dat;"),
# "SAVE = CPROBABILITIES;"
#
# ), fileConn)
# close(fileConn)
# })
#----------------All cycles together by scale--------------------------------------
# for (l in c("ETHN", "GNDR", "IMMI")) { #3 scales
#
# f1 <- as.matrix(dplyr::select(dataAll, starts_with(l)))~1
#
# cas <- list()
#
# lapply(2:4, function(k) { #input file for different number of classes 2-5
# for(c in 1:k){
# cas[c] <- paste0("%c#",c,"%")
# }
# fileConn <- file(paste0("Mplus/lca_", l, "cl", sprintf("%d", k), ".inp"))
# writeLines(c(
# paste("TITLE: LCA", l, k ," classes;"),
# "DATA: ",
# paste0("FILE = Dta.dat;"),
# "",
# "VARIABLE: ",
# paste0("NAMES = ", paste(colnames(dataAll), collapse = "\n"),";"),
# "IDVARIABLE = IDSTUD;",
# paste0("USEVARIABLES = ", paste(colnames(dataAll)[grepl(paste0('^',l), colnames(dataAll))], collapse = "\n"),";"),
# paste0("CATEGORICAL = ", paste(colnames(dataAll)[grepl(paste0('^',l), colnames(dataAll))], collapse = "\n"),";"),
# "MISSING = .;",
# paste0("CLASSES = ",sprintf("c(%d);", k)),
# #paste0("CLASSES = g(",length(unique(dataAll$IDCNTRY)),") ",sprintf("c(%d));", k)),
# #paste0("KNOWNCLASS = g(IDCNTRY =", paste(unique(dataAll$IDCNTRY), collapse = "\n"),");"),
# "WEIGHT = SENWGT;",
# "STRATIFICATION = IDJK;",
# "CLUSTER = IDCL;",
# " ",
# "ANALYSIS:",
# "TYPE = COMPLEX MIXTURE;",
# "PROCESSORS = 4;",
# "STARTS = 100 50;",
# "STITERATIONS = 5;",
# "STSEED = 288;",
# "",
# "MODEL:",
# "%OVERALL%",
# " ",
# #"c ON g",
# paste(cas, collapse = "\n\n"),
# " ",
# "OUTPUT: ",
# "TECH10",#for bivariate model fit information
# "TECH11",
# #"SAMP",
# #"TECH14;", Time consuming tests
# #"CINTERVAL",
# #"SVALUES",
# ";",
# "",
# "SAVEDATA:",
# paste0("FILE = lca", l, "cl", k,".dat;"),
# "SAVE = CPROBABILITIES;"
#
# ), fileConn)
# close(fileConn)
# })
# }
# #-------------------------
#runModels(target = "Mplus", replaceOutfile = "never") modifiedDate
#load("LCA_MplusModels.RData")
# MAllScalesBycycleMplus <- readModels(target = "Mplus", recursive = TRUE, filefilter = "lca_c[1-3]{1}cl*")
# MByScalesBycycleMplus <- readModels(target = "Mplus", recursive = TRUE, filefilter = "lca_c[1-3]{1}[a-z]{4}cl*")
# MBAllScalesBycycleMplus <- readModels(target = "Mplus", recursive = TRUE, filefilter = "blca_c[1-3]{1}cl*")
# MAllScalesAllcycleMplus <- readModels(target = "Mplus", recursive = TRUE, filefilter = "lca_cl[1-3]{1}")
# MByScalesAllcycleMplus <- readModels(target = "Mplus", recursive = TRUE, filefilter = "lca_[a-z]{4}cl*")
# save(MAllScalesBycycleMplus, MByScalesBycycleMplus, MBAllScalesBycycleMplus, file="LCA_MplusModels.RData")
# MAllScalesAllcycleMplus, MByScalesAllcycleMplus, file="LCA_MplusModels.RData")
#------------------------------------------------------------
#-----------------poLCA package------------------------------
#------------------------------------------------------------
#------------------------------------------------------------
# #------ All scales by cycle------------
Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)]
# MAllScalesbycyclev2 <- list(c1=list(),c2=list(),c3=list())
# for (j in 1:3) { #input file for each cycle 1:3
# print(paste0("C",j))
# # if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# # if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# # Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle)
#
# f1 <- as.matrix(dplyr::select(data, starts_with("T_")))~1
#
# for(i in 1:3){ #4 to 10 classes
# lc <- poLCA(f1, data, nclass=i, maxiter=3000,
# tol=1e-5, na.rm=FALSE,
# nrep=5, verbose=FALSE, calc.se=TRUE)
# MAllScalesbycyclev2[[j]][[i]] <- lc
# }
# }
# #------ By scales and cycles------
# Mbyscalebycyclev2 <- list(C1 = list(IMMI = list(), GNDR = list(), ETHN = list()),
# C2 = list(IMMI = list(), GNDR = list(), ETHN = list()),
# C3 = list(IMMI = list(), GNDR = list(), ETHN = list()))
# for (j in 1:3) { #3 cycles
# print(paste0("C",j))
# if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle)
# for (k in c("IMMI", "GNDR", "ETHN")) { #3 scales
# print(paste0(k))
# f1 <- as.matrix(dplyr::select(data, starts_with(paste0("T_",k,"EQ"))))~1
#
# for(i in 1:3){#1 to 5 classes
# lc <- poLCA(f1, data, nclass=i, maxiter=3000,
# tol=1e-5, na.rm=FALSE,
# nrep=5, verbose=FALSE, calc.se=TRUE, graphs = TRUE)
# Mbyscalebycyclev2[[j]][[k]][[i]] <- lc
# }
# }
# }
# #----------------------------------------------------------
# #------ By scales all cycles---------
# MAllcyclebyscale <- list(C1 = list(IMMI = list(), GNDR = list(), ETHN = list()),
# C2 = list(IMMI = list(), GNDR = list(), ETHN = list()),
# C3 = list(IMMI = list(), GNDR = list(), ETHN = list()))
# for (k in c("IMMI", "GNDR", "ETHN")) { #3 scales
# print(paste0(k))
# f1 <- as.matrix(dplyr::select(dataAllR, starts_with(paste0("T_",k,"EQ"))))~1
#
# for(i in 2:4){#1 to 5 classes
# lc <- poLCA(f1, dataAllR, nclass=i, maxiter=3000,
# tol=1e-5, na.rm=FALSE,
# nrep=5, verbose=FALSE, calc.se=TRUE, graphs = TRUE)
# MAllcyclebyscale[[k]][[i]] <- lc
# }
# }
# #------ All scales and cycle-----
# dataAllR <- ds_lc0 %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -COUNTRY, -cycle)
#
# f1 <- as.matrix(dplyr::select(dataAllR, starts_with("T_")))~1
#
# MAllScalesCycles <- list()
# for(i in 4:7){ #4 to 10 classes
# lc <- poLCA(f1, dataAllR, nclass=i, maxiter=3000,
# tol=1e-5, na.rm=FALSE,
# nrep=5, verbose=FALSE, calc.se=TRUE)
# MAllScalesCycles[[i]] <- lc
# }
# #----------------------------------------------------------
# #-------Each scale with country covariate----------
# MbyscaleCov1 <- list(C1 = list(IMMI = list(), GNDR = list(), ETHN = list()),
# C2 = list(IMMI = list(), GNDR = list(), ETHN = list()),
# C3 = list(IMMI = list(), GNDR = list(), ETHN = list()))
# for (j in 1:3) { #3 cycles
# print(paste0("C",j))
# if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# Scalesc <- Scales
#
# data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
# dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesc), -cycle)
# for (k in c("IMMI", "GNDR", "ETHN")) { #3 scales
# print(paste0("C",j,"\n"))
# print(paste0(k,"\n"))
# fcov1 <- as.matrix(dplyr::select(data, starts_with(paste0("T_",k,"EQ")))) ~ COUNTRY
#
# for(i in 2:5){#1 to 5 classes
# lc <- poLCA(fcov1, data, nclass=i, maxiter=3000,
# tol=1e-5, na.rm=FALSE,
# nrep=5, verbose=FALSE, calc.se=TRUE, graphs = TRUE)
# MbyscaleCov1[[j]][[k]][[i]] <- lc
# }
# }
# }
#------------------------------------------------------------
# #--------Binomial---------------------
# #------------------------------------------------------------
# #------ All scales by cycle ------
Scalesbc <- Scalesb[!grepl(paste0(c("bT_GNDREQ7", "bT_ETHNEQ5"), collapse = "|"), Scalesb)]
MBAllScalesbycycle <- list(c1=list(),c2=list(),c3=list())
for (j in 1:3) { #input file for each cycle 1:3
print(paste0("C",j))
# if(j == 1) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7", "T_ETHNEQ5"), collapse = "|"), Scales)] else
# if(j == 2) Scalesc <- Scales[!grepl(paste0(c("T_GNDREQ7"), collapse = "|"), Scales)] else
# Scalesc <- Scales
data <- ds_lc0 %>% filter(cycle == paste0("C",j)) %>%
dplyr::select(all_of(Id), all_of(sampleID), all_of(Scalesbc), -COUNTRY, -cycle)
f1 <- as.matrix(dplyr::select(data, starts_with("bT_"))+1)~1
for(i in 4:7){ #4 to 10 classes
lc <- poLCA(f1, data, nclass=i, maxiter=3000,
tol=1e-5, na.rm=FALSE,
nrep=5, verbose=FALSE, calc.se=TRUE)
MBAllScalesbycycle[[j]][[i]] <- lc
}
}
#---------------
#load("LCA_RModels.RData")
#save(MAllScalesbycyclev2, Mbyscalebycyclev2,file="LCA_RModelsv2.RData")
# MAllScalesCycles, MAllcyclebyscale,
# MbyscaleCov1, file="LCA_RModels.RData")
#
# library(lcca)
# lca.lcca<-lcca::lca(
# f1,
# nclass=4,
# data=ds_lc0,
# flatten.rhos=1
# )
# summary.lca(lca.lcca, show.all=T)
# groups
# constrain.rhos
# constrain.gammas
# lca.cov
library(mclust)
height_fit_mclust <- Mclust(height)
summary(height_fit_mclust, parameters = TRUE)