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Report Models.R
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library(MplusAutomation)
library(gridExtra)
detach(package:plyr)
ISC_lvRlca <- ISC_lvR %>%
dplyr::select(all_of(Id), all_of(sampleID), all_of(ScalesM2), all_of(Man_cate), all_of(Man_cont)) %>%
mutate(cntgnd = factor(ifelse(!is.na(T_GENDER), paste0(COUNTRY,T_GENDER), NA)))
load("MplusModels.RData")
classes2M2 <- c("Engaged with \ngender equality",
"Gender differentiation")
classes3M2 <- c("Engaged with \ngender equality",
"Gender differentiation",
"Not engaged with \ngender equality")
#--Model fit
cat('\n')
cat('\n')
cat('## Models with 2 and 3 classes \n')
cat('\n')
cat('\n')
Graph_modelfit("lca", mg = FALSE)
cat('\n')
cat('\n')
# compareModels(lca$lca_C1cl2.out,
# lca$lca_C1cl3.out, diffTest = TRUE)
# compareModels(lca$lca_C2cl2.out,
# lca$lca_C2cl3.out, diffTest = TRUE)
# compareModels(lca$lca_C3cl2.out,
# lca$lca_C3cl3.out, diffTest = TRUE)
# ------------Results with 2 classes by cycle ----
cat('\n')
cat('\n')
cat('### Results with 2-classes \n')
cat('\n')
cat('\n')
warn_lca2 <- list(C1 = lca$lca_C1cl2.out$warnings,
C2 = lca$lca_C2cl2.out$warnings,
C3 = lca$lca_C3cl2.out$warnings)
cat('\n')
cat('\n')
cat('\n')
cat('\n')
wr <- names(warn_lca2[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_lca2)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
lca_C1cl2 <- lca$lca_C1cl2.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))
lca_C1cl2$Class <- factor(lca_C1cl2$Class,
levels = c("1", "2"),
labels = classes2M2)
#----C2
lca_C2cl2 <- lca$lca_C2cl2.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))
lca_C2cl2$Class <- factor(lca_C2cl2$Class,
levels = c("1", "2"),
labels = classes2M2)
#----C3
lca_C3cl2 <- lca$lca_C3cl2.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))
lca_C3cl2$Class <- factor(lca_C3cl2$Class,
levels = c("1", "2"),
labels = classes2M2)
lc2 <- rbind(cbind(Cycle = "1999", lca_C1cl2),
cbind(Cycle = "2009", lca_C2cl2),
cbind(Cycle = "2016", lca_C3cl2))
graphclass(lc2, nclass = 2, orden = c(5,2,1,3,4,6) ,title = "LCA with 2 classes", mg = FALSE)
cat('\n')
cat('\n')
sizelca2 <- full_join(full_join(lca$lca_C1cl2.out$class_counts$modelEstimated %>% dplyr::select(-count) %>%
rename_with(~ c("1999", "Class")[which(c("proportion", "class") == .x)], .cols = c("proportion", "class")) %>%
mutate(Class = factor(
case_when(
Class == 1 ~ 1,
Class == 2 ~ 2), levels = 1:2, labels = classes2M2)),
lca$lca_C2cl2.out$class_counts$modelEstimated %>% dplyr::select(-count) %>%
rename_with(~ c("2009", "Class")[which(c("proportion", "class") == .x)], .cols = c("proportion", "class")) %>%
mutate(Class = factor(
case_when(
Class == 1 ~ 1,
Class == 2 ~ 2), levels = 1:2, labels = classes2M2)), by = "Class"),
lca$lca_C3cl2.out$class_counts$modelEstimated %>% dplyr::select(-count) %>%
rename_with(~ c("2016", "Class")[which(c("proportion", "class") == .x)], .cols = c("proportion", "class")) %>%
mutate(Class = factor(
case_when(
Class == 1 ~ 1,
Class == 2 ~ 2), levels = 1:2, labels = classes2M2)), by = "Class")
sizelca2 <- sizelca2 %>% reshape2::melt(id.vars = c("Class"), variable.name = "Cycle") %>%
arrange(Cycle) %>%
group_by(Cycle) %>%
mutate(countT= sum(value, na.rm = TRUE)) %>%
group_by(Class) %>%
mutate(per=value/countT) %>% dplyr::select(Cycle, Class, per)
HighProb(lc2, orden = c(5,2,1,3,4,6), title = "Response categories probabilities for 2 classes by Cycle")
cat('\n')
cat('\n')
sizelca2 %>%
reshape2::dcast(Cycle ~ Class) %>%
kbl(caption = "Size two classes", booktabs = TRUE, longtable = TRUE, row.names = FALSE, digits = 3) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2:3, width = "8em") %>%
print()
cat('\n')
cat('\n')
# ----------
# ------------Results with 3 classes by cycle ----
cat('\n')
cat('\n')
cat('### Results with 3-classes \n')
cat('\n')
cat('\n')
warn_lca3 <- list(C1 = lca$lca_C1cl3.out$warnings,
C2 = lca$lca_C2cl3.out$warnings,
C3 = lca$lca_C3cl3.out$warnings)
cat('\n')
cat('\n')
wr <- names(warn_lca3[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_lca3)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
lca_C1cl3 <- lca$lca_C1cl3.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))
lca_C1cl3$Class <- factor(lca_C1cl3$Class,
levels = c("1", "2", "3"),
labels = classes3M2)
#----C2
lca_C2cl3 <- lca$lca_C2cl3.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))
lca_C2cl3$Class <- factor(lca_C2cl3$Class,
levels = c("3", "2", "1"),
labels = classes3M2)
#----C3
lca_C3cl3 <- lca$lca_C3cl3.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))
lca_C3cl3$Class <- factor(lca_C3cl3$Class,
levels = c("3", "1", "2"),
labels = classes3M2)
lc3 <- rbind(cbind(Cycle = "1999", lca_C1cl3),
cbind(Cycle = "2009", lca_C2cl3),
cbind(Cycle = "2016", lca_C3cl3))
graphclass(lc3, nclass = 3, orden = c(5,2,1,3,4,6) ,title = "LCA with 3 classes", mg = FALSE)
cat('\n')
cat('\n')
lci3 <- VarClass(lc3)
lci3 %>% group_by(Cycle, Class, param) %>% filter(category == 2) %>% select(Cycle, param, Class, value) %>%
mutate(value = cell_spec(value, color = ifelse(value > 0.8, "blue", "red"))) %>%
reshape2::dcast(param + Cycle ~ Class) %>%
kbl(caption = "Probabilities to Agree each item by Class", booktabs = TRUE, longtable = TRUE,
align = "llrrr", row.names = FALSE, digits = 3, escape = FALSE) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(1, width = "14em") %>%
column_spec(3:5, width = "8em") %>%
collapse_rows(1, valign = "top") %>%
print()
cat('\n')
cat('\n')
sizelca3 <- full_join(full_join(lca$lca_C1cl3.out$class_counts$modelEstimated %>% dplyr::select(-count) %>%
rename_with(~ c("1999", "Class")[which(c("proportion", "class") == .x)], .cols = c("proportion", "class")) %>%
mutate(Class = factor(
case_when(
Class == 1 ~ 1,
Class == 2 ~ 2,
Class == 3 ~ 3), levels = 1:3, labels = classes3M2)),
lca$lca_C2cl3.out$class_counts$modelEstimated %>% dplyr::select(-count) %>%
rename_with(~ c("2009", "Class")[which(c("proportion", "class") == .x)], .cols = c("proportion", "class")) %>%
mutate(Class = factor(
case_when(
Class == 1 ~ 3,
Class == 2 ~ 2,
Class == 3 ~ 1), levels = 1:3, labels = classes3M2)), by = "Class"),
lca$lca_C3cl3.out$class_counts$modelEstimated %>% dplyr::select(-count) %>%
rename_with(~ c("2016", "Class")[which(c("proportion", "class") == .x)], .cols = c("proportion", "class")) %>%
mutate(Class = factor(
case_when(
Class == 1 ~ 2,
Class == 2 ~ 3,
Class == 3 ~ 1), levels = 1:3, labels = classes3M2)), by = "Class")
sizelca3 <- sizelca3 %>% reshape2::melt(id.vars = c("Class"), variable.name = "Cycle") %>%
arrange(Cycle) %>%
group_by(Cycle) %>%
mutate(countT= sum(value, na.rm = TRUE)) %>%
group_by(Class) %>%
mutate(per=value/countT) %>% dplyr::select(Cycle, Class, per)
HighProb(lc3, orden = c(5,2,1,3,4,6), title = "Response category probabilities for three classes by Cycle")
cat('\n')
cat('\n')
sizelca3 %>%
reshape2::dcast(Cycle ~ Class) %>%
kbl(caption = "Size three classes", booktabs = TRUE, longtable = TRUE, row.names = FALSE, digits = 3) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2:4, width = "8em") %>%
print()
cat('\n')
cat('\n')
# ------
cat('\\newpage')
cat('\n')
cat('\n')
# ------------Results with 3C MG Gndr classes by cycle ----
cat('\n')
cat('\n')
cat('## Multigroup analysis for Gender \n')
cat('\n')
cat('\n')
Graph_modelfitMG("MGGndr", title = "Gender Multigroup (2 groups), 2 and 3 classes")
cat('\n')
cat('\n')
cat('### Results with 3-classes \n')
cat('\n')
cat('\n')
# ------ MG Gender _1CHet ----
cat('#### Complete Heterogeneity across gender \n')
cat('\n')
cat('\n')
warn_MGGndr <- list(C1 = MGGndr$MGGndr_C1cl3.out$warnings,
C2 = MGGndr$MGGndr_C2cl3.out$warnings,
C3 = MGGndr$MGGndr_C3cl3.out$warnings)
cat('\n')
cat('\n')
wr <- names(warn_MGGndr[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_MGGndr)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
MGGndr_C1cl3 <- MGGndr$MGGndr_C1cl3_1CHet.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(
case_when(
Class == "1.1" ~ 2,
Class == "1.2" ~ 3,
Class == "1.3" ~ 1,
Class == "2.1" ~ 2,
Class == "2.2" ~ 1,
Class == "2.3" ~ 3),
levels = c("1", "2", "3"), labels = classes3M2))
#----C2
MGGndr_C2cl3 <- MGGndr$MGGndr_C2cl3_1CHet.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))%>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(
case_when(
Class == "1.1" ~ 3,
Class == "1.2" ~ 2,
Class == "1.3" ~ 1,
Class == "2.1" ~ 2,
Class == "2.2" ~ 1,
Class == "2.3" ~ 3),
levels = c("1", "2", "3"), labels = classes3M2))
#----C3
MGGndr_C3cl3 <- MGGndr$MGGndr_C3cl3_1CHet.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class == "1.1" ~ 1,
Class == "1.2" ~ 3,
Class == "1.3" ~ 2,
Class == "2.1" ~ 2,
Class == "2.2" ~ 3,
Class == "2.3" ~ 1),
levels = c("1", "2", "3"), labels = classes3M2))
MGGndr3 <- rbind(cbind(Cycle = "1999", MGGndr_C1cl3),
cbind(Cycle = "2009", MGGndr_C2cl3),
cbind(Cycle = "2016", MGGndr_C3cl3))
#---
graphclass(MGGndr3, nclass = 3, orden = c(5,2,1,3,4,6) ,title = "MG Gender Complete Heterogeneity")
cat('\n')
cat('\n')
lcign <- VarClass(MGGndr3)
lcign %>% group_by(Cycle, ClassN, Group, param) %>% filter(category == 2) %>%
select(Cycle, param, ClassN, Group, value) %>%
mutate(value = cell_spec(value, color = ifelse(value > 0.8, "blue", "red"))) %>%
reshape2::dcast(param + Cycle ~ Group + ClassN) %>%
kbl(caption = "Probabilities to Agree each item by Class and Gender Complete Heterogeneity", booktabs = TRUE, longtable = TRUE,
align = "llrrrrrr", row.names = FALSE, digits = 3, col.names = c("Item", "Year", classes3M2, classes3M2), escape = FALSE) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 10, full_width = FALSE) %>%
column_spec(1, width = "14em") %>%
column_spec(2, width = "2em") %>%
column_spec(3:8, width = "4em") %>%
collapse_rows(1, valign = "top") %>%
add_header_above(c(" "=1," "=1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
#Extracting patterns
sizeMGGndr3 <- full_join(
full_join(MGGndr$MGGndr_C1cl3_1CHet.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("1999", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class == "1.1" ~ 2,
Class == "1.2" ~ 3,
Class == "1.3" ~ 1,
Class == "2.1" ~ 2,
Class == "2.2" ~ 1,
Class == "2.3" ~ 3),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) ,
MGGndr$MGGndr_C2cl3_1CHet.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2009", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class == "1.1" ~ 3,
Class == "1.2" ~ 2,
Class == "1.3" ~ 1,
Class == "2.1" ~ 2,
Class == "2.2" ~ 1,
Class == "2.3" ~ 3),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class), by = c("Group","ClassN")),
MGGndr$MGGndr_C3cl3_1CHet.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2016", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class == "1.1" ~ 1,
Class == "1.2" ~ 3,
Class == "1.3" ~ 2,
Class == "2.1" ~ 2,
Class == "2.2" ~ 3,
Class == "2.3" ~ 1),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) , by = c("Group","ClassN")) %>%
mutate(Group = factor(Group, levels = c("1", "2"), labels = c("Boy", "Girl"))) %>% select(Group, ClassN, `1999`, `2009`, `2016`)
sizeMGGndr3 <- sizeMGGndr3 %>% reshape2::melt(id.vars = c("Group","ClassN"), variable.name = "Cycle") %>%
arrange(Group, Cycle) %>%
group_by(Group, Cycle) %>%
mutate(countT= sum(value, na.rm = TRUE)) %>%
group_by(ClassN) %>%
mutate(per=value/countT) %>% dplyr::select(Group, Cycle, ClassN, per)
HighProbMG(MGGndr3, sizeMGGndr3, orden = c(5,2,1,3,4,6), n = 1:2, title = "Probabilities of Agree and size for each class in Gender Multigroup analysis\n Complete Heterogeneity by Cycle")
sizeMGGndr3 %>%
reshape2::dcast(Cycle ~ Group + ClassN) %>%
kbl(caption = "Size three classes MG Gender Complete Heterogeneity", booktabs = TRUE, longtable = TRUE, row.names = FALSE,
digits = 3, col.names = c("Year", classes3M2, classes3M2)) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2:7, width = "4em") %>%
add_header_above(c(" " = 1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
# ------ MG Gender PH1 ----
cat('#### Partial Homogeneity - one class equal across gender \n')
cat('\n')
cat('\n')
warn_MGGndr1PH1 <- list(C1 = MGGndr$MGGndr_C1cl31PH1.out$warnings,
C2 = MGGndr$MGGndr_C2cl31PH1.out$warnings,
C3 = MGGndr$MGGndr_C3cl31PH1.out$warnings)
cat('\n')
cat('\n')
wr <- names(warn_MGGndr1PH1[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_MGGndr1PH1)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
MGGndr_C1cl3 <- MGGndr$MGGndr_C1cl3PH1.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(
case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 3,
Class1 == "3" ~ 1),
levels = c("1", "2", "3"), labels = classes3M2))
#----C2
MGGndr_C2cl3 <- MGGndr$MGGndr_C2cl3PH1.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))%>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(
case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 1,
Class1 == "3" ~ 3),
levels = c("1", "2", "3"), labels = classes3M2))
#----C3
MGGndr_C3cl3 <- MGGndr$MGGndr_C3cl3PH1.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 1,
Class1 == "3" ~ 3),
levels = c("1", "2", "3"), labels = classes3M2))
MGGndr3PH1 <- rbind(cbind(Cycle = "1999", MGGndr_C1cl3),
cbind(Cycle = "2009", MGGndr_C2cl3),
cbind(Cycle = "2016", MGGndr_C3cl3))
#---
graphclass(MGGndr3PH1, nclass = 3, orden = c(5,2,1,3,4,6) ,title = "MG Gender Partial Homogeneity - 1 class equal across gender")
cat('\n')
cat('\n')
lcign <- VarClass(MGGndr3PH1)
lcign %>% group_by(Cycle, ClassN, Group, param) %>% filter(category == 2) %>%
select(Cycle, param, ClassN, Group, value) %>%
mutate(value = cell_spec(value, color = ifelse(value > 0.8, "blue", "red"))) %>%
reshape2::dcast(param + Cycle ~ Group + ClassN) %>%
kbl(caption = "Probabilities to Agree each item by Class and Gender - Partial homogeneity 1 class", booktabs = TRUE, longtable = TRUE,
align = "llrrrrrr", row.names = FALSE, digits = 3, col.names = c("Item", "Year", classes3M2, classes3M2), escape = FALSE) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 10, full_width = FALSE) %>%
column_spec(1, width = "14em") %>%
column_spec(2, width = "2em") %>%
column_spec(3:8, width = "4em") %>%
collapse_rows(1, valign = "top") %>%
add_header_above(c(" "=1," "=1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
#Extracting patterns
sizeMGGndr3PH1 <- full_join(
full_join(MGGndr$MGGndr_C1cl3PH1.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("1999", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 3,
Class1 == "3" ~ 1),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) ,
MGGndr$MGGndr_C2cl3PH1.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2009", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 1,
Class1 == "3" ~ 3),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class), by = c("Group","ClassN")),
MGGndr$MGGndr_C3cl3PH1.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2016", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 1,
Class1 == "3" ~ 3),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) , by = c("Group","ClassN")) %>%
mutate(Group = factor(Group, levels = c("1", "2"), labels = c("Boy", "Girl"))) %>% select(Group, ClassN, `1999`, `2009`, `2016`)
sizeMGGndr3PH1 <- sizeMGGndr3PH1 %>% reshape2::melt(id.vars = c("Group","ClassN"), variable.name = "Cycle") %>%
arrange(Group, Cycle) %>%
group_by(Group, Cycle) %>%
mutate(countT= sum(value, na.rm = TRUE)) %>%
group_by(ClassN) %>%
mutate(per=value/countT) %>% dplyr::select(Group, Cycle, ClassN, per)
HighProbMG(MGGndr3PH1, sizeMGGndr3PH1, orden = c(5,2,1,3,4,6), n = 1:2, title = "Probabilities of Agree and size for each class in Gender Multigroup analysis\n Partial Homogeneity (1 class fixed) by Cycle")
sizeMGGndr3PH1 %>%
reshape2::dcast(Cycle ~ Group + ClassN) %>%
kbl(caption = "Size three classes MG Gender Partial Homogeneity - one class equal across gender", booktabs = TRUE, longtable = TRUE, row.names = FALSE,
digits = 3, col.names = c("Year", classes3M2, classes3M2)) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2:7, width = "4em") %>%
add_header_above(c(" " = 1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
# ------ MG Gender PH2 ----
cat('#### Partial Homogeneity - two classes equal across gender \n')
cat('\n')
cat('\n')
warn_MGGndr1PH2 <- list(C1 = MGGndr$MGGndr_C1cl31PH2.out$warnings,
C2 = MGGndr$MGGndr_C2cl31PH2.out$warnings,
C3 = MGGndr$MGGndr_C3cl31PH2.out$warnings)
cat('\n')
cat('\n')
wr <- names(warn_MGGndr1PH2[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_MGGndr1PH2)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
MGGndrPH2_C1cl3 <- MGGndr$MGGndr_C1cl3PH2.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(
case_when(
Class1 == "1" ~ 1,
Class1 == "2" ~ 2,
Class1 == "3" ~ 3),
levels = c("1", "2", "3"), labels = classes3M2))
#----C2
MGGndrPH2_C2cl3 <- MGGndr$MGGndr_C2cl3PH2.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))%>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(
case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 3,
Class1 == "3" ~ 1),
levels = c("1", "2", "3"), labels = classes3M2))
#----C3
MGGndrPH2_C3cl3 <- MGGndr$MGGndr_C3cl3PH2.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = c("1", "2"), labels = c("Boy", "Girl")),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 3,
Class1 == "3" ~ 1),
levels = c("1", "2", "3"), labels = classes3M2))
MGGndr3PH2 <- rbind(cbind(Cycle = "1999", MGGndrPH2_C1cl3),
cbind(Cycle = "2009", MGGndrPH2_C2cl3),
cbind(Cycle = "2016", MGGndrPH2_C3cl3))
#---
graphclass(MGGndr3PH2, nclass = 3, orden = c(5,2,1,3,4,6) ,title = "MG Gender Partial Homogeneity - 2 classes equal across gender")
cat('\n')
cat('\n')
lcign <- VarClass(MGGndr3PH2)
lcign %>% group_by(Cycle, ClassN, Group, param) %>% filter(category == 2) %>%
select(Cycle, param, ClassN, Group, value) %>%
mutate(value = cell_spec(value, color = ifelse(value > 0.8, "blue", "red"))) %>%
reshape2::dcast(param + Cycle ~ Group + ClassN) %>%
kbl(caption = "Probabilities to Agree each item by Class and Gender - Partial homogeneity 2 classes", booktabs = TRUE, longtable = TRUE,
align = "llrrrrrr", row.names = FALSE, digits = 3, col.names = c("Item", "Year", classes3M2, classes3M2), escape = FALSE) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 10, full_width = FALSE) %>%
column_spec(1, width = "14em") %>%
column_spec(2, width = "2em") %>%
column_spec(3:8, width = "4em") %>%
collapse_rows(1, valign = "top") %>%
add_header_above(c(" "=1," "=1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
#Extracting patterns
sizeMGGndr3PH2 <- full_join(
full_join(MGGndr$MGGndr_C1cl3PH2.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("1999", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 1,
Class1 == "2" ~ 2,
Class1 == "3" ~ 3),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) ,
MGGndr$MGGndr_C2cl3PH2.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2009", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 3,
Class1 == "3" ~ 1),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class), by = c("Group","ClassN")),
MGGndr$MGGndr_C3cl3PH2.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2016", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 3,
Class1 == "3" ~ 1),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) , by = c("Group","ClassN")) %>%
mutate(Group = factor(Group, levels = c("1", "2"), labels = c("Boy", "Girl"))) %>% select(Group, ClassN, `1999`, `2009`, `2016`)
sizeMGGndr3PH2 <- sizeMGGndr3PH2 %>% reshape2::melt(id.vars = c("Group","ClassN"), variable.name = "Cycle") %>%
arrange(Group, Cycle) %>%
group_by(Group, Cycle) %>%
mutate(countT= sum(value, na.rm = TRUE)) %>%
group_by(ClassN) %>%
mutate(per=value/countT) %>% dplyr::select(Group, Cycle, ClassN, per)
HighProbMG(MGGndr3PH2, sizeMGGndr3PH2, orden = c(5,2,1,3,4,6), n = 1:2, title = "Probabilities of Agree and size for each class in Gender Multigroup analysis\n Partial Homogeneity (2 classes fixed) by Cycle")
sizeMGGndr3PH2 %>%
reshape2::dcast(Cycle ~ Group + ClassN) %>%
kbl(caption = "Size three classes MG Gender Partial Homogeneity - two classes equal across gender", booktabs = TRUE, longtable = TRUE, row.names = FALSE,
digits = 3, col.names = c("Year", classes3M2, classes3M2)) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2:7, width = "4em") %>%
add_header_above(c(" " = 1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
# ------ MG Gender PH3 ----
cat('#### Complete Homogeneity - three classes equal across gender \n')
cat('\n')
cat('\n')
warn_MGGndr1PH3 <- list(C1 = MGGndr$MGGndr_C1cl31PH3.out$warnings,
C2 = MGGndr$MGGndr_C2cl31PH3.out$warnings,
C3 = MGGndr$MGGndr_C3cl31PH3.out$warnings)
cat('\n')
cat('\n')
wr <- names(warn_MGGndr1PH3[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_MGGndr1PH3)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
MGGndrPH3_C1cl3 <- MGGndr$MGGndr_C1cl3PH3.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Class = factor(case_when(
Class == "C#1" ~ 2,
Class == "C#2" ~ 1,
Class == "C#3" ~ 3),
levels = c(1, 2, 3), labels = classes3M2),
ClassN = Class)
#----C2
MGGndrPH3_C2cl3 <- MGGndr$MGGndr_C2cl3PH3.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))%>%
mutate(Class = factor(
case_when(
Class == "C#1" ~ 1,
Class == "C#2" ~ 3,
Class == "C#3" ~ 2),
levels = c(1, 2, 3), labels = classes3M2),
ClassN = Class)
#----C3
MGGndrPH3_C3cl3 <- MGGndr$MGGndr_C3cl3PH3.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Class = factor(case_when(
Class == "C#1" ~ 1,
Class == "C#2" ~ 3,
Class == "C#3" ~ 2),
levels = c(1, 2, 3), labels = classes3M2),
ClassN = Class)
MGGndr3PH3 <- rbind(cbind(Cycle = "1999", MGGndrPH3_C1cl3),
cbind(Cycle = "2009", MGGndrPH3_C2cl3),
cbind(Cycle = "2016", MGGndrPH3_C3cl3))
#---
graphclass(MGGndr3PH3, nclass = 3, orden = c(5,2,1,3,4,6) ,title = "MG Gender Complete Homogeneity across gender", mg = FALSE)
cat('\n')
cat('\n')
lcign <- VarClass(MGGndr3PH3)
lcign %>% group_by(Cycle, ClassN, param) %>% filter(category == 2) %>%
select(Cycle, param, Class, value) %>%
mutate(value = cell_spec(value, color = ifelse(value > 0.8, "blue", "red"))) %>%
reshape2::dcast(param + Cycle ~ Class) %>%
kbl(caption = "Probabilities to Agree each item by Class and Gender - Complete homogeneity", booktabs = TRUE, longtable = TRUE,
align = "llrrrrrr", row.names = FALSE, digits = 3, col.names = c("Item", "Year", classes3M2), escape = FALSE) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 10, full_width = FALSE) %>%
column_spec(1, width = "18em") %>%
column_spec(2, width = "2em") %>%
column_spec(3:5, width = "4em") %>%
collapse_rows(1, valign = "top") %>%
print()
cat('\n')
cat('\n')
#Extracting patterns
sizeMGGndr3PH3 <- full_join(
full_join(MGGndr$MGGndr_C1cl3PH3.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("1999", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 2,
Class1 == "2" ~ 1,
Class1 == "3" ~ 3),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) ,
MGGndr$MGGndr_C2cl3PH3.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2009", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 1,
Class1 == "2" ~ 3,
Class1 == "3" ~ 2),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class), by = c("Group","ClassN")),
MGGndr$MGGndr_C3cl3PH3.out$class_counts$modelEstimated.patterns %>%
rename_with(~ c("2016", "Group")[which(c("count", "G") == .x)], .cols = c("count", "G")) %>%
mutate(Class = paste(Group,C,sep="."),
Class1 = sub('*.\\.', '', Class),
ClassN = factor(case_when(
Class1 == "1" ~ 1,
Class1 == "2" ~ 3,
Class1 == "3" ~ 2),
levels = 1:3, labels = classes3M2)) %>% dplyr::select(-proportion, -C, -Class) , by = c("Group","ClassN")) %>%
mutate(Group = factor(Group, levels = c("1", "2"), labels = c("Boy", "Girl"))) %>% select(Group, ClassN, `1999`, `2009`, `2016`)
sizeMGGndr3PH3 <- sizeMGGndr3PH3 %>% reshape2::melt(id.vars = c("Group","ClassN"), variable.name = "Cycle") %>%
arrange(Group, Cycle) %>%
group_by(Group, Cycle) %>%
mutate(countT= sum(value, na.rm = TRUE)) %>%
group_by(ClassN) %>%
mutate(per=value/countT) %>% dplyr::select(Group, Cycle, ClassN, per)
MGGndr3PH3 <- rbind(cbind(Group = "Boy", MGGndr3PH3), cbind(Group = "Girl", MGGndr3PH3))
HighProbMG(MGGndr3PH3, sizeMGGndr3PH3, orden = c(5,2,1,3,4,6), n = 1:2, title = "Probabilities of Agree and size for each class in Gender Multigroup analysis Complete Homogeneity by Cycle")
sizeMGGndr3PH3 %>%
reshape2::dcast(Cycle ~ Group + ClassN) %>%
kbl(caption = "Size three classes MG Gender Complete Homogeneity across gender", booktabs = TRUE, longtable = TRUE, row.names = FALSE,
digits = 3, col.names = c("Year", classes3M2, classes3M2)) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2:7, width = "4em") %>%
add_header_above(c(" " = 1, "Boy" = 3, "Girl" = 3)) %>%
print()
cat('\n')
cat('\n')
# ----------
cat('\\newpage')
# ------------Results with MGCntry 2-classes by cycle ----
cat('\n')
cat('\n')
cat('## Multigroup analysis for Country \n')
cat('\n')
cat('\n')
Graph_modelfitMG("MGCntry", title = "Country Multigroup")
cat('\n')
cat('\n')
cat('### Results with 2-classes \n')
cat('\n')
cat('\n')
# ------ 2C MG Country _1CHet ----
cat('#### Complete Heterogeneity across country \n')
cat('\n')
cat('\n')
warn_MGCntry <- list(C1 = MGCntry$MGCnty_C1cl2_1CHet.out$warnings,
C2 = MGCntry$MGCnty_C2cl2_1CHet.out$warnings,
C3 = MGCntry$MGCnty_C3cl2_1CHet.out$warnings)
cat('\n')
cat('\n')
wr <- names(warn_MGCntry[grep("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED",warn_MGCntry)])
if (length(wr) == 0) print("ALL BEST LOGLIKELIHOOD VALUES WERE REPLICATED") else
cat("THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED for the following cases: \n", wr)
cat('\n')
cat('\n')
#----C1
cnt1 <- sort(unique(ISC_lvRlca[ISC_lvRlca$cycle == "C1", c("COUNTRY")])$COUNTRY)
MGCnty_C1cl2 <- MGCntry$MGCnty_C1cl2_1CHet.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = 1:length(cnt1), labels = cnt1),
ClassN = factor(case_when(
#-----
Class == "1.1" ~ 2, #BGR
Class == "1.2" ~ 1,
Class == "2.1" ~ 1, #CHL
Class == "2.2" ~ 2,
Class == "3.1" ~ 2, #COL
Class == "3.2" ~ 1,
Class == "4.1" ~ 2, #DNK
Class == "4.2" ~ 1,
Class == "5.1" ~ 1, #EST
Class == "5.2" ~ 2,
Class == "6.1" ~ 2, #FIN
Class == "6.2" ~ 1,
Class == "7.1" ~ 1, #HKG
Class == "7.2" ~ 2,
Class == "8.1" ~ 1, #ITA
Class == "8.2" ~ 2,
Class == "9.1" ~ 1, #LTU
Class == "9.2" ~ 2,
Class == "10.1" ~ 1, #LVA
Class == "10.2" ~ 2,
Class == "11.1" ~ 1, #NOR
Class == "11.2" ~ 2,
Class == "12.1" ~ 2, #RUS
Class == "12.2" ~ 1,
Class == "13.1" ~ 1, #SVN
Class == "13.2" ~ 2,
Class == "14.1" ~ 1, #SWE
Class == "14.2" ~ 2
#-----
), levels = c("1", "2"), labels = classes2M2))
#----C2
cnt2 <- sort(unique(ISC_lvRlca[ISC_lvRlca$cycle == "C2", c("COUNTRY")])$COUNTRY)
MGCnty_C2cl2 <- MGCntry$MGCnty_C2cl2_1CHet.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x))%>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = 1:length(cnt2), labels = cnt2),
ClassN = factor(case_when(
#-----
Class == "1.1" ~ 1, #BFL
Class == "1.2" ~ 2,
Class == "2.1" ~ 2, #BGR
Class == "2.2" ~ 1,
Class == "3.1" ~ 1, #CHL
Class == "3.2" ~ 2,
Class == "4.1" ~ 1, #COL
Class == "4.2" ~ 2,
Class == "5.1" ~ 1, #DNK \\\SPECIAL CLASS 3
Class == "5.2" ~ 2,
Class == "6.1" ~ 1, #DOM
Class == "6.2" ~ 2,
Class == "7.1" ~ 1, #EST
Class == "7.2" ~ 2,
Class == "8.1" ~ 1, #FIN
Class == "8.2" ~ 2,
Class == "9.1" ~ 1, #HKG
Class == "9.2" ~ 2,
Class == "10.1" ~ 2, #ITA
Class == "10.2" ~ 1,
Class == "11.1" ~ 2, #KOR
Class == "11.2" ~ 1,
Class == "12.1" ~ 1, #LTU
Class == "12.2" ~ 2,
Class == "13.1" ~ 1, #LVA
Class == "13.2" ~ 2,
Class == "14.1" ~ 1, #MEX
Class == "14.2" ~ 2,
Class == "15.1" ~ 2, #MLT
Class == "15.2" ~ 1,
Class == "16.1" ~ 2, #NLD
Class == "16.2" ~ 1,
Class == "17.1" ~ 1, #NOR
Class == "17.2" ~ 2,
Class == "18.1" ~ 1, #RUS
Class == "18.2" ~ 2,
Class == "19.1" ~ 1, #SVN
Class == "19.2" ~ 2,
Class == "20.1" ~ 1, #SWE
Class == "20.2" ~ 2,
Class == "21.1" ~ 2, #TWN
Class == "21.2" ~ 1
#-----
), levels = c("1", "2"), labels = classes2M2))
#----C3
cnt3 <- sort(unique(ISC_lvRlca[ISC_lvRlca$cycle == "C3", c("COUNTRY")])$COUNTRY)
MGCnty_C3cl2 <- MGCntry$MGCnty_C3cl2_1CHet.out$parameters$probability.scale %>%
rename_with(~ c("Class", "value")[which(c("LatentClass", "est") == .x)], .cols = c("LatentClass", "est")) %>%
mutate_at( c("param", "category", "Class"), ~ as.factor(.x)) %>%
mutate(Group = factor(sub('\\..*', '', Class),
levels = 1:length(cnt3), labels = cnt3),
ClassN = factor(case_when(
#-----
Class == "1.1" ~ 1, #BFL
Class == "1.2" ~ 2,
Class == "2.1" ~ 2, #BGR
Class == "2.2" ~ 1,
Class == "3.1" ~ 2, #CHL
Class == "3.2" ~ 1,
Class == "4.1" ~ 2, #COL
Class == "4.2" ~ 1,
Class == "5.1" ~ 2, #DNK
Class == "5.2" ~ 1,
Class == "6.1" ~ 2, #DOM
Class == "6.2" ~ 1,
Class == "7.1" ~ 2, #EST
Class == "7.2" ~ 1,
Class == "8.1" ~ 1, #FIN
Class == "8.2" ~ 2,
Class == "9.1" ~ 1, #HKG
Class == "9.2" ~ 2,
Class == "10.1" ~ 2, #ITA
Class == "10.2" ~ 1,
Class == "11.1" ~ 1, #KOR
Class == "11.2" ~ 2,
Class == "12.1" ~ 1, #LTU
Class == "12.2" ~ 2,
Class == "13.1" ~ 1, #LVA
Class == "13.2" ~ 2,
Class == "14.1" ~ 1, #MEX
Class == "14.2" ~ 2,
Class == "15.1" ~ 2, #MLT
Class == "15.2" ~ 1,
Class == "16.1" ~ 2, #NLD
Class == "16.2" ~ 1,
Class == "17.1" ~ 2, #NOR
Class == "17.2" ~ 1,
Class == "18.1" ~ 1, #RUS
Class == "18.2" ~ 2,
Class == "19.1" ~ 1, #SVN
Class == "19.2" ~ 2,
Class == "20.1" ~ 1, #SWE
Class == "20.2" ~ 2,
Class == "21.1" ~ 2, #TWN
Class == "21.2" ~ 1
#-----
), levels = c("1", "2"), labels = classes2M2))
MGCnty3 <- rbind(#cbind(Cycle = "1999", MGCnty_C1cl2),
cbind(Cycle = "2009", MGCnty_C2cl2),
cbind(Cycle = "2016", MGCnty_C3cl2))
#---
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[c(1,6)],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[2:3],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[4:5],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[7:8],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[9:10],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[12:13],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[c(11,16,21)],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[14:15],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[17:18],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
graphclass(MGCnty3[MGCnty3$Group %in% cnt3[19:20],], nclass = 2, orden = c(5,2,1,3,4,6) ,title = "MG Country Complete Heterogeneity")
lcicn <- VarClass(MGCnty3)
lcicn %>% group_by(Cycle, ClassN, Group, param) %>% filter(category == 2) %>%
select(Cycle, param, ClassN, Group, value) %>%
mutate(value = cell_spec(value, color = ifelse(value > 0.8, "blue", "red"))) %>%
reshape2::dcast(Group + param + Cycle ~ ClassN) %>% arrange(as.character(Group)) %>%
kbl(caption = "Probabilities to Agree each item by Class and Country Complete Heterogeneity", booktabs = TRUE, longtable = TRUE,
align = "llrrr", row.names = FALSE, digits = 3, col.names = c("Country", "Item", "Year", classes2M2), escape = FALSE) %>%
kable_styling(bootstrap_options = c("bordered", "hover"), latex_options = c("repeat_header", "HOLD_position"),
position = "left", font_size = 11, full_width = FALSE) %>%
column_spec(2, width = "18em") %>%
column_spec(3, width = "2em") %>%
column_spec(4:5, width = "4em") %>%
collapse_rows(c(1,2), valign = "top") %>%
print()
cat('\n')
cat('\n')