Aliases: tidy.poLCA poLCA_tidiers
Keywords:
### ** Examples # feel free to ignore the following lineāit allows {broom} to supply # examples without requiring the model-supplying package to be installed. if (requireNamespace("poLCA", quietly = TRUE)) { # load libraries for models and data library(poLCA) library(dplyr) # generate data data(values) f <- cbind(A, B, C, D) ~ 1 # fit model M1 <- poLCA(f, values, nclass = 2, verbose = FALSE) M1 # summarize model fit with tidiers + visualization tidy(M1) augment(M1) glance(M1) library(ggplot2) ggplot(tidy(M1), aes(factor(class), estimate, fill = factor(outcome))) + geom_bar(stat = "identity", width = 1) + facet_wrap(~variable) # three-class model with a single covariate. data(election) f2a <- cbind( MORALG, CARESG, KNOWG, LEADG, DISHONG, INTELG, MORALB, CARESB, KNOWB, LEADB, DISHONB, INTELB ) ~ PARTY nes2a <- poLCA(f2a, election, nclass = 3, nrep = 5, verbose = FALSE) td <- tidy(nes2a) td ggplot(td, aes(outcome, estimate, color = factor(class), group = class)) + geom_line() + facet_wrap(~variable, nrow = 2) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) au <- augment(nes2a) au count(au, .class) # if the original data is provided, it leads to NAs in new columns # for rows that weren't predicted au2 <- augment(nes2a, data = election) au2 dim(au2) }