Tidy a(n) poLCA object
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'poLCA' tidy(x, ...)
x |
A |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble() with columns:
class |
The class under consideration. |
outcome |
Outcome of manifest variable. |
std.error |
The standard error of the regression term. |
variable |
Manifest variable |
estimate |
Estimated class-conditional response probability |
Other poLCA tidiers:
augment.poLCA(),
glance.poLCA()
if (requireNamespace("poLCA", quietly = TRUE)) {
library(poLCA)
library(dplyr)
data(values)
f <- cbind(A, B, C, D) ~ 1
M1 <- poLCA(f, values, nclass = 2, verbose = FALSE)
M1
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
# show
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)
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