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 poLCA
tidy(x, ...)
A poLCA
object returned from poLCA::poLCA()
.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
Other poLCA tidiers:
augment.poLCA()
,
glance.poLCA()
A tibble::tibble()
with columns:
The class under consideration.
Outcome of manifest variable.
The standard error of the regression term.
Manifest variable
Estimated class-conditional response probability
# feel free to ignore the following line—it 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)
}
#> Warning: 'as.is' should be specified by the caller; using TRUE
#> Warning: 'as.is' should be specified by the caller; using TRUE
#> Error in cbind(MORALG, CARESG, KNOWG, LEADG, DISHONG, INTELG, MORALB, CARESB, KNOWB, LEADB, DISHONB, INTELB): object 'MORALG' not found