broom (version 0.7.5)

tidy.poLCA: Tidy a(n) poLCA object

Description

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.

Usage

# S3 method for poLCA
tidy(x, ...)

Arguments

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.level = 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.

Value

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

See Also

tidy(), poLCA::poLCA()

Other poLCA tidiers: augment.poLCA(), glance.poLCA()

Examples

# NOT RUN {
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)
# }