slca (version 1.2.0)

regress: Regress Exogenous Variables on Latent Variables

Description

This function performs regression analysis to explore the influence of exogenous (external) variables on the latent class variables within an estimated slca model. It utilizes logistic regression and employs a three-step approach.

Usage

regress(object, ...)
# S3 method for slca
regress(
   object, formula, data = parent.frame(),
   imputation = c("modal", "prob"),
   method = c("naive", "BCH", "ML"), ...
)

Value

A list with following components:

coefficients

a matrix of regression coefficients representing the odds ratios of each class against the baseline class (the last class).

std.err

a matrix of standard errors corresponding to the regression coefficients.

vcov

the calculated variance-covariance matrix for the regression coefficients.

dim

the dimensions of the coefficients matrix.

ll

the log likelihood of the regression model.

Using the summary function, you can print coefficients, standard errors, corresponding Wald statistics, and p-values for these statistics.

Arguments

object

an object of class slca and estimated

...

additional arguments.

formula

a formula defining the regression model, including both latent class variables from the estimated model and any exogenous (external) variables.

data

an optional data frame containing the exogenous variables of interest.

imputation

the imputation method for imputing (assigning) latent class variables. Possible values are:

• "modal": Assigns each individual to the latent class with the highest posterior probability.

• "prob": Assigns classes to individuals randomly according to the distribution of posterior probabilities.

method

the method used to adjust bias in the three-step approach, with options including "naive", "BCH", and "ML".

References

Vermunt, J. K. (2010). Latent Class Modeling with Covariates: Two Improved Three-Step Approaches. Political Analysis, 18(4), 450–469. http://www.jstor.org/stable/25792024

Examples

library(magrittr)
names(nlsy97)
nlsy_jlcpa %>% regress(SMK_98 ~ SEX, nlsy97)
# \donttest{
nlsy_jlcpa %>% regress(SUB_98 ~ SEX, nlsy97)
nlsy_jlcpa %>% regress(PROF ~ SEX, nlsy97)
# }