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Regress Exogenous Variables on Latent Variables

Source: R/regress.R
regress.Rd

Performs regression analysis to examine the influence of exogenous (external) variables on latent class variables in an estimated slca model. The function uses logistic regression with a three-step approach to account for measurement error.

Usage

regress(object, ...)

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

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

Arguments

object

an object of class slcafit.

...

additional arguments.

formula

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

data

an optional data.frame containing the exogenous variables of interest. If omitted, the variables are taken from the parent environment.

imputation

a character string specifying the imputation method for latent class assignment. Options include:

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

  • "prob": Assigns classes probabilistically based on the posterior probability distribution.

method

a character string specifying the method to adjust for bias in the three-step approach. Options include:

  • "naive": A simple approach without correction for classification error.

  • "BCH": The bias-adjusted Bolck, Croon, and Hagenaars method.

  • "ML": A maximum likelihood approach that accounts for classification error.

Value

A list of class reg.slca with the following components:

coefficients

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

std.err

A matrix of standard errors corresponding to the regression coefficients.

vcov

The variance-covariance matrix of the regression coefficients.

dim

The dimensions of the coefficients matrix.

ll

The log-likelihood of the regression model.

The summary function can be used to display the regression coefficients, standard errors, Wald statistics, and p-values.

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)
#>  [1] "SEX"     "RACE"    "ESMK_98" "FSMK_98" "DSMK_98" "HSMK_98" "EDRK_98"
#>  [8] "CDRK_98" "WDRK_98" "BDRK_98" "EMRJ_98" "CMRJ_98" "OMRJ_98" "SMRJ_98"
#> [15] "ESMK_03" "FSMK_03" "DSMK_03" "HSMK_03" "EDRK_03" "CDRK_03" "WDRK_03"
#> [22] "BDRK_03" "EMRJ_03" "CMRJ_03" "OMRJ_03" "SMRJ_03" "ESMK_08" "FSMK_08"
#> [29] "DSMK_08" "HSMK_08" "EDRK_08" "CDRK_08" "WDRK_08" "BDRK_08" "EMRJ_08"
#> [36] "CMRJ_08" "OMRJ_08" "SMRJ_08"
nlsy_jlcpa %>% regress(SMK_98 ~ SEX, nlsy97)
#> Coefficients:     
#> class  (Intercept)  SEXFemale
#>   1/3   0.3983      -0.4445  
#>   2/3  -0.6614       0.0804  
# \donttest{
nlsy_jlcpa %>% regress(PROF ~ SEX, nlsy97)
#> Coefficients:     
#> class  (Intercept)  SEXFemale
#>   1/4   0.436       -0.106   
#>   2/4   0.157       -0.210   
#>   3/4   0.443       -0.221   
# }