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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", "prop"),
   method = c("naive", "BCH", "ML"), ...
)

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

Arguments

object

an object of class slcafit.

...

additional arguments.

formula

a formula specifying the regression model. The left-hand side must be a single latent class variable from the estimated model.

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.

  • "prop": 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. The standard errors are derived by numerically calculated Hessian matrix from nlm function.

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   
# }