glca (version 0.1.0)

glca: Fits Latent Class Models for Data Containing Group Variable and Covariates

Description

Function for fitting latent class models with multiple groups, which may or may not include latent class structure for group variable.

Usage

glca(formula, group = NULL, data = NULL, nclass = 3, ncluster = 0,
  measure.inv = TRUE, std.err = TRUE, init.param = NULL,
  n.init = 1, testiter = 50, maxiter = 1000, eps = 1e-06,
  na.rm = FALSE, random.seed = NULL, verbose = TRUE)

Arguments

formula

a formula for specifying manifest items and covariates using the "item" function

group

an optional vector specifying a group of observations. Given group variable, group covariates can be incorporated.

data

a data frame containing the manifest item, covariates and group variable

nclass

number of level-1 (individual-level) latent classes

ncluster

number of level-2 (group-level) latent classes. When group and ncluster (>1) are given the multilevel latent class models will be fitted.

measure.inv

a logical value of the measurement invariance assumption across groups

std.err

a logical value for whether calculating standard errors for estimates

init.param

a list which contains user-defined initial parameter

n.init

number of random initial parameter sets

testiter

an integer for maximum number of test set iteration

maxiter

an integer for maximum number of iteration

eps

positive convergence tolerance

na.rm

a logical value for whether or not to remove observations who has at least 1 item missing

random.seed

random seed to have the equivalent solution for every trials

verbose

a logical value for whether or not to print the result of a function's execution

Value

glca returns an object of class "glca".

The function summary prints estimates for parameters and glca.gof function gives goodness of fit measures for the model.

An object of class "glca" is a list containing the following components:

call

the matched call

model

a list of model description

datalist

a list of data used for fitting

param

a list of parameter estimates

std.err

a list of standard errors for estimates

coefficient

a list of logistic regression coefficients for prevalence of level-1 class

posterior

a data frame of posterior probablities of each individaul for latent classes and each group for latent clusters

gof

a list of goodness of fit measures

convergence

a list containing information about convergence

Details

The glca is the function for implementing LCA consist of two-type latent categorical variables (i.e., level-1 and level-2 latent class). The level-1 (individual-level) latent class is identified by the association among the individuals' responses to multiple manifest items, but level-2 (group-level) latent class is categorized by the prevalence of level-1 latent class for group variable. The function glca can handle two types of covariates: level-1 and level-2 covariates. If covariates vary across individuals, they are considered as level-1 covariates. When group and ncluster (>1) are given, covariates which are varying across groups are considered as level-2 covariates. Both types of covariates have effect on level-1 class prevalence.

The formula should consist of an ~ operator between two sides. Manifest items should be indicated in LHS of formula using item function and covariates should be specified in RHS of formula. For example, item(y1, y2, y3) ~ 1 item(y1, y2, y3) ~ x1 + x2 where the first fomula indicates LCA with three manifest variables (y1, y2, and y3) and no covariate, and the second formula includes two covariates (x1 and x2). Two types of covariates (i.e., level-1 and level-2 covariates) will be automatically detected by glca.

The estimated parameters in glca are rho, gamma, delta, and beta. The set of item response probabilities for each level-1 class is rho. The sets of prevalences for level-1 and level-2 class are gamma and delta, respectively. The prevalence for level-1 class (i.e., gamma) can be modeled as logistic regression using level-1 and/or level-2 covariates. The set of logistic regression coefficients is beta in glca output.

References

Vermunt, J.K. (2003) Multilevel latent class models. Sociological Methodology, 33, 213--239. 10.1111/j.0081-1750.2003.t01-1-00131.x

Collins, L.M. and Lanza, S.T. (2009) Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. John Wiley & Sons Inc.

See Also

gss brfss

Examples

# NOT RUN {
##
## Example 1. GSS dataset
##
data("gss")
# LCA
lca = glca(item(ABDEFECT, ABHLTH, ABRAPE, ABPOOR, ABSINGLE) ~ 1,
            data = gss, nclass = 3)
summary(lca)

# LCA with covariate(s)
lcr = glca(item(ABDEFECT, ABHLTH, ABRAPE, ABPOOR, ABSINGLE) ~ AGE,
           data = gss, nclass = 3)
summary(lcr)
coef(lcr)

# Multiple-group LCA (MGLCA)
mglca = glca(item(ABDEFECT, ABHLTH, ABRAPE, ABPOOR, ABSINGLE) ~ 1,
             group = DEGREE, data = gss, nclass = 3)
summary(mglca)


# Multiple-group LCA with covariate(s) (MGLCR)
mglcr = glca(item(ABDEFECT, ABHLTH, ABRAPE, ABPOOR, ABSINGLE) ~ SEX,
             group = DEGREE, data = gss, nclass = 3)
summary(mglcr)
coef(mglcr)

# }
# NOT RUN {
##
## Example 2. BRFSS data
##
data("brfss")
# Multilevel LCA (MLCA)
brfss1000 = brfss[sample(1:nrow(brfss), 1000),]
mlca = glca(item(OBESE, PA300, FRTLT1A, VEGLT1A, SMOKER, DRNK30) ~ 1,
            group = STATE, data = brfss1000, nclass = 3, ncluster = 2)
summary(mlca)

# MLCA with covariate(s) (MLCR)
# (SEX: level-1 covariate, REGION: level-2 covariate)
mlcr = glca(item(OBESE, PA300, FRTLT1A, VEGLT1A, SMOKER, DRNK30) ~ SEX + PARTY,
            group = STATE, data = brfss1000, nclass = 3, ncluster = 2)
summary(mlcr)
coef(mlcr)
# }
# NOT RUN {
# }