Package 'glca' reference manual

Title:	An R Package for Multiple-Group Latent Class Analysis
Description:	Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).
Authors:	Youngsun Kim [aut, cre], Hwan Chung [aut]
Maintainer:	Youngsun Kim <[email protected]>
License:	GPL-3
Version:	1.4.2
Built:	2025-01-24 04:24:37 UTC
Source:	https://github.com/kim0sun/glca

An R Package for Multiple-Group Latent Class Analysis

Description

Fits latent class analysis (LCA) including group variable and covariates. The group variable can be handled either by multilevel LCA described in Vermunt (2003) <DOI:10.1111/j.0081-1750.2003.t01-1-00131.x> or standard LCA at each level of group variable. The covariates can be incorporated in the form of logistic regression (Bandeen-Roche et al. (1997) <DOI:10.1080/01621459.1997.10473658>).

Extracts `glca` Model Coefficients

Description

Extracts regression coefficients of glca model if the model includes covariates.

Usage

## S3 method for class 'glca'
coef(
  object,
  intercept = FALSE,
  digits = max(3, getOption("digits") - 3),
  show.signif.stars = getOption("show.signif.stars"),
  ...
)
## S3 method for class 'glca'
coef(
  object,
  intercept = FALSE,
  digits = max(3, getOption("digits") - 3),
  show.signif.stars = getOption("show.signif.stars"),
  ...
)

Arguments

`object`	an object of "`glca`".
`intercept`	a logical value for whether to print intercept".
`digits`	number of significant digits to use when printing.
`show.signif.stars`	logical. If TRUE, ‘significance stars’ are printed for each coefficient.
`...`	further arguments passed to or from other methods.

Value

Coefficient matrix from the glca model

If the model has calculated standard errors, coefficient matrix contains standard errors, t-statistic, and its p-value.

Examples

## For examples see example(glca)

## For examples see example(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 = NULL,
  std.err = TRUE,
  measure.inv = TRUE,
  coeff.inv = TRUE,
  init.param = NULL,
  n.init = 10,
  decreasing = FALSE,
  testiter = 50,
  maxiter = 5000,
  eps = 1e-06,
  na.rm = FALSE,
  seed = NULL,
  verbose = TRUE
)
glca(
  formula,
  group = NULL,
  data = NULL,
  nclass = 3,
  ncluster = NULL,
  std.err = TRUE,
  measure.inv = TRUE,
  coeff.inv = TRUE,
  init.param = NULL,
  n.init = 10,
  decreasing = FALSE,
  testiter = 50,
  maxiter = 5000,
  eps = 1e-06,
  na.rm = FALSE,
  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.
`std.err`	a logical value for whether calculating standard errors for estimates.
`measure.inv`	a logical value of the measurement invariance assumption across groups.
`coeff.inv`	a logical value of the coefficient invariance assumption across groups (random intercept model).
`init.param`	A set of model parameters to be used as the user-defined initial values for the EM algorithm. It should be `list` with the named parameters and have same structure of `param` of the `glca` output. In default, initial parameters are randomly generated.
`n.init`	number of randomly generated initial parameter sets to be used for avoiding the problem of local maxima.
`decreasing`	a logical value for whether reordering the parameters by descending order responding probability for first-category of first manifest item.
`testiter`	number of iterations in the EM algorithm for each initial parameter set. The initial parameter set that provides the largest log-likelihood will be selected for estimating the model.
`maxiter`	maximum number of iterations for the EM algorithm.
`eps`	a convergence tolerance value. When the largest absolute difference between former estimates and current estimates is less than `eps`, the algorithm will stop updating and consider the convergence to be reached.
`na.rm`	a logical value for deleting the lines that have at least one missing manifest item. If `na.rm = FALSE`, MAR procedure will be conducted.
`seed`	In default, the set of initial parameters is drawn randomly. As the same value for seed guarantees the same initial parameters to be drawn, this argument can be used for reproducibility of estimation results.
`verbose`	a logical value indicating whether `glca` should print the estimation procedure onto the screen.

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.

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.
`terms`	the `terms` object used.
`model`	a `list` of model description.
`var.names`	a `list` of names of data.
`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` or a `list` 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.

References

Vermunt, J.K. (2003) Multilevel latent class models. Sociological Methodology, 33, 213–239. doi: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.

Examples

##
## Example 1. GSS dataset
##
data("gss08")
# LCA
lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
            data = gss08, nclass = 3, n.init = 1)
summary(lca)

# LCA with covariate(s)
lcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ AGE,
           data = gss08, nclass = 3, n.init = 1)
summary(lcr)
coef(lcr)


# Multiple-group LCA (MGLCA)
mglca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = DEGREE, data = gss08, nclass = 3, n.init = 1)
summary(mglca)

# Multiple-group LCA with covariate(s) (MGLCR)
mglcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ SEX,
             group = DEGREE, data = gss08, nclass = 3, n.init = 1)
summary(mglcr)
coef(mglcr)


##
## Example 2. NYTS dataset
##
data("nyts18")
# Multilevel LCA (MLCA)
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2, n.init = 1)
summary(mlca)

# MLCA with covariate(s) (MLCR)
# (SEX: level-1 covariate, SCH_LEV: level-2 covariate)
mlcr = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX + SCH_LEV,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2, n.init = 1)
coef(mlcr)


##
## Example 1. GSS dataset
##
data("gss08")
# LCA
lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
            data = gss08, nclass = 3, n.init = 1)
summary(lca)

# LCA with covariate(s)
lcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ AGE,
           data = gss08, nclass = 3, n.init = 1)
summary(lcr)
coef(lcr)


# Multiple-group LCA (MGLCA)
mglca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = DEGREE, data = gss08, nclass = 3, n.init = 1)
summary(mglca)

# Multiple-group LCA with covariate(s) (MGLCR)
mglcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ SEX,
             group = DEGREE, data = gss08, nclass = 3, n.init = 1)
summary(mglcr)
coef(mglcr)


##
## Example 2. NYTS dataset
##
data("nyts18")
# Multilevel LCA (MLCA)
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2, n.init = 1)
summary(mlca)

# MLCA with covariate(s) (MLCR)
# (SEX: level-1 covariate, SCH_LEV: level-2 covariate)
mlcr = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX + SCH_LEV,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2, n.init = 1)
coef(mlcr)

Goodness of Fit Tests for Fitted `glca` Model

Description

Provides AIC, BIC, entropy and deviance statitistic for goodness of fit test for the fitted model. Given object2, the function computes the log-likelihood ratio (LRT) statisic for comparing the goodness of fit for two models. The bootstrap p-value can be obtained from the empirical distribution of LRT statistic by choosing test = "boot".

Usage

gofglca(
  object,
  ...,
  test = NULL,
  nboot = 50,
  criteria = c("AIC", "BIC", "entropy"),
  maxiter = 500,
  eps = 1e-04,
  seed = NULL,
  verbose = FALSE
)
gofglca(
  object,
  ...,
  test = NULL,
  nboot = 50,
  criteria = c("AIC", "BIC", "entropy"),
  maxiter = 500,
  eps = 1e-04,
  seed = NULL,
  verbose = FALSE
)

Arguments

`object`	an object of "`glca`", usually, a result of a call to `glca`.
`...`	an optional object of "`glca`" to be compared with `object`.
`test`	a character string indicating type of test (chi-square test or bootstrap) to obtain the p-value for goodness of fit test (`"chisq"` or `"boot"`).
`nboot`	number of bootstrap samples, only used when `test = "boot"`.
`criteria`	a character vector indicating criteria to be printed.
`maxiter`	an integer for maximum number of iteration for bootstrap sample.
`eps`	positive convergence tolerance for bootstrap sample.
`seed`	As the same value for seed guarantees the same datasets to be generated, this argument can be used for reproducibility of bootstrap results.
`verbose`	an logical value for whether or not to print the result of a function's execution.

Value

`gtable`	a matrix with model goodneess-of-fit criteria
`dtable`	a matrix with deviance statistic and bootstrap p-value
`boot`	a list of LRT statistics from each bootstrap sample

gtable, which is always included in output of this function, includes goodness-of-fit criteria which are indicated criteria arguments for the object(s). dtable are contained when the objects are competing models. (when used items of the models are identical) dtable prints deviance and p-value. (bootstrap or chi-square) Lastly, when the boostrap sample is used, the G^2-statistics for each bootstrap samples will be included in return object..

References

Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723. doi:10.1109/tac.1974.1100705

Schwarz, G. (1978) Estimating the dimensions of a model. The Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136

Langeheine, R., Pannekoek, J., and van de Pol, F. (1996) Bootstrapping goodness-of-fit measures in categorical data analysis. Sociological Methods and Research. 24. 492-516. doi:10.1177/0049124196024004004

Ramaswamy, V., Desarbo, W., Reibstein, D., & Robinson, W. (1993). An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data. Marketing Science, 12(1), 103-124. doi:10.1287/mksc.12.1.103

Examples

## Example 1.
## Model selection between two LCA models with different number of latent classes.
data(gss08)
class2 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
              data = gss08, nclass = 2,  n.init = 1)
class3 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
              data = gss08, nclass = 3,  n.init = 1)
class4 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
              data = gss08, nclass = 4,  n.init = 1)

gofglca(class2, class3, class4)
## Not run: gofglca(class2, class3, class4, test = "boot")

## Example 2.
## Model selection between two MLCA models with different number of latent clusters.
cluster2 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
                group = SCH_ID, data = nyts18, nclass = 2, ncluster = 2, n.init = 1)
cluster3 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
                group = SCH_ID, data = nyts18, nclass = 2, ncluster = 3, n.init = 1)

gofglca(cluster2, cluster3)
## Not run: gofglca(cluster2, cluster3, test = "boot")

## Example 3.
## MGLCA model selection under the measurement (invariance) assumption across groups.
measInv = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
               group = DEGREE, data = gss08, nclass = 3, n.init = 1)
measVar = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
               group = DEGREE, data = gss08, nclass = 3, n.init = 1, measure.inv = FALSE)

gofglca(measInv, measVar)


## Example 1.
## Model selection between two LCA models with different number of latent classes.
data(gss08)
class2 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
              data = gss08, nclass = 2,  n.init = 1)
class3 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
              data = gss08, nclass = 3,  n.init = 1)
class4 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
              data = gss08, nclass = 4,  n.init = 1)

gofglca(class2, class3, class4)
## Not run: gofglca(class2, class3, class4, test = "boot")

## Example 2.
## Model selection between two MLCA models with different number of latent clusters.
cluster2 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
                group = SCH_ID, data = nyts18, nclass = 2, ncluster = 2, n.init = 1)
cluster3 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
                group = SCH_ID, data = nyts18, nclass = 2, ncluster = 3, n.init = 1)

gofglca(cluster2, cluster3)
## Not run: gofglca(cluster2, cluster3, test = "boot")

## Example 3.
## MGLCA model selection under the measurement (invariance) assumption across groups.
measInv = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
               group = DEGREE, data = gss08, nclass = 3, n.init = 1)
measVar = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
               group = DEGREE, data = gss08, nclass = 3, n.init = 1, measure.inv = FALSE)

gofglca(measInv, measVar)

General Social Study (GSS) 2008

Description

This dataset includes 6 manifest items about abortion and several covariates from 355 respondents to the 2008 General Social Survey. Respondents answer the questions whether or not think it should be possible for a pregnant woman to obtain a legal abortion. The covariates include age, sex, race, region, and degree of respondents.

Format

A data frame with 355 observations on 11 variables.

DEFECT: If there is a strong chance of serious defect in the baby?
HLTH: If the womans own health is seriously endangered by the pregnancy?
RAPE: If she became pregnant as a result of rape?
POOR: If the family has a very low income and cannot afford any more children?
SINGLE: If she is not married and does not want to marry the man?
NOMORE: If she is married and does not want any more children?
AGE: Respondent's age
SEX: Respondent's race
RACE: Respondent's sex
REGION: Region of interview
DEGREE: Respondent's degree

Source

https://gss.norc.org/

References

Smith, Tom W, Peter Marsden, Michael Hout, and Jibum Kim. General Social Surveys, 2008/Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science Foundation. -NORC ed.- Chicago: NORC at the University of Chicago

Examples

data("gss08")
# Model 1: LCA
lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
           data = gss08, nclass = 3)
summary(lca)

# Model 2: LCA with a covariate
lcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ SEX,
           data = gss08, nclass = 3)
summary(lcr)
coef(lcr)

# Model 3: MGLCA
mglca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = REGION, data = gss08, nclass = 3)

# Model 4: MGLCA with covariates
summary(mglca)
mglcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ AGE,
             group = SEX, data = gss08, nclass = 3)
summary(mglcr)
coef(mglcr)
data("gss08")
# Model 1: LCA
lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
           data = gss08, nclass = 3)
summary(lca)

# Model 2: LCA with a covariate
lcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ SEX,
           data = gss08, nclass = 3)
summary(lcr)
coef(lcr)

# Model 3: MGLCA
mglca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = REGION, data = gss08, nclass = 3)

# Model 4: MGLCA with covariates
summary(mglca)
mglcr = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ AGE,
             group = SEX, data = gss08, nclass = 3)
summary(mglcr)
coef(mglcr)

Specifies Manifest Items for `glca`

Description

Specifying manifest items in formula of glca function.

Usage

item(..., starts.with = NULL, ends.with = NULL)
item(..., starts.with = NULL, ends.with = NULL)

Arguments

`...`	vectors of manifest items. These can be given as named arguments which is colnames of `data.frame`.
`starts.with`	a string for prefix of variable names to be selected.
`ends.with`	a string for suffix of variable names to be selected.

Value

a matrix of specified variables, which contains names and levels of manifest items.

Examples

## For examples see example(glca)

## For examples see example(glca)

National Youth Tobacco Survey (NYTS) 2018

Description

This dataset includes 5 manifest items about abortion and several covariates. From the original 2018 National Youth Tobacco Survey data, the Non Hispanic, white students are selected and schools with 30-50 students were selected. Thus, the dataset has 1743 respondents. The covariates include the sex of the respondents and the school ID to which the respondnets belong, and the level of the corresponding school.

Format

A data frame with 1734 observations on the following 8 variables.

ECIGT: Whether to have tried cigarette smoking, even one or two puffs
ECIGAR: Whether to have ever tried cigar smoking, even one or two puffs
ESLT: Whether to have used chewing tobacco, snuff, or dip
EELCIGT: Whether to have used electronic cigarettes or e-cigarettes
EHOOKAH: Whether to have tried smoking tobacco from a hookah or a waterpipe
SEX: Respondent's Sex
SCH_ID: School ID to which the respondent belongs
SCH_LEV: Level of the corresponding school

Source

https://www.cdc.gov/tobacco/

Examples

data("nyts18")

# Model 1: LCA
lca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
           data = nyts18, nclass = 3)
summary(lca)

# Model 2: LCR
lca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX,
           data = nyts18, nclass = 3)
summary(lca)
coef(lca)

# Model 3: MGLCA
mglca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
             group = SEX, data = nyts18, nclass = 3)
summary(mglca)

# Model 4: MLCA
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
   group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
summary(mlca)

# Model 5: MLCA with level-1 covariate(s) only
mlcr = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
summary(mlcr)
coef(mlcr)

# Model 6: MLCA with level-1 and level-2 covariate(s)
# (SEX: level-1 covariate, PARTY: level-2 covariate)
mlcr2 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX + SCH_LEV,
             group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
summary(mlcr2)
coef(mlcr2)

data("nyts18")

# Model 1: LCA
lca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
           data = nyts18, nclass = 3)
summary(lca)

# Model 2: LCR
lca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX,
           data = nyts18, nclass = 3)
summary(lca)
coef(lca)

# Model 3: MGLCA
mglca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
             group = SEX, data = nyts18, nclass = 3)
summary(mglca)

# Model 4: MLCA
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
   group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
summary(mlca)

# Model 5: MLCA with level-1 covariate(s) only
mlcr = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
summary(mlcr)
coef(mlcr)

# Model 6: MLCA with level-1 and level-2 covariate(s)
# (SEX: level-1 covariate, PARTY: level-2 covariate)
mlcr2 = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ SEX + SCH_LEV,
             group = SCH_ID, data = nyts18, nclass = 3, ncluster = 2)
summary(mlcr2)
coef(mlcr2)

Plots the Estimated Parameters of Fitted glca Model

Description

plot method for class "glca".

Usage

## S3 method for class 'glca'
plot(x, ask = TRUE, ...)
## S3 method for class 'glca'
plot(x, ask = TRUE, ...)

Arguments

`x`	an object of "`glca`", usually, a result of a call to `glca`.
`ask`	a logical value whether to be asked before printing each plot.
`...`	further arguments passed to or from other methods.

Value

This function plots estimated parameters of model.

Examples

## Not run: 
# LCA
lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
            data = gss08, nclass = 3, na.rm = TRUE)
plot(lca)

# Multitple Group LCA (MGLCA)
mglca1 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = DEGREE, data = gss08, nclass = 3)
plot(mglca1)

# Multitple Group LCA (MGLCA) (measure.inv = FALSE)
mglca2 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = DEGREE, data = gss08, nclass = 3, measure.inv = FALSE)
plot(mglca2)
plot(mglca2, "all")

# Multilvel LCA (MLCA)
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 3)
plot(mlca)

## End(Not run)

## Not run: 
# LCA
lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
            data = gss08, nclass = 3, na.rm = TRUE)
plot(lca)

# Multitple Group LCA (MGLCA)
mglca1 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = DEGREE, data = gss08, nclass = 3)
plot(mglca1)

# Multitple Group LCA (MGLCA) (measure.inv = FALSE)
mglca2 = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
             group = DEGREE, data = gss08, nclass = 3, measure.inv = FALSE)
plot(mglca2)
plot(mglca2, "all")

# Multilvel LCA (MLCA)
mlca = glca(item(ECIGT, ECIGAR, ESLT, EELCIGT, EHOOKAH) ~ 1,
            group = SCH_ID, data = nyts18, nclass = 3, ncluster = 3)
plot(mlca)

## End(Not run)

Reorders the estimated parameters of glca model

Description

Function for reordering the estimated parameters for glca model.

Usage

## S3 method for class 'glca'
reorder(x, class.order = NULL, cluster.order = NULL, decreasing = TRUE, ...)
## S3 method for class 'glca'
reorder(x, class.order = NULL, cluster.order = NULL, decreasing = TRUE, ...)

Arguments

`x`	an object of "`glca`", usually, a result of a call to `glca`.
`class.order`	a integer vector of length equal to number of latent classes of the glca model, assigning the desired order of the latent classes
`cluster.order`	a integer vector of length equal to number of latent clusters of the glca model, assigning the desired order of the latent clusters
`decreasing`	logical, when the `class.order` or `cluster.order` are not given, whether to rearrange the latent classes (clusters) by decreasing order of the magnitude of the probability of responding the first-category to the first manifest item (prevalence for the first latent class).
`...`	further arguments passed to or from other methods.

Details

Since the latent classes or clusters can be switched according to the initial value of EM algorithm, the order of estimated parameters can be arbitrary.

Examples

lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
            data = gss08, nclass = 3, na.rm = TRUE)
plot(lca)

# Given ordering number
lca321 = reorder(lca, 3:1)
plot(lca321)

# Descending order
dec_lca = reorder(lca, decreasing = TRUE)
plot(dec_lca)

# Ascending order
inc_lca = reorder(lca, decreasing = FALSE)
plot(inc_lca)

lca = glca(item(DEFECT, HLTH, RAPE, POOR, SINGLE, NOMORE) ~ 1,
            data = gss08, nclass = 3, na.rm = TRUE)
plot(lca)

# Given ordering number
lca321 = reorder(lca, 3:1)
plot(lca321)

# Descending order
dec_lca = reorder(lca, decreasing = TRUE)
plot(dec_lca)

# Ascending order
inc_lca = reorder(lca, decreasing = FALSE)
plot(inc_lca)

Summarizes the Estimated Parameters of Fitted glca Model

Description

summary method for class "glca".

Usage

## S3 method for class 'glca'
summary(object, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'glca'
summary(object, digits = max(3, getOption("digits") - 3), ...)

Arguments

`object`	an object of "`glca`", usually, a result of a call to `glca`
`digits`	the number of digits to be printed
`...`	further arguments passed to or from other methods

Value

This function prints decriptions of model and its more detailed estimated parameters but returns NULL.

Examples

## For examples see example(glca)

## For examples see example(glca)

Package 'glca'

Help Index

An R Package for Multiple-Group Latent Class Analysis

Description

Extracts glca Model Coefficients

Description

Usage

Arguments

Value

See Also

Examples

Fits Latent Class Models for Data Containing Group Variable and Covariates

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Goodness of Fit Tests for Fitted glca Model

Description

Usage

Arguments

Value

References

See Also

Examples

General Social Study (GSS) 2008

Description

Format

Source

References

Examples

Specifies Manifest Items for glca

Description

Usage

Arguments

Value

See Also

Examples

National Youth Tobacco Survey (NYTS) 2018

Description

Format

Source

Examples

Plots the Estimated Parameters of Fitted glca Model

Description

Usage

Arguments

Value

See Also

Examples

Reorders the estimated parameters of glca model

Description

Usage

Arguments

Details

Examples

Summarizes the Estimated Parameters of Fitted glca Model

Description

Usage

Arguments

Value

See Also

Examples

Extracts `glca` Model Coefficients

Goodness of Fit Tests for Fitted `glca` Model

Specifies Manifest Items for `glca`