glca (version 1.4.2)

nyts18: 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

Examples

data("nyts18")
# \donttest{
# 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)
# }