Compare Two Estimated slca Models

Provides relative model fit test for two fitted SLCM models with deviance statistic.

Usage

compare(
  model1,
  model2,
  test = c("none", "chisq", "boot"),
  nboot = 100,
  method = c("hybrid", "em", "nlm"),
  plot = FALSE,
  maxiter = 1000,
  tol = 1e-08,
  verbose = FALSE
)

Arguments

model1

an object of class slca and estimated.

model2

another object of class slca and estimated.

test

a character string specifying the type of test to be conducted. If "chisq", a chi-squared test is conducted. If "boot", a bootstrap test is conducted.

nboot

an integer specifying the number of bootstrap rounds to be performed.

method

estimation method for bootstrapping.

plot

a logical value indicating whether to print histogram of G-squared statistics for boostrap samples, only for test = "boot".

maxiter

an integer specifying maximum number of iterations allowed for the estimation process of each bootstrapping round.

tol

a numeric value setting tolerance for the convergence of each bootstrapping round.

verbose

a logical value indicating whether to print progress updates on the number of bootstrapping rounds completed.

Value

A data.frame containing the number of parameters (Df), loglikelihood, AIC, BIC, G-squared statistics, and the residual degree of freedom for each object. Depending on the test argument, the p-value for the corresponding statistical test may also be included.

See also

gof

Examples

library(magrittr)
data <- gss7677[gss7677$COHORT == "YOUNG", ]
stat2 <- slca(status(2) ~ PAPRES + PADEG + MADEG) %>%
   estimate(data = data, control = list(verbose = FALSE))
stat3 <- slca(status(3) ~ PAPRES + PADEG + MADEG) %>%
   estimate(data = data, control = list(verbose = FALSE))
stat4 <- slca(status(4) ~ PAPRES + PADEG + MADEG) %>%
   estimate(data = data, control = list(verbose = FALSE))

gof(stat2, stat3, stat4)
#> Analysis of Goodness of Fit Table
#> 
#>       Df  logLik    AIC    BIC     Gsq Res. Df
#> stat2 21 -1179.2 2400.4 2489.5 115.979      53
#> stat3 32 -1139.3 2342.6 2478.5  36.204      42
#> stat4 43 -1137.7 2361.4 2544.0  33.011      31
gof(stat2, stat3, stat4, test = "chisq")
#> Analysis of Goodness of Fit Table
#> 
#>       Df  logLik    AIC    BIC     Gsq Res. Df Pr(>Chi)    
#> stat2 21 -1179.2 2400.4 2489.5 115.979      53 1.34e-06 ***
#> stat3 32 -1139.3 2342.6 2478.5  36.204      42   0.7225    
#> stat4 43 -1137.7 2361.4 2544.0  33.011      31   0.3690    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# \donttest{
gof(stat2, stat3, stat4, test = "boot")
#> Analysis of Goodness of Fit Table
#> 
#>       Df  logLik    AIC    BIC     Gsq Res. Df Pr(Boot)    
#> stat2 21 -1179.2 2400.4 2489.5 115.979      53   <2e-16 ***
#> stat3 32 -1139.3 2342.6 2478.5  36.204      42     0.21    
#> stat4 43 -1137.7 2361.4 2544.0  33.011      31     0.31    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# }

compare(stat3, stat4)
#> Analysis of Relative Model Fit
#> 
#> Model H0: stat3
#> Model H1: stat4
#>       Df  logLik    AIC    BIC    Gsq Res. Df
#> stat3 32 -1139.3 2342.6 2478.5               
#> stat4 43 -1137.7 2361.4 2544.0 3.1929      11
compare(stat3, stat4, test = "chisq")
#> Analysis of Relative Model Fit
#> 
#> Model H0: stat3
#> Model H1: stat4
#>       Df  logLik    AIC    BIC    Gsq Res. Df Pr(>Chi)
#> stat3 32 -1139.3 2342.6 2478.5                        
#> stat4 43 -1137.7 2361.4 2544.0 3.1929      11   0.9879
# \donttest{
compare(stat3, stat4, test = "boot")
#> Analysis of Relative Model Fit
#> 
#> Model H0: stat3
#> Model H1: stat4
#>       Df  logLik    AIC    BIC    Gsq Res. Df Pr(Boot)
#> stat3 32 -1139.3 2342.6 2478.5                        
#> stat4 43 -1137.7 2361.4 2544.0 3.1929      11     0.67
# }