Goodness-of-Fit Test for Fitted slca Model

Computes the AIC, BIC, and deviance statistic (G-squared) for assessing the goodness-of-fit of a fitted slca model. If the test argument is specified, absolute model fit can be evaluated using deviance statistics.

Usage

gof(object, ...)

# S3 method for class 'slcafit'
gof(
   object, ..., test = c("none", "chisq", "boot"),
   nboot = 100, plot = FALSE,
   maxiter = 100, tol = 1e-6, verbose = FALSE
)

# S3 method for class 'slcafit'
gof(
  object,
  ...,
  test = c("none", "chisq", "boot"),
  nboot = 100,
  plot = FALSE,
  maxiter = 100,
  tol = 1e-06,
  verbose = FALSE
)

Arguments

object

an object of class slcafit.

...

additional objects of class slcafit for comparison.

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.

plot

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

maxiter

an integer specifying the maximum number of iterations allowed for the estimation process during each bootstrap iteration. The default is 100.

tol

a numeric value specifying the convergence tolerance for each bootstrap iteration. The default is 1e-6.

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. If a statistical test is performed (using test), the result includes the corresponding p-value.

See also

compare

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 -1135.5 2357.0 2539.6  28.633      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   0.7226    
#> stat4 43 -1135.5 2357.0 2539.6  28.633      31   0.5883    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# \donttest{
gof(stat2, stat3, stat4, test = "boot")
#> Error in stats::nlm(llf, logit[-c(fix0, fix1, arg$ref_idx)], fix0, fix1,     arg$ref_idx, arg$id, y = attr(mf, "y"), nobs = arg$nobs,     nvar = arg$nvar, nlev = unlist(arg$nlev), nlv = arg$nlv,     nrl = arg$nrl, nlf = arg$nlf, npi = arg$npi, ntau = arg$ntau,     nrho = arg$nrho, ul = arg$ul, vl = arg$vl, lf = arg$lf, tr = arg$tr,     rt = arg$rt, eqrl = arg$eqrl, eqlf = arg$eqlf, nc = arg$nc,     nk = arg$nk, nl = arg$nl, ncl = arg$ncl, nc_pi = arg$nc_pi,     nk_tau = arg$nk_tau, nl_tau = arg$nl_tau, nc_rho = arg$nc_rho,     nr_rho = arg$nr_rho, iterlim = 1, hessian = TRUE): missing value in parameter
# }

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 -1135.5 2357.0 2539.6 7.5707      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 -1135.5 2357.0 2539.6 7.5707      11   0.7512
# \donttest{
compare(stat3, stat4, test = "boot")
#> Error in stats::nlm(llf, logit[-c(fix0, fix1, arg$ref_idx)], fix0, fix1,     arg$ref_idx, arg$id, y = attr(mf, "y"), nobs = arg$nobs,     nvar = arg$nvar, nlev = unlist(arg$nlev), nlv = arg$nlv,     nrl = arg$nrl, nlf = arg$nlf, npi = arg$npi, ntau = arg$ntau,     nrho = arg$nrho, ul = arg$ul, vl = arg$vl, lf = arg$lf, tr = arg$tr,     rt = arg$rt, eqrl = arg$eqrl, eqlf = arg$eqlf, nc = arg$nc,     nk = arg$nk, nl = arg$nl, ncl = arg$ncl, nc_pi = arg$nc_pi,     nk_tau = arg$nk_tau, nl_tau = arg$nl_tau, nc_rho = arg$nc_rho,     nr_rho = arg$nr_rho, iterlim = control$nlm.iterlim, gradtol = control$nlm.tol,     steptol = control$nlm.tol): missing value in parameter
# }