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
slcafitfor 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 isFALSE.- 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.
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 -1135.4 2356.8 2539.4 28.423 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 -1135.4 2356.8 2539.4 28.423 31 0.5993
#> ---
#> 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 0.19
#> stat4 43 -1135.4 2356.8 2539.4 28.423 31 0.39
#> ---
#> 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 -1135.4 2356.8 2539.4 7.7805 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 -1135.4 2356.8 2539.4 7.7805 11 0.7328
# \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 -1135.4 2356.8 2539.4 7.7805 11 0.18
# }