Likelihood ratio test for mkinfit models

Compare two mkinfit models based on their likelihood. If two fitted mkinfit objects are given as arguments, it is checked if they have been fitted to the same data. It is the responsibility of the user to make sure that the models are nested, i.e. one of them has less degrees of freedom and can be expressed by fixing the parameters of the other.

# S3 method for mkinfit
lrtest(object, object_2 = NULL, ...)

Arguments

object	An mkinfit object
object_2	Optionally, another mkinfit object fitted to the same data.
...	Argument to mkinfit, passed to update.mkinfit for creating the alternative fitted object.

Details

Alternatively, an argument to mkinfit can be given which is then passed to update.mkinfit to obtain the alternative model.

The comparison is then made by the lrtest.default method from the lmtest package. The model with the higher number of fitted parameters (alternative hypothesis) is listed first, then the model with the lower number of fitted parameters (null hypothesis).

Examples

# \dontrun{
test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == "parent")
sfo_fit <- mkinfit("SFO", test_data, quiet = TRUE)
dfop_fit <- mkinfit("DFOP", test_data, quiet = TRUE)
lrtest(dfop_fit, sfo_fit)
#> Likelihood ratio test
#> 
#> Model 1: DFOP with error model const
#> Model 2: SFO with error model const
#>   #Df  LogLik Df  Chisq Pr(>Chisq)    
#> 1   5 -42.453                         
#> 2   3 -63.954 -2 43.002  4.594e-10 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
lrtest(sfo_fit, dfop_fit)
#> Likelihood ratio test
#> 
#> Model 1: DFOP with error model const
#> Model 2: SFO with error model const
#>   #Df  LogLik Df  Chisq Pr(>Chisq)    
#> 1   5 -42.453                         
#> 2   3 -63.954 -2 43.002  4.594e-10 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# The following two examples are commented out as they fail during
# generation of the static help pages by pkgdown
# lrtest(dfop_fit, error_model = "tc")
# lrtest(dfop_fit, fixed_parms = c(k2 = 0))
# }