The D3-statistic is a likelihood-ratio test statistic.
D3(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL)An object of class mira, produced by with().
An object of class mira, produced by with(). The
model in fit0 is a nested within fit1. The default null
model fit0 = NULL compares fit1 to the intercept-only model.
A single number denoting the
complete-data degrees of freedom of model fit1. If not specified,
it is set equal to df.residual of model fit1. If that cannot
be done, the procedure assumes (perhaps incorrectly) a large sample.
Deprecated
An object of class mice.anova
The D3() function implement the LR-method by
Meng and Rubin (1992). The implementation of the method relies
on the broom package, the standard update mechanism
for statistical models in R and the offset function.
The function calculates m repetitions of the full
(or null) models, calculates the mean of the estimates of the
(fixed) parameter coefficients \(\beta\). For each imputed
imputed dataset, it calculates the likelihood for the model with
the parameters constrained to \(\beta\).
The mitml::testModels() function offers similar functionality
for a subset of statistical models. Results of mice::D3() and
mitml::testModels() differ in multilevel models because the
testModels() also constrains the variance components parameters.
For more details on
Meng, X. L., and D. B. Rubin. 1992. Performing Likelihood Ratio Tests with Multiply-Imputed Data Sets. Biometrika, 79 (1): 103–11.
https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:likelihoodratio
# Compare two linear models:
imp <- mice(nhanes2, seed = 51009, print = FALSE)
mi1 <- with(data = imp, expr = lm(bmi ~ age + hyp + chl))
mi0 <- with(data = imp, expr = lm(bmi ~ age + hyp))
D3(mi1, mi0)
#> test statistic df1 df2 dfcom p.value riv
#> 1 ~~ 2 2.917381 1 8.764849 20 0.122711 2.082143
if (FALSE) { # \dontrun{
# Compare two logistic regression models
imp <- mice(boys, maxit = 2, print = FALSE)
fit1 <- with(imp, glm(gen > levels(gen)[1] ~ hgt + hc + reg, family = binomial))
fit0 <- with(imp, glm(gen > levels(gen)[1] ~ hgt + hc, family = binomial))
D3(fit1, fit0)
} # }