Compare two nested models using D3-statistic
The D3-statistic is a likelihood-ratio test statistic.
D3(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL)
fit1 |
An object of class |
fit0 |
An object of class |
dfcom |
A single number denoting the
complete-data degrees of freedom of model |
df.com |
Deprecated |
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 β. For each imputed
imputed dataset, it calculates the likelihood for the model with
the parameters constrained to β.
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
An object of class mice.anova
Meng, X. L., and D. B. Rubin. 1992. Performing Likelihood Ratio Tests with Multiply-Imputed Data Sets. Biometrika, 79 (1): 103–11.
# 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) # 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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.