# D2: Compare two nested models using D2-statistic In mice: Multivariate Imputation by Chained Equations

## Description

The D2-statistic pools test statistics from the repeated analyses. The method is less powerful than the D1- and D3-statistics.

## Usage

 `1` ```D2(fit1, fit0 = NULL, use = "wald") ```

## Arguments

 `fit1` An object of class `mira`, produced by `with()`. `fit0` 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. `use` A character string denoting Wald- or likelihood-based based tests. Can be either `"wald"` or `"likelihood"`. Only used if `method="D2"`.

## References

Li, K. H., X. L. Meng, T. E. Raghunathan, and D. B. Rubin. 1991. Significance Levels from Repeated p-Values with Multiply-Imputed Data. Statistica Sinica 1 (1): 65–92.

`testModels`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# 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)) D2(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)) D2(fit1, fit0) ```