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

## 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"`

.

## Note

Warning: `D2()` assumes that the order of the variables is the same in different models. See https://github.com/amices/mice/issues/420 for details.

## 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.

https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:chi

## Examples

```
# 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)
#> test statistic df1 df2 dfcom p.value riv
#> 1 ~~ 2 3.649642 1 11.69791 NA 0.08089545 1.408231
# \donttest{
# 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)
#> test statistic df1 df2 dfcom p.value riv
#> 1 ~~ 2 0.5406582 4 5.795658 NA 0.7131141 1.212884
# }
```