The D1-statistics is the multivariate Wald test.

## 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.- dfcom
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.- df.com
Deprecated

## Note

Warning: `D1()` 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., T. E. Raghunathan, and D. B. Rubin. 1991.
Large-Sample Significance Levels from Multiply Imputed Data Using
Moment-Based Statistics and an F Reference Distribution.
*Journal of the American Statistical Association*, 86(416): 1065–73.

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

## 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))
D1(mi1, mi0)
#> test statistic df1 df2 dfcom p.value riv
#> 1 ~~ 2 5.28351 1 4 20 0.08306791 0.671799
if (FALSE) {
# 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))
D1(fit1, fit0)
}
```