# D1: Compare two nested models using D1-statistic In mice: Multivariate Imputation by Chained Equations

 D1 R Documentation

## Compare two nested models using D1-statistic

### Description

The D1-statistics is the multivariate Wald test.

### Usage

``````D1(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL)
``````

### 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.

`testModels`

### 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)
## Not run:
# 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)

## End(Not run)
``````

mice documentation built on June 7, 2023, 5:38 p.m.