D2: Compare two nested models using D2-statistic

In mice: Multivariate Imputation by Chained Equations

Compare two nested models using D2-statistic

Description

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

Usage

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".

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

See Also

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))
D2(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))
D2(fit1, fit0)

## End(Not run)

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