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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

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
# }