The D1-statistics is the multivariate Wald test.
D1(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL)An object of class mira, produced by with().
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.
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.
Deprecated
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.
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
# 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) { # \dontrun{
# 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)
} # }