An object of class <code>mira</code>, produced by <code>with()</code>.

fit1

An object of class <code>mira</code>, produced by <code>with()</code>. The
model in <code>fit0</code> is a nested within <code>fit1</code>. The default null 
model <code>fit0 = NULL</code> compares <code>fit1</code> to the intercept-only model.

fit0

A single number or a numeric vector denoting the 
complete-data degrees of freedom for the hypothesis test. If not specified,
it is set equal to <code>df.residual</code> of model <code>fit1</code>. Not used 
for method <code>D2()</code>.

df.com

A character string denoting Wald- or likelihood-based based tests. Can be either <code>"wald"</code> or <code>"likelihood"</code>. Only used if <code>method="D2"</code>.

Compare two nested models using D2-statistic

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
its own imputation model. Built-in imputation models are provided for
continuous data (predictive mean matching, normal), binary data (logistic
regression), unordered categorical data (polytomous logistic regression)
and ordered categorical data (proportional odds). MICE can also impute
continuous two-level data (normal model, pan, second-level variables).
Passive imputation can be used to maintain consistency between variables.
Various diagnostic plots are available to inspect the quality of the
imputations.

Stef van Buuren

mice

Multivariate Imputation by Chained Equations

D2: Compare two nested models using D2-statistic

Description

Usage

Arguments

Details