Usage
pool.compare(fit1, fit0, method = c("wald", "likelihood"), data = NULL)Arguments
- fit1
An object of class 'mira', produced by
with.mids().- fit0
An object of class 'mira', produced by
with.mids(). The model infit0is a nested fit0 offit1.- method
Either
"wald"or"likelihood"specifying the type of comparison. The default is"wald".- data
No longer used.
Value
A list containing several components. Component call is
the call to the pool.compare function. Component call11 is
the call that created fit1. Component call12 is the
call that created the imputations. Component call01 is the
call that created fit0. Component call02 is the
call that created the imputations. Components method is the
method used to compare two models: 'Wald' or 'likelihood'. Component
nmis is the number of missing entries for each variable.
Component m is the number of imputations.
Component qhat1 is a matrix, containing the estimated coefficients of the
m repeated complete data analyses from fit1.
Component qhat0 is a matrix, containing the estimated coefficients of the
m repeated complete data analyses from fit0.
Component ubar1 is the mean of the variances of fit1,
formula (3.1.3), Rubin (1987).
Component ubar0 is the mean of the variances of fit0,
formula (3.1.3), Rubin (1987).
Component qbar1 is the pooled estimate of fit1, formula (3.1.2) Rubin
(1987).
Component qbar0 is the pooled estimate of fit0, formula (3.1.2) Rubin
(1987).
Component Dm is the test statistic.
Component rm is the relative increase in variance due to nonresponse, formula
(3.1.7), Rubin (1987).
Component df1: df1 = under the null hypothesis it is assumed that Dm has an F
distribution with (df1,df2) degrees of freedom.
Component df2: df2.
Component pvalue is the P-value of testing whether the model fit1 is
statistically different from the smaller fit0.
Details
Compares two nested models after m repeated complete data analysis
The function is based on the article of Meng and Rubin (1992). The
Wald-method can be found in paragraph 2.2 and the likelihood method can be
found in paragraph 3. One could use the Wald method for comparison of linear
models obtained with e.g. lm (in with.mids()). The likelihood
method should be used in case of logistic regression models obtained with
glm() in with.mids().
The function assumes that fit1 is the
larger model, and that model fit0 is fully contained in fit1.
In case of method='wald', the null hypothesis is tested that the extra
parameters are all zero.
References
Li, K.H., Meng, X.L., Raghunathan, T.E. and Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1, 65-92.
Meng, X.L. and Rubin, D.B. (1992). Performing likelihood ratio tests with multiple-imputed data sets. Biometrika, 79, 103-111.
van Buuren S and Groothuis-Oudshoorn K (2011). mice: Multivariate
Imputation by Chained Equations in R. Journal of Statistical
Software, 45(3), 1-67. doi:10.18637/jss.v045.i03