Pools the results of m repeated complete data analysis
Usage
pool(object, method = "smallsample")
Arguments
object
An object of class mira, produced by with.mids() or as.mira()
method
A string describing the method to compute the degrees of
freedom. The default value is "smallsample", which specifies the is
Barnard-Rubin adjusted degrees of freedom (Barnard and Rubin, 1999) for small
samples. Specifying a different string produces the conventional degrees of
freedom as in Rubin (1987).
Details
The function averages the estimates of the complete data model, computes the
total variance over the repeated analyses, and computes the relative increase
in variance due to nonresponse and the fraction of missing information. The
function relies on the availability of
the estimates of the
model, typically present as 'coefficients' in the fit object
an
appropriate estimate of the variance-covariance matrix of the estimates per
analyses (estimated by vcov.
The function pools also
estimates obtained with lme() and lmer(), BUT only the fixed
part of the model.
Value
An object of class mipo, which stands for 'multiple imputation
pooled outcome'.
Author(s)
Stef van Buuren, Karin Groothuis-Oudshoorn, 2009
References
Barnard, J. and Rubin, D.B. (1999). Small sample degrees of
freedom with multiple imputation. Biometrika, 86, 948-955.
Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys.
New York: John Wiley and Sons.
van Buuren S and Groothuis-Oudshoorn K (2011). mice: Multivariate
Imputation by Chained Equations in R. Journal of Statistical
Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/
Pinheiro, J.C. and Bates, D.M. (2000). Mixed-Effects Models in S and
S-PLUS. Berlin: Springer.
See Also
with.mids, as.mira, vcov
Examples
# which vcov methods can R find
methods(vcov)
#
imp <- mice(nhanes)
fit <- with(data=imp,exp=lm(bmi~hyp+chl))
pool(fit)
#Call: pool(object = fit)
#
#Pooled coefficients:
#(Intercept) hyp chl
# 22.01313 -1.45578 0.03459
#
#Fraction of information about the coefficients missing due to nonresponse:
#(Intercept) hyp chl
# 0.29571 0.05639 0.38759
#> summary(pool(fit))
# est se t df Pr(>|t|) lo 95 hi 95 missing
#(Intercept) 22.01313 4.94086 4.4553 12.016 0.000783 11.24954 32.77673 NA
#hyp -1.45578 2.26789 -0.6419 20.613 0.528006 -6.17752 3.26596 8
#chl 0.03459 0.02829 1.2228 9.347 0.251332 -0.02904 0.09822 10
# fmi
#(Intercept) 0.29571
#hyp 0.05639
#chl 0.38759
#