mice (version 2.46.0)

pool: Multiple imputation pooling

Description

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).

Value

An object of class mipo, which stands for 'multiple imputation pooled outcome'.

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

1. the estimates of the model, typically present as 'coefficients' in the fit object

2. 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.

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

# NOT RUN {
# 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
# 

# }