pool {mice}R Documentation

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 functions like lm.mids or glm.mids.
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& 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

  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.

R-Specific: The original use of Varcov has been removed to vcov (VR MASS).

Value

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

Author(s)

Stef van Buuren, Karin Oudshoorn, 2000

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.

Alzola, C.F. and Harrell, F.E. (1999). An introduction to S-Plus and the Hmisc and Design Libraries. http://hesweb1.med.virginia.edu/biostat/s/index.html.

See Also

lm.mids, glm.mids, vcov, print.mira, summary.mira

Examples

data(nhanes)
imp <- mice(nhanes)
fit <- lm.mids(bmi~hyp+chl,data=imp)
pool(fit)
#  Call: pool(object = fit)
#  Pooled coefficients:
#   (Intercept)       hyp        chl 
#      21.29782 -1.751721 0.04085703
#
#  Fraction of information about the coefficients missing due to nonrespons
#  e: 
#   (Intercept)       hyp       chl 
#     0.1592247 0.1738868 0.3117452
#
#  > summary(pool(fit))
#           est         se          t       df     Pr(>|t|) 
#  (Intercept)  21.29781702 4.33668150  4.9110863 16.95890 0.0001329371
#      hyp  -1.75172102 2.30620984 -0.7595671 16.39701 0.4582953905
#      chl   0.04085703 0.02532914  1.6130442 11.50642 0.1338044664
#             lo 95      hi 95 missing       fmi 
#  (Intercept)  12.14652927 30.4491048      NA 0.1592247
#      hyp  -6.63106456  3.1276225       8 0.1738868
#    chl  -0.01459414  0.0963082      10 0.3117452 

[Package mice version 1.15 Index]