Combines estimates and standard errors from m complete-data analyses
performed on m imputed datasets to produce a single inference. Uses
the technique described by Rubin (1987) for multiple
imputation inference for a scalar estimand.
Usage
mi.inference(est, std.err, confidence=0.95)
Arguments
est
a list of m (at least 2) vectors representing estimates (e.g.,
vectors of estimated regression coefficients) from complete-data
analyses performed on m imputed datasets.
std.err
a list of m vectors containing standard errors from the
complete-data analyses corresponding to the estimates in est.
confidence
desired coverage of interval estimates.
Value
a list with the following components, each of which is a vector of the
same length as the components of est and std.err:
est
the average of the complete-data estimates.
std.err
standard errors incorporating both the between and the
within-imputation uncertainty (the square root of the "total
variance").
df
degrees of freedom associated with the t reference
distribution used for interval estimates.
signif
P-values for the two-tailed hypothesis tests that the estimated
quantities are equal to zero.
lower
lower limits of the (100*confidence)% interval estimates.
upper
upper limits of the (100*confidence)% interval estimates.
r
estimated relative increases in variance due to nonresponse.
fminf
estimated fractions of missing information.
Method
Uses the method described on pp. 76-77 of Rubin (1987) for combining
the complete-data estimates from $m$ imputed datasets
for a scalar estimand. Significance levels and interval estimates are
approximately valid for each one-dimensional estimand, not for all of
them jointly.
References
Rubin, D. B. (1987) Multiple Imputation for Nonresponse in
Surveys. Wiley.
Schafer, J. L. (1996) Analysis of Incomplete Multivariate Data.
Chapman & Hall.