Multiple imputation inference
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.
mi.inference(est, std.err, confidence=0.95)
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 |
confidence |
desired coverage of interval estimates. |
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. |
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.
See Rubin (1987) or Schafer (1996), Chapter 4.
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