Combine sets of estimates (and their standard errors) generated from different multiply imputed datasets into one set of results.
mi.meld(q, se, byrow = TRUE)Average value of each quantity of interest across the m models
Standard errors of each quantity of interest
A matrix or data frame of (k) quantities of interest (eg.
coefficients, parameters, means) from (m) multiply imputed datasets.
Default is to assume the matrix is m-by-k (see byrow), thus each
row represents a set of results from one dataset, and each column
represents the different values of a particular quantity of interest
across the imputed datasets.
A matrix or data frame of standard errors that correspond to each of the
elements of the quantities of interest in q. Should be the same
dimensions as q.
logical. If TRUE, q and se are treated as
though each row represents the set of results from one dataset
(thus m-by-k). If FALSE, each column represents results from one
dataset (thus k-by-m).
Uses Rubin's rules for combining a set of results from multiply imputed datasets to reflect the average result, with standard errors that both average uncertainty across models and account for disagreement in the estimated values across the models.
Rubin, D. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
Honaker, J., King, G., Honaker, J. Joseph, A. Scheve K. (2001). Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation American Political Science Review, 95(1), 49--69. (p53)