rubin_rules: Combine estimates using Rubin's rules

In rbmi: Reference Based Multiple Imputation

Combine estimates using Rubin's rules

Description

Pool together the results from M complete-data analyses according to Rubin's rules. See details.

Usage

rubin_rules(ests, ses, v_com)

Arguments

ests	Numeric vector containing the point estimates from the complete-data analyses.
ses	Numeric vector containing the standard errors from the complete-data analyses.
v_com	Positive number representing the degrees of freedom in the complete-data analysis.

Details

rubin_rules applies Rubin's rules (Rubin, 1987) for pooling together the results from a multiple imputation procedure. The pooled point estimate est_point is is the average across the point estimates from the complete-data analyses (given by the input argument ests). The total variance var_t is the sum of two terms representing the within-variance and the between-variance (see Little-Rubin (2002)). The function also returns df, the estimated pooled degrees of freedom according to Barnard-Rubin (1999) that can be used for inference based on the t-distribution.

Value

A list containing:

• est_point: the pooled point estimate according to Little-Rubin (2002).

• var_t: total variance according to Little-Rubin (2002).

• df: degrees of freedom according to Barnard-Rubin (1999).

References

Barnard, J. and Rubin, D.B. (1999). Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955

Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]

See Also

rubin_df() for the degrees of freedom estimation.

rbmi documentation built on Oct. 16, 2024, 5:09 p.m.