Multiple imputation pooling: univariate version
Pools univariate estimates of m repeated complete data analysis
pool.scalar(Q, U, n = Inf, k = 1)
Q |
A vector of univariate estimates of |
U |
A vector containing the corresponding |
n |
A number providing the sample size. If nothing is specified,
an infinite sample |
k |
A number indicating the number of parameters to be estimated.
By default, |
The function averages the univariate 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.
Returns a list with components.
m:Number of imputations.
qhat:The m univariate estimates of repeated complete-data analyses.
u:The corresponding m variances of the univariate estimates.
qbar:The pooled univariate estimate, formula (3.1.2) Rubin (1987).
ubar:The mean of the variances (i.e. the pooled within-imputation variance), formula (3.1.3) Rubin (1987).
b:The between-imputation variance, formula (3.1.4) Rubin (1987).
t:The total variance of the pooled estimated, formula (3.1.5) Rubin (1987).
r:The relative increase in variance due to nonresponse, formula (3.1.7) Rubin (1987).
df:The degrees of freedom for t reference distribution by the method of Barnard-Rubin (1999).
fmi:The fraction missing information due to nonresponse, formula (3.1.10) Rubin (1987).
Karin Groothuis-Oudshoorn and Stef van Buuren, 2009
Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.
# example with manual and automatic pooling imp <- mice(nhanes, maxit = 2, m = 2, print = FALSE, seed = 18210) fit <- with(data = imp, lm(bmi ~ age)) # manual pooling summary(fit$analyses[[1]]) summary(fit$analyses[[2]]) pool.scalar(Q = c(-1.5457, -1.428), U = c(0.9723^2, 1.041^2), n = 25, k = 2) # automatic pooling using broom pool(fit)
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