pool.scalar {mice} | R Documentation |
Pools univariate estimates of m repeated complete data analysis
pool.scalar(Q, U, n = 99999, k = 1, method = "smallsample")
Q |
A vector of univariate estimates of |
U |
A vector containing the corresponding |
n |
A number providing the sample size. If nothing is specified, a large sample |
k |
A number indicating the number of parameters to be estimated. By default, |
method |
A string indicatint the method to calculate the degrees of freedom. If |
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. Component m
is the
number of imputations. Component qhat
contains the m
univariate estimates of repeated complete data analyses.
Component u
contains the corresponding m
variances of the univariate estimates.
Component qbar
is the pooled univariate estimate, formula (3.1.2) Rubin
(1987). Component ubar
is the mean of the variances
(i.e. the pooled within-imputation variance), formula (3.1.3) Rubin (1987).
Component b
is the between-imputation variance, formula (3.1.4) Rubin (1987).
Component t
is the total variance of the pooled estimated, formula (3.1.5) Rubin
(1987).
Component r
is the relative increase in variance due to nonresponse, formula
(3.1.7) Rubin (1987).
Component df
is the degrees of freedom for t reference distribution, formula
(3.1.6) Rubin (1987) or method of Barnard-Rubin (1999) (if method = "smallsample"
).
Component fmi
is the fraction missing information due to nonresponse, formula
(3.1.10) Rubin (1987).
Component lambda
is the proportion of variation due to nonresponse, formula
(2.24) Van Buuren (2012).
Karin Groothuis-Oudshoorn and Stef van Buuren, 2009
Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.
imp <- mice(nhanes) m <- imp$m Q <- rep(NA, m) U <- rep(NA, m) for (i in 1:m) { Q[i] <- mean(complete(imp, i)$bmi) U[i] <- var(complete(imp, i)$bmi) / nrow(nhanes) # (standard error of estimate)^2 } pool.scalar(Q, U, method = "rubin") # Rubin 1987 pool.scalar(Q, U, n = nrow(nhanes), k = 1) # Barnard-Rubin 1999