wqs.pool.test was produced to demonstrate pool.mi(). First, the univariate Bayesian imputation approach (using impute.univariate.bayesian.mi) imputed the X.bdl element of simdata87 multiple times to form an imputed X array. Multiple WQS regressions were run on the imputed X array to produce an array of WQS parameter estimates, wqs.pool.test.
Usage
data(wqs.pool.test)
Arguments
Format
An array of 16 x 2 x 3, with
16 parameters as the rows (The 14 weights, intercept, and WQS estimate of a WQS model),
2 refers to the parameters of mean and standard deviation