maicChecks (version 0.2.0)

exmWt.2ipd: Exact matching for two IPD's

Description

Exact matching for two IPD's

Usage

exmWt.2ipd(
  ipd1,
  ipd2,
  vars_to_match = NULL,
  cat_vars_to_01 = NULL,
  mean.constrained = FALSE
)

Value

ipd1

re-scaled weights of the exact matching by maximizing ESS for IPD 1, and the input IPD 1 data with categorical variables converted to 0-1 indicators

ipd2

re-scaled weights of the exact matching by maximizing ESS for IPD 2, and the input IPD 2 data with categorical variables converted to 0-1 indicators

wtd.summ

ESS for IPD 1, ESS for IPD 2, and weighted means of the matching variables

Arguments

ipd1

a dataframe with n row and p column, where n is number of subjects and p is the number of variables used in matching.

ipd2

the other IPD with the same number of columns

vars_to_match

variables used for matching. if NULL, use all variables.

cat_vars_to_01

a list of variable names for the categorical variables that need to be converted to indicator variables.

mean.constrained

whether to restrict the weighted means to be within the ranges of observed means. Default is FALSE. When it is TRUE, there is a higher chance of not having a solution.

Author

Lillian Yau

Details

If dummy variables are already created for the categorical variables in the data set, and are present in ipd1 and ipd2, then cat_vars_to_01 should be left as NULL.

Examples

if (FALSE) {
ipd1 <- sim110[sim110$study == 'IPD A',]
ipd2 <- sim110[sim110$study == 'IPD B',]
x <- exmWt.2ipd(ipd1, ipd2, vars_to_match = paste0('X', 1:5), 
cat_vars_to_01 = paste0('X', 1:3), mean.constrained = FALSE) 
}