exmLP.2ipd {maicChecks} | R Documentation |
Checks whether two IPD datasets can be matched with lpSolve::lp
Description
Checks whether two IPD datasets can be matched with lpSolve::lp
Usage
exmLP.2ipd(
ipd1,
ipd2,
vars_to_match = NULL,
cat_vars_to_01 = NULL,
mean.constrained = FALSE
)
Arguments
ipd1 |
a dataframe with n1 row and p column, where n1 is number of subjects of the first IPD, and p is the number of variables used in standardization. |
ipd2 |
a dataframe with n2 row and p column, where n2 is number of subjects of the second IPD, and p is the number of variables used in standardization. |
vars_to_match |
variables used for matching. if NULL, use all variables. |
cat_vars_to_01 |
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. |
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.
Value
lp.check |
0 = OS can be conducted; 2 = OS cannot be conducted |
Author(s)
Lillian Yau
Examples
## Not run:
ipd1 <- sim110[sim110$study == 'IPD A',]
ipd2 <- sim110[sim110$study == 'IPD B',]
x <- exmLP.2ipd(ipd1, ipd2, vars_to_match = paste0('X', 1:5),
cat_vars_to_01 = paste0('X', 1:3), mean.constrained = FALSE)
## End(Not run)