Description Usage Arguments Details Value Examples
Performs propensity score Calculation.
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data 
Data Frame  containing the dataset to be balanced. Must include treatment indicator as 0, 1 factor. 
covariates 
Vector  containing the list of variable names to be included as confounding variables 
ps.method 
String  name of the method to use for calculation of propensity scores. Options are

max.covariates 
The maximum number of covariates that can be used to calculate propensity scores (default 200) 
max.twang 
The maximum number of samples that can be used with the twang ps.method (default 30k) 
min.twang 
The minimum number of covariates required to use the twang ps.method (default 25) 
control.ratio 
The desired ratio of control to treatment subjects. A large imbalance between control and treatment subjects can cause problems with algorithm convergence. If the control population exceeds control.ratio times as many subjects as the treatment group, the control population will be randomly sampled to the desired size. 
random.seed 
Sets the random number generator seed, which determines how the control population is sampled. Override the default (43762116) to generate a different sample of control subjects. 
odds.ratio 
A logical argument indicating if the odds ratio and 95 This is an intensive calculation that can take a while. 
lr.summary.file 
The file name where the logistic regression summary should be written to. If directory is provided as part of the file name, it has to already exist. Default value is NULL. If not given, no file will be written. 
This function calculates the propensity score based on the specified options.
In order for this function to work correctly, the data
argument must be a data frame
containing the collection of confounding variables and a treatment indicator factor variable.
Additionally, the covariates
argument must contain a vector of the variable names to
be included as covariates or confounding variables. See list of arguments for additional
options.
Data Frame  containing the original dataset, trimmed of any incomplete cases with additional
variable added for ps_values
(the calculated propensity scores)
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