| npCBPS.fit {CBPS} | R Documentation |
npCBPS.fit
Description
npCBPS.fit
Usage
npCBPS.fit(treat, X, corprior, print.level, ...)
Arguments
treat |
A vector of treatment assignments. Binary or multi-valued treatments should be factors. Continuous treatments should be numeric. |
X |
A covariate matrix. |
corprior |
Prior hyperparameter controlling the expected amount of correlation between each covariate and the treatment. Specifically, the amount of correlation between the k-dimensional covariates, X, and the treatment T after weighting is assumed to have prior distribution MVN(0,sigma^2 I_k). We conceptualize sigma^2 as a tuning parameter to be used pragmatically. It's default of 0.1 ensures that the balance constraints are not too harsh, and that a solution is likely to exist. Once the algorithm works at such a high value of sigma^2, the user may wish to attempt values closer to 0 to get finer balance. |
print.level |
Controls verbosity of output to the screen while npCBPS runs. At the default of print.level=0, little output is produced. It print.level>0, it outputs diagnostics including the log posterior (log_post), the log empirical likelihood associated with the weights (log_el), and the log prior probability of the (weighted) correlation of treatment with the covariates. |
... |
Other parameters to be passed. |