documentation updates

R/independence_weights_estimation.R

@@ -35,6 +35,16 @@
 #' 
 #' Huling, J. D., Greifer, N., & Chen, G. (2021). Independence weights for causal inference with continuous exposures. 
 #' arXiv preprint arXiv:2107.07086. \url{https://arxiv.org/abs/2107.07086}
+#' @return An object of class \code{"independence_weights"}.
+#' \item{weights}{the estimated weights, the distance covariance optimal weights (DCOWs)}
+#' \item{A}{the treatment vector}
+#' \item{opt}{the object returned by whatever optimization routine was used}
+#' \item{objective}{the value of the optimized objective function}
+#' \item{distcov_unweighted}{the unweighted distance covariance between treatment and covariates}
+#' \item{distcov_weighted}{the weighted distance covariance between treatment and covariates}
+#' \item{energy_A}{the (energy) distance between the treatment distribution and the weighted treatment distribution. Smaller values mean the marginal distribution of the treatment is preserved after weighting}
+#' \item{energy_x}{the (energy) distance between the covariate distribution and the weighted covariate distribution. Smaller values mean the marginal distribution of the covariates is preserved after weighting}
+#' \item{ess}{the expected sample size after weighting. Kish's approximation is used}
 #'
 #' @examples
 #'
@@ -290,6 +300,14 @@ independence_weights <- function(A,
 #' 
 #' Huling, J. D., Greifer, N., & Chen, G. (2021). Independence weights for causal inference with continuous exposures. 
 #' arXiv preprint arXiv:2107.07086. \url{https://arxiv.org/abs/2107.07086}
+#' @return An object of class \code{"weighted_energy_terms"}.
+#' \item{D_w}{the value of the DCOW measure}
+#' \item{distcov_unweighted}{the unweighted distance covariance between treatment and covariates}
+#' \item{distcov_weighted}{the weighted distance covariance between treatment and covariates}
+#' \item{energy_A}{the (energy) distance between the treatment distribution and the weighted treatment distribution. Smaller values mean the marginal distribution of the treatment is preserved after weighting}
+#' \item{energy_x}{the (energy) distance between the covariate distribution and the weighted covariate distribution. Smaller values mean the marginal distribution of the covariates is preserved after weighting}
+#' \item{ess}{the expected sample size after weighting. Kish's approximation is used}
+#' 
 #' @examples 
 #' 
 #' simdat <- simulate_confounded_data(seed = 999, nobs = 100)

R/print_indep_weights_object.R

@@ -9,6 +9,7 @@
 #' @rdname print
 #' @seealso \code{\link[independenceWeights]{independence_weights}} for function which produces energy balancing weights
 #' @importFrom stats quantile
+#' @return Nothing returned
 #' @export
 print.independence_weights <- function(x, digits = max(getOption('digits')-3, 3), ...)
 {
@@ -35,6 +36,7 @@ print.independence_weights <- function(x, digits = max(getOption('digits')-3, 3)
 #' @rdname print
 #' @seealso \code{\link[independenceWeights]{weighted_energy_stats}} for function which produces energy balancing weights
 #' @importFrom stats quantile
+#' @return Nothing returned
 #' @export
 print.weighted_energy_terms <- function(x, digits = max(getOption('digits')-3, 3), ...)
 {

R/simulate_data.R

@@ -22,6 +22,10 @@
 #' Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures. 
 #' Health Services and Outcomes Research Methodology, 21(1), 69-110.
 #' @importFrom stats dist rbinom rchisq rnorm
+#' @return A list with the following elements
+#' \item{data}{a \code{data.frame} with the response (\code{Y}), treatment (\code{A}), confounders (\code{Z1} to \code{Z5}), and true average dose response function \code{truth}}
+#' \item{true_adrf}{a function; true average dose response function}
+#' \item{original_covariates}{original, untransformed covariates in the simulation setup. Do not use, as it makes the simulation setup significantly easier.}
 #'
 #' @examples
 #'