Cleaning

Author: ngreifer

R/functions_for_processing.R

@@ -759,7 +759,7 @@ get_treated_level <- function(treat, estimand, focal = NULL) {
 
   for (i in names(poly_terms)) {
     if (poly[i] == 1L) {
-      poly_terms[[i]] <- d[,i]
+      poly_terms[[i]] <- d[, i]
       poly_co.names[[i]] <- i
     }
     else {

R/summary.weightit.R

@@ -238,7 +238,8 @@ plot.summary.weightit <- function(x, binwidth = NULL, bins = NULL, ...) {
                      fill = "gray70", alpha = 1) +
       scale_y_continuous(expand = expansion(c(0, .05))) +
       geom_vline(xintercept = mean(w), linetype = "12", color = "blue", size = .75) +
-      labs(x = "Weight", y = "Count", title = "Distribution of Weights") +
+      labs(x = "Weight", y = "Count", title = "Distribution of Weights",
+           subtitle = subtitle) +
       theme_bw()
   }
   else {
@@ -261,7 +262,8 @@ plot.summary.weightit <- function(x, binwidth = NULL, bins = NULL, ...) {
                      fill = "gray70", alpha = 1) +
       scale_y_continuous(expand = expansion(c(0, .05))) +
       geom_vline(data = w_means, aes(xintercept = .data$w), linetype = "12", color = "red") +
-      labs(x = "Weight", y = "Count", title = "Distribution of Weights") +
+      labs(x = "Weight", y = "Count", title = "Distribution of Weights",
+           subtitle = subtitle) +
       theme_bw() +
       facet_wrap(vars(.data$t), ncol = 1L, scales = "free") +
       theme(panel.background = element_blank(),

R/utils.R

@@ -529,7 +529,7 @@ col.w.r <- function(mat, y, w = NULL, s.weights = NULL, bin.vars = NULL, na.rm =
 scale_w <- function(x, w = NULL) {
   if (length(dim(x)) == 2L) {
     for (i in seq_col(x)) {
-      x[,i] <- scale_w(x[,i], w)
+      x[, i] <- scale_w(x[, i], w)
     }
 
     return(x)

R/weightit2ebal.R

@@ -867,4 +867,4 @@ weightit2ebal.cont <- function(covs, treat, s.weights, subset, missing, verbose,
 #
 #   list(w = w, fit.obj = fit$opt.out,
 #        Mparts = Mparts)
-# }
+# }

R/weightit2optweight.R

@@ -161,14 +161,13 @@
 #' cobalt::bal.tab(W2)
 #' plot(W2)
 #'
-#' \dontrun{
 #' #Balancing covariates with respect to re75 (continuous)
 #' (W3 <- weightit(re75 ~ age + educ + married +
 #'                   nodegree + re74, data = lalonde,
 #'                 method = "optweight", tols = .05))
 #' summary(W3)
 #' cobalt::bal.tab(W3)
-#' plot(W3)}
+#' plot(W3)
 NULL
 
 weightit2optweight <- function(covs, treat, s.weights, subset, estimand, focal, missing,
@@ -344,7 +343,6 @@ weightit2optweight.cont <- function(covs, treat, s.weights, subset, missing, ver
     return(NULL)
   }
 
-  original.vars <- colnames(covs)
   na.cov <- is.na(d$cov)
 
   if (is_not_null(d$treat) && any(na.cov)) {
@@ -360,4 +358,4 @@ weightit2optweight.cont <- function(covs, treat, s.weights, subset, missing, ver
   rownames(d) <- NULL
 
   d
-}
+}

man/method_optweight.Rd

@@ -163,7 +163,7 @@ If *optweight* is not on CRAN, or if you want to install the development version
 pak::pkg_install("ngreifer/optweight")
 ```
 
-*optweight* depends on the *osqp* package, which requires compilation, which means you may need additional software installed on your computer to install it from source.
+*optweight* depends on the *osqp* package, which requires compilation, which means you may need additional software installed on your computer to install it from source. It's also a good idea to install the *highs* and *scs* packages if you want to take advantage of all of *optweight*'s features and optimize its performance.
 
 ## Propensity score weighting using SuperLearner (`method = "super"`)