From 4f746478dbf957b52f7aac6e7f0e67324a7a988c Mon Sep 17 00:00:00 2001
From: Noah Greifer <noah.greifer@gmail.com>
Date: Tue, 20 Aug 2024 11:39:16 -0400
Subject: [PATCH] Adjust doc to prevent gbm warning with multinomial

---
 R/get_w_from_ps.R    | 31 ++++++++++++++++++-------------
 man/get_w_from_ps.Rd | 32 ++++++++++++++++++--------------
 2 files changed, 36 insertions(+), 27 deletions(-)

diff --git a/R/get_w_from_ps.R b/R/get_w_from_ps.R
index b2fcb9c..7c445fc 100644
--- a/R/get_w_from_ps.R
+++ b/R/get_w_from_ps.R
@@ -199,22 +199,27 @@
 #'                     estimand = "ATT", treated = "A")
 #' all.equal(w1, w3)
 #'
-#' #Using MMWS
+#' # Using MMWS
 #' w4 <- get_w_from_ps(ps, treat = lalonde$treat,
 #'                     estimand = "ATE", subclass = 20,
 #'                     stabilize = TRUE)
-#' @examplesIf requireNamespace("gbm", quietly = TRUE)
-#' #A multi-category example using GBM predicted probabilities
-#' library(gbm)
-#' T3 <- factor(sample(c("A", "B", "C"), nrow(lalonde), replace = TRUE))
-#'
-#' gbm.fit <- gbm(T3 ~ age + educ + race + married +
-#'                  nodegree + re74 + re75, data = lalonde,
-#'                distribution = "multinomial", n.trees = 200,
-#'                interaction.depth = 3)
-#' ps.multi <- drop(predict(gbm.fit, type = "response",
-#'                          n.trees = 200))
-#' w <- get_w_from_ps(ps.multi, T3, estimand = "ATE")
+#'
+#' # A multi-category example using predicted probabilities
+#' # from multinomial logistic regression
+#' T3 <- factor(sample(c("A", "B", "C"), nrow(lalonde),
+#'                     replace = TRUE))
+#'
+#' multi.fit <- multinom_weightit(
+#'   T3 ~ age + educ + race + married +
+#'     nodegree + re74 + re75, data = lalonde,
+#'   vcov = "none"
+#' )
+#'
+#' ps.multi <- fitted(multi.fit)
+#' head(ps.multi)
+#'
+#' w5 <- get_w_from_ps(ps.multi, treat = T3,
+#'                     estimand = "ATE")
 
 #' @export
 get_w_from_ps <- function(ps, treat, estimand = "ATE", focal = NULL, treated = NULL,
diff --git a/man/get_w_from_ps.Rd b/man/get_w_from_ps.Rd
index 335e662..3ebc288 100644
--- a/man/get_w_from_ps.Rd
+++ b/man/get_w_from_ps.Rd
@@ -155,23 +155,27 @@ w3 <- get_w_from_ps(ps, treat = treatAB,
                     estimand = "ATT", treated = "A")
 all.equal(w1, w3)
 
-#Using MMWS
+# Using MMWS
 w4 <- get_w_from_ps(ps, treat = lalonde$treat,
                     estimand = "ATE", subclass = 20,
                     stabilize = TRUE)
-\dontshow{if (requireNamespace("gbm", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-#A multi-category example using GBM predicted probabilities
-library(gbm)
-T3 <- factor(sample(c("A", "B", "C"), nrow(lalonde), replace = TRUE))
-
-gbm.fit <- gbm(T3 ~ age + educ + race + married +
-                 nodegree + re74 + re75, data = lalonde,
-               distribution = "multinomial", n.trees = 200,
-               interaction.depth = 3)
-ps.multi <- drop(predict(gbm.fit, type = "response",
-                         n.trees = 200))
-w <- get_w_from_ps(ps.multi, T3, estimand = "ATE")
-\dontshow{\}) # examplesIf}
+
+# A multi-category example using predicted probabilities
+# from multinomial logistic regression
+T3 <- factor(sample(c("A", "B", "C"), nrow(lalonde),
+                    replace = TRUE))
+
+multi.fit <- multinom_weightit(
+  T3 ~ age + educ + race + married +
+    nodegree + re74 + re75, data = lalonde,
+  vcov = "none"
+)
+
+ps.multi <- fitted(multi.fit)
+head(ps.multi)
+
+w5 <- get_w_from_ps(ps.multi, treat = T3,
+                    estimand = "ATE")
 }
 \references{
 \subsection{Binary treatments}{