Doc and vignette uodates to support new WeightIt version

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: cobalt
 Title: Covariate Balance Tables and Plots
-Version: 4.5.4.9001
+Version: 4.5.4.9002
 Authors@R: c(
     person("Noah", "Greifer", role=c("aut", "cre"), 
            email = "noah.greifer@gmail.com",
 	       comment=c(ORCID="0000-0003-3067-7154"))
     )
 Description: Generate balance tables and plots for covariates of groups preprocessed through 
              matching, weighting or subclassification, for example, using propensity scores. Includes 
-             integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', 
+             integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 
              'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing 
              functions. Users can also specify data for balance assessment not generated through 
              the above packages. Also included are methods for assessing balance in clustered or 
@@ -26,7 +26,7 @@ Imports:
     backports (>= 1.1.9)
 Suggests:
     MatchIt (>= 4.0.0),
-    WeightIt (>= 0.14.0),
+    WeightIt (>= 1.0.0),
     twang (>= 1.6),
     twangContinuous,
     Matching,

NEWS.md

@@ -7,6 +7,8 @@
 
 * Improved processing when no covariates are specified.
 
+* Documentation updates
+
 # cobalt 4.5.4
 
 * Minor update to accommodate `ggplot2` 3.5.0. Thanks to @teunbrand. (#80)

R/bal.tab.time.list.R

@@ -25,41 +25,49 @@
 #' * [`class-bal.tab.imp`] for more information on multiply imputed data.
 #' * [`bal.tab.multi()`][class-bal.tab.multi] for more information on multi-category treatments.
 #' 
-#' @examplesIf requireNamespace("twang", quietly = TRUE)
-#' data("iptwExWide", package = "twang")
-#' library("cobalt")
+#' @examplesIf requireNamespace("WeightIt", quietly = TRUE)
+#' data("msmdata", package = "WeightIt")
 #' 
 #' ## Estimating longitudinal propensity scores and weights
-#' ps1 <- glm(tx1 ~ age + gender + use0,
-#'            data = iptwExWide, 
+#' ps1 <- glm(A_1 ~ X1_0 + X2_0,
+#'            data = msmdata, 
 #'            family = "binomial")$fitted.values
-#' w1 <- ifelse(iptwExWide$tx1 == 1, 1/ps1, 1/(1-ps1))
-#' ps2 <- glm(tx2 ~ age + gender + use0 + tx1 + use1,
-#'            data = iptwExWide, 
+#' w1 <- ifelse(msmdata$A_1 == 1, 1 / ps1, 1 / (1 - ps1))
+#' 
+#' ps2 <- glm(A_2 ~ X1_1 + X2_1 +
+#'                A_1 + X1_0 + X2_0,
+#'            data = msmdata, 
 #'            family = "binomial")$fitted.values
-#' w2 <- ifelse(iptwExWide$tx2 == 1, 1/ps2, 1/(1-ps2))
-#' ps3 <- glm(tx3 ~ age + gender + use0 + tx1 + use1 + tx2 + use2,
-#'            data = iptwExWide, 
+#' w2 <- ifelse(msmdata$A_2 == 1, 1 / ps2, 1 / (1 - ps2))
+#' 
+#' ps3 <- glm(A_3 ~ X1_2 + X2_2 +
+#'                A_2 + X1_1 + X2_1 +
+#'                A_1 + X1_0 + X2_0,
+#'            data = msmdata, 
 #'            family = "binomial")$fitted.values
-#' w3 <- ifelse(iptwExWide$tx3 == 1, 1/ps3, 1/(1-ps3))
+#' w3 <- ifelse(msmdata$A_3 == 1, 1 / ps3, 1 / (1 - ps3))
 #' 
-#' w <- w1*w2*w3
+#' w <- w1 * w2 * w3
 #' 
 #' # Formula interface plus addl:
-#' bal.tab(list(tx1 ~ use0 + gender,
-#'              tx2 ~ use0 + gender + use1 + tx1,
-#'              tx3 ~ use0 + gender + use1 + tx1 + use2 + tx2),
-#'         data = iptwExWide, 
+#' bal.tab(list(A_1 ~ X1_0 + X2_0,
+#'              A_2 ~ X1_1 + X2_1 +
+#'                  A_1 + X1_0 + X2_0,
+#'              A_3 ~ X1_2 + X2_2 +
+#'                  A_2 + X1_1 + X2_1 +
+#'                  A_1 + X1_0 + X2_0),
+#'         data = msmdata, 
 #'         weights = w,
 #'         distance = list(~ps1, ~ps2, ~ps3),
-#'         addl = ~age*gender,
+#'         addl = ~X1_0 * X2_0,
 #'         un = TRUE)
 #' 
 #' # data frame interface:
-#' bal.tab(list(iptwExWide[c("use0", "gender")],
-#'              iptwExWide[c("use0", "gender", "use1", "tx1")],
-#'              iptwExWide[c("use0", "gender", "use1", "tx1", "use2", "tx2")]),
-#'         treat.list = iptwExWide[c("tx1", "tx2", "tx3")], 
+#' bal.tab(list(msmdata[c("X1_0", "X2_0")],
+#'              msmdata[c("X1_1", "X2_1", "A_1", "X1_0", "X2_0")],
+#'              msmdata[c("X1_2", "X2_2", "A_2", "X1_1", "X2_1",
+#'                        "A_1", "X1_0", "X2_0")]),
+#'         treat.list = msmdata[c("A_1", "A_2", "A_3")], 
 #'         weights = w,
 #'         distance = list(~ps1, ~ps2, ~ps3),
 #'         un = TRUE)

R/bal.tab.weightit.R

@@ -26,32 +26,36 @@
 #' @seealso
 #' * [bal.tab()] for details of calculations.
 #' 
-#' @examplesIf all(sapply(c("WeightIt", "twang"), requireNamespace, quietly = TRUE))
+#' @examplesIf all(sapply(c("WeightIt"), requireNamespace, quietly = TRUE))
 #' library(WeightIt)
 #' data("lalonde", package = "cobalt")
 #' 
 #' ## Basic propensity score weighting
 #' w.out1 <- weightit(treat ~ age + educ + race + 
 #'                        married + nodegree + re74 + re75, 
-#'                    data = lalonde, method = "ps")
+#'                    data = lalonde, method = "glm")
 #' bal.tab(w.out1, un = TRUE, m.threshold = .1, 
 #'         v.threshold = 2)
 #' 
 #' ## Weighting with a multi-category treatment
 #' w.out2 <- weightit(race ~ age + educ + married + 
 #'                        nodegree + re74 + re75, 
-#'                    data = lalonde, method = "ps",
-#'                    estimand = "ATE", use.mlogit = FALSE)
+#'                    data = lalonde, method = "glm",
+#'                    estimand = "ATE")
 #' bal.tab(w.out2, un = TRUE)
 #' bal.tab(w.out2, un = TRUE, pairwise = FALSE)
 #' 
 #' ## IPW for longitudinal treatments
-#' data("iptwExWide", package = "twang")
-#' wmsm.out <- weightitMSM(list(tx1 ~ use0 + gender,
-#'                              tx2 ~ use0 + gender + use1 + tx1,
-#'                              tx3 ~ use0 + gender + use1 + tx1 + use2 + tx2),
-#'                         data = iptwExWide,
-#'                         stabilize = TRUE)
+#' data("msmdata", package = "WeightIt")
+#' 
+#' wmsm.out <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
+#'                         A_2 ~ X1_1 + X2_1 +
+#'                             A_1 + X1_0 + X2_0,
+#'                         A_3 ~ X1_2 + X2_2 +
+#'                             A_2 + X1_1 + X2_1 +
+#'                             A_1 + X1_0 + X2_0),
+#'                    data = msmdata,
+#'                    method = "glm")
 #' bal.tab(wmsm.out)
 
 #' @exportS3Method bal.tab weightit

R/balance-summary.R

@@ -74,7 +74,7 @@
 #' covs0 <- splitfactor(covs, drop.first = "if2")
 #' bin.vars <- c(FALSE, FALSE, TRUE, TRUE, TRUE,
 #'               TRUE, TRUE, FALSE, FALSE)
-#' W <- WeightIt::weightit(treat ~ covs, method = "ps", 
+#' W <- WeightIt::weightit(treat ~ covs, method = "glm", 
 #'                         estimand = "ATE")
 #' weights <- W$weights
 #' 

vignettes/cobalt.Rmd

@@ -432,7 +432,7 @@ In `cobalt`, users can assess and present balance for multi-category treatments
 
 `love.plot()` allows for the display of each pairwise treatment or the range of balance across treatment pairs for each covariate. `bal.plot()` displays distributional balance for the requested covariate across all treatment groups.
 
-Below is an example of using `cobalt` with multi-category treatments. For this example, `race` will be the "treatment"; this type of analysis is not meant to be causal, but rather represents a method to examine disparities among groups accounting for covariates that might otherwise explain differences among groups. We will use `WeightIt` to generate balanced groups by estimating energy balancing weights.
+Below is an example of using `cobalt` with multi-category treatments. For this example, `race` will be the "treatment"; this type of analysis is not meant to be causal, but rather represents a method to examine disparities among groups accounting for covariates that might otherwise explain differences among groups. We will use `WeightIt` to generate balanced groups by estimating generalized propensity score weights using multinomial logistic regression.
 
 ```{r}
 data("lalonde", package = "cobalt")
@@ -442,8 +442,7 @@ data("lalonde", package = "cobalt")
 W.out.mn <- WeightIt::weightit(race ~ age + educ + married +
                                    nodegree + re74 + re75,
                                data = lalonde,
-                               method = "energy",
-                               use.mlogit = FALSE)
+                               method = "glm")
 ```
 
 First, we can examine balance numerically using `bal.tab()`. There are three possible pairwise comparisons, all of which can be requested with `which.treat = .all`. See `?bal.tab.multi` for more details.