Fixed gcomp_stan() bug

R/IPD_stats.R

@@ -147,8 +147,8 @@ IPD_stats.default <- function() {
 #' using bootstrapping
 #'
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @export
 #' 
@@ -174,8 +174,8 @@ IPD_stats.maic <- function(strategy,
 #' IPD_stats.stc
 #' 
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #' @export
 #' 
 IPD_stats.stc <- function(strategy,
@@ -196,8 +196,8 @@ IPD_stats.stc <- function(strategy,
 #' IPD_stats.gcomp_ml
 #'
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @return
 #' @export
@@ -220,8 +220,8 @@ IPD_stats.gcomp_ml <- function(strategy,
 #' IPD_stats.gcomp_stan
 #'
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @return
 #' @export
@@ -230,7 +230,7 @@ IPD_stats.gcomp_stan <- function(strategy,
                                  ipd, ald) {
 
   ppv <- gcomp_stan(formula = strategy$formula,
-                    dat = ipd)
+                    ipd = ipd, ald = ald)
 
   # compute marginal log-odds ratio for A vs C for each MCMC sample
   # by transforming from probability to linear predictor scale

R/gcomp_stan.R

@@ -2,8 +2,8 @@
 #' G-computation using Stan
 #'
 #' @param formula 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @return
 #' @export
@@ -15,9 +15,7 @@ gcomp_stan <- function(formula = as.formula("y ~ X3 + X4 + trt*X1 + trt*X2"),
 
   # remove treatment
   cov_names <- cov_names[cov_names != treat_names]
-
-  n_covariates <- length(covariate_names)
-
+  n_covariates <- length(cov_names)
   rho <- cor(ipd[, cov_names])
 
   # covariate simulation for BC trial using copula package
@@ -40,7 +38,6 @@ gcomp_stan <- function(formula = as.formula("y ~ X3 + X4 + trt*X1 + trt*X2"),
   # simulated BC pseudo-population of size 1000
   x_star <- as.data.frame(rMvdc(1000, mvd))
 
-  browser()
   colnames(x_star) <- cov_names
 
   # outcome logistic regression fitted to IPD using MCMC (Stan)

man-roxygen/args-ald.R

@@ -0,0 +1 @@
+#' @param ald Aggregate-level data

man-roxygen/args-ipd.R

@@ -0,0 +1 @@
+#' @param ipd Individual-level data