Updated vignettes and plot_est_box

Fixed bugs and descriptions in the vignettes and plot_est_box

R/plot_est_box.R

@@ -60,14 +60,17 @@ plot_est_box <- function(input, ...) {
     UpperCI = upperbd
   )
 
+  # Alternate label positions to avoid overlap
+  label_position <- rep(c(1, -1), length.out = nrow(plot_data))
+
   # ggplot2 visualization
   ggplot2::ggplot(plot_data, ggplot2::aes(x = Treatment, y = Mean)) +
     ggplot2::geom_point(size = 3) +
     ggplot2::geom_errorbar(ggplot2::aes(ymin = LowerCI, ymax = UpperCI), width = 0.2, color = "blue") +
     ggplot2::labs(title = "Treatment Effect Estimates", x = "Treatment Level", y = "Effect") +
     ggplot2::theme_minimal() +
-    ggplot2::geom_text(ggplot2::aes(y = UpperCI + 0.05, label = paste0("Mean: ", round(Mean, 2))), vjust = -0.5) +
-    ggplot2::geom_text(ggplot2::aes(y = UpperCI + 0.25, label = paste0("95% CI: [", round(LowerCI, 2), ", ", round(UpperCI, 2), "]")), vjust = -0.5) +
+    ggplot2::geom_text(ggplot2::aes(y = UpperCI + 0.2 * label_position, label = paste0("Mean: ", round(Mean, 2))), vjust = -0.5) +
+    ggplot2::geom_text(ggplot2::aes(y = UpperCI + 0.4 * label_position, label = paste0("95% CI: [", round(LowerCI, 2), ", ", round(UpperCI, 2), "]")), vjust = -0.5) +
     ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
-    ggplot2::expand_limits(y = max(plot_data$UpperCI) + 0.15)
+    ggplot2::expand_limits(y = max(plot_data$UpperCI) + 0.5)
 }

vignettes/bayesmsm-censoring.Rmd

@@ -30,12 +30,14 @@ knitr::opts_chunk$set(
 - For Step 1, we estimate treatment weights $w_{ij}$ using posterior samples of the $\alpha$ and $\beta$ via fitting a series of logistic regressions in a Bayesian framework. The package incorporates both Inverse Probability of Treatment Weighting (IPTW) and Inverse Probability of Censoring Weighting (IPCW) to handle longitudinal data without and with right-censoring. For Step 2, $P_n(v_{ij})$ is estimated via non-parametric Bayesian bootstrap with $Dir(1,...,1)$ sampling weights.
 
 - The main functions in this package include:
-
+  
   - `bayesweight`: Calculates Bayesian weights for subject-specific treatment effects.
+  - `bayesweight_cen`: Calculates Bayesian weights for subject-specific treatment effects with right-censored data.
   - `bayesmsm`: Estimates marginal structural models using the calculated Bayesian weights.
   - `plot_ATE`: Plots the estimated Average Treatment Effect (ATE).
   - `plot_APO`: Plots the estimated Average Potential Outcome (APO).
   - `plot_est_box`: Plots the distribution of estimated treatment effects.
+  - `summary_bayesmsm`: Summarizes the model results from `bayesmsm`.
 
 - Installation
 
@@ -141,7 +143,7 @@ head(model$bootdata)
 
 # Visualization functions: `plot_ATE`, `plot_APO`, `plot_est_box`
 
-Similarly, we can use the built-in functions `plot_APO` and `summary_bayesmsm` to visualize and summarize the results.
+Similarly, we can use the built-in functions as well as `summary_bayesmsm` to visualize and summarize the results.
 
 - Plotting the Average Treatment Effect (ATE)
   - The `plot_ATE` function generates a plot of the estimated ATE with its 95% credible interval.

vignettes/bayesmsm-nocensoring.Rmd

@@ -32,10 +32,12 @@ knitr::opts_chunk$set(
 - The main functions in this package include:
 
   - `bayesweight`: Calculates Bayesian weights for subject-specific treatment effects.
+  - `bayesweight_cen`: Calculates Bayesian weights for subject-specific treatment effects with right-censored data.
   - `bayesmsm`: Estimates marginal structural models using the calculated Bayesian weights.
   - `plot_ATE`: Plots the estimated Average Treatment Effect (ATE).
   - `plot_APO`: Plots the estimated Average Potential Outcome (APO).
   - `plot_est_box`: Plots the distribution of estimated treatment effects.
+  - `summary_bayesmsm`: Summarizes the model results from `bayesmsm`.
 
 - Installation