---
title: "SBMTrees: Introduction and Usage"
output: html_vignette
vignette: >
  %\VignetteIndexEntry{SBMTrees: Introduction and Usage}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```


# Introduction
The R package SBMTrees (Sequential Imputation with Bayesian Trees Mixed-Effects Models) provides a powerful Bayesian non-parametric framework for imputing missing covariates and outcomes in longitudinal data under the Missing at Random (MAR) assumption. The package leverages centralized Dirichlet Process (CDP) Normal Mixture priors to model non-normal random effects and errors, offering robust handling of model misspecification and capturing complex relationships in longitudinal data.

This vignette introduces the key functionalities of the package, including:

* Prediction: Using `BMTrees_prediction` to predict longitudinal outcomes.
* Imputation: Using `sequential_imputation` for imputing missing values.

# Install and load the package
```{r setup}
library(SBMTrees)
library(mitml)
library(lme4)
```

# Prediction
The `BMTrees_prediction` function is used to predict longitudinal outcomes based on Bayesian Mixed-Effects Models. Below is an example of how to generate data, split it into training and testing datasets, and run predictions.
```{r prediction_sim}
# Simulate data
data <- simulation_prediction(
  n_subject = 100, 
  seed = 1234, 
  nonlinear = TRUE, 
  nonrandeff = TRUE, 
  nonresidual = TRUE
)

# Extract training and testing datasets
X_train <- data$X_train
Y_train <- data$Y_train
Z_train <- data$Z_train
subject_id_train <- data$subject_id_train

X_test <- data$X_test
Z_test <- data$Z_test
subject_id_test <- data$subject_id_test

Y_test_true <- data$Y_test_true

```

We then run the prediction model `BMTrees`, with 3 burn-in iterations and 4 posterior samples. The number of burn-in and posterior iterations should be increase to 3000 and 4000, respectively. Here we only use the small numbers to simply debug.
```{r prediction}
# Fit the predictive model
model <- BMTrees_prediction(
  X_train, Y_train, Z_train, subject_id_train, 
  X_test, Z_test, subject_id_test, 
  model = "BMTrees", 
  binary = FALSE, 
  nburn = 3L, 
  npost = 4L, 
  skip = 1L, 
  verbose = FALSE, 
  seed = 1234
)

# Posterior expectation for the testing dataset
posterior_predictions <- model$post_predictive_y_test
head(colMeans(posterior_predictions))
```

To evaluate the model's predictive performance, we compute the Mean Absolute Error (MAE), and the Mean Square Error (MSE). We also calculate the 95% posterior predictive intervals to check coverage, and visualize the results using scatterplots of true versus predicted values. 

```{r prediction_evaluation}
point_predictions = colMeans(posterior_predictions)

# Compute MAE
mae <- mean(abs(point_predictions - Y_test_true))
cat("Mean Absolute Error (MAE):", mae, "\n")

# Compute MSE
mse <- mean((point_predictions - Y_test_true)^2)
cat("Mean Squared Error (MSE):", mse, "\n")

# Compute 95% credible intervals
lower_bounds <- apply(posterior_predictions, 2, quantile, probs = 0.025)
upper_bounds <- apply(posterior_predictions, 2, quantile, probs = 0.975)

# Check if true values fall within the intervals
coverage <- mean(Y_test_true >= lower_bounds & Y_test_true <= upper_bounds)
cat("95% Posterior Predictive Interval Coverage:", coverage * 100, "%\n")



plot(Y_test_true, point_predictions, 
     xlab = "True Values", 
     ylab = "Predicted Values", 
     main = "True vs Predicted Values")
abline(0, 1, col = "red") # Add a 45-degree reference line

```


# Multiple Imputation
The `sequential_imputation` function is used to impute missing covariates and outcomes in longitudinal data. Below is an example of how to generate longitudinal data with MAR missing, and run imputations.
```{r imputation_sim}
# Simulate data with missing values
data <- simulation_imputation(
  n_subject = 100, 
  seed = 1234, 
  nonrandeff = TRUE, 
  nonresidual = TRUE, 
  alligned = FALSE
)

# Extract components of the dataset
X_mis <- data$X_mis   # Covariates with missing values
Y_mis <- data$Y_mis   # Outcomes with missing values
Z <- data$Z           # Random predictors
subject_id <- data$subject_id

```

We then run the sequential imputation with `BMTrees`, with 3 burn-in iterations, 40 posterior iterations, and sample one posterior sample for every 2 posterior iterations, ensuring 20 multiply-imputed sets are generated. The number of burn-in and posterior iterations should be increase to 3000 and 4000, respectively. Here we only use the small numbers to simply debug.
```{r imputation}
# Run the sequential imputation
imputed_model <- sequential_imputation(
  X_mis, Y_mis, Z, subject_id, 
  type = rep(0, ncol(X_mis)), 
  binary_outcome = FALSE, 
  model = "BMTrees", 
  nburn = 3L, 
  npost = 40L, 
  skip = 2L, 
  verbose = FALSE, 
  seed = 1234
)

# Extract imputed data
imputed_data <- imputed_model$imputed_data
dim(imputed_data) # Dimensions: posterior samples x observations x variables
```

To evaluate the model's imputation performance, we apply Rubin`s rule to estimate linear mixed-effects model on the multiply-imputed sets.
```{r imputation_evaluation}
# create structure which can be used in mitml
MI_data = list()
for (i in 1:dim(imputed_data)[1]) {
  MI_data[[i]] = cbind(as.data.frame(imputed_data[i,,]), Z, subject_id)
  colnames(MI_data[[i]]) = c(colnames(X_mis), "Y", "Z1", "Z2", "Z3", "subject_id")
}
MI_data <- as.mitml.list(MI_data)  # Replace with actual datasets
# Fit the model on each imputed dataset
lmm_results <- with(MI_data, lmer(Y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + (0 + Z1 + Z2 + Z3 | subject_id)))

# Pool fixed effects using Rubin's Rules
pooled_results <- testEstimates(lmm_results)

# Print pooled results
print(pooled_results)

```

# Summary
The SBMTrees package provides flexible tools for handling missing values and making predictions in longitudinal data. By leveraging Bayesian non-parametric methods, it effectively addresses challenges associated with model misspecification, non-normal random effects, and non-normal errors.

For further details, please refer to the package documentation and the paper: Nonparametric Bayesian Additive Regression Trees for Predicting and Handling Missing Covariates and Outcomes in Longitudinal Data.


# License

This vignette is part of the SBMTrees R package and is distributed under the terms of the GNU General Public License (GPL-2).
