---
title: "Pool Model Performance"
author: "Martijn W Heymans"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Pool Model Performance}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

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

# Introduction

The `psfmi` package includes the function `pool_performance`, to pool the performance 
measures of logistic and Cox regression models. This vignette show you how to use this function.

# Examples 

# Performance Logistic regression model

The performance of a logistic regression model across multiply imputed datasets can be obtained 
as follows. 

```{r}

perf <- pool_performance(data=lbpmilr, nimp=5, impvar="Impnr", 
  formula = Chronic ~ Gender + Pain + Tampascale + 
  Smoking + Function + Radiation + Age + 
  Duration + BMI, 
  cal.plot=TRUE, plot.method="mean", 
  groups_cal=10, model_type="binomial")
  
perf

``` 

# Performance Cox regression model

For a Cox regression model the following code can be used.

```{r}

perf <- pool_performance(data=lbpmicox, nimp=5, impvar="Impnr", 
  formula = Surv(Time, Status) ~ Duration + Pain + Tampascale + 
  factor(Expect_cat) + Function + Radiation + Age , 
  cal.plot=FALSE, model_type="survival")
  
perf

```