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

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

# Introduction

The `mice` function is one of the most used functions to apply 
multiple imputation. This page shows how functions in the `psfmi` 
package can be easily used in combination with `mice`. In this way 
multivariable models can easily be developed in combination with mice.

# Installing the psfmi and mice packages

You can install the released version of psfmi with:

``` r
install.packages("psfmi")
```
And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("mwheymans/psfmi")
```
You can install the released version of mice with:

``` r
install.packages("mice")

``` 
# Examples 

+ [mice and psfmi for pooling logistic regression models] 
+ [mice and psfmi for selecting logistic regression models]

## mice and psfmi for pooling logistic regression models

```{r}

  library(psfmi)
  library(mice)

  imp <- mice(lbp_orig, m=5, maxit=5) 
  
  data_comp <- complete(imp, action = "long", include = FALSE)
  
  library(psfmi)
  pool_lr <- psfmi_lr(data=data_comp, nimp=5, impvar=".imp", 
                      formula=Chronic ~ Gender + Smoking + Function + 
                      JobControl + JobDemands + SocialSupport, method="D1")
  pool_lr$RR_model
  
 
```

Back to [Examples]

## mice and psfmi for selecting logistic regression models

```{r}

  library(psfmi)
  library(mice)

  imp <- mice(lbp_orig, m=5, maxit=5) 
  
  data_comp <- complete(imp, action = "long", include = FALSE)
  
  library(psfmi)
  pool_lr <- psfmi_lr(data=data_comp, nimp=5, impvar=".imp", 
                      formula=Chronic ~ Gender + Smoking + Function + 
                      JobControl + JobDemands + SocialSupport, 
                      p.crit = 0.157, method="D1", direction = "FW")
  
  pool_lr$RR_model_final
 
```

Back to [Examples]