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

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

# Stability analysis

The psfmi package contains a function to conduct stability analyses of logistic, Cox and 
Mixed models. With this function the stability of models selected and the bootstrap inclusion 
frequency can be evaluated. For logistic and Cox regression models bootstrapping 
is than used and for Multilevel models cluster bootstrapping.

# Examples 

* [Stability analysis of Logistic regression model] 
* [Stability analysis of Cox regression model]

## Stability analysis of Logistic regression model

```{r}

  library(psfmi)
  psfmi_res <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
                      predictors=c("Gender", "Smoking", "JobControl", "Age", "Duration",
                         "JobDemands", "SocialSupport"), p.crit =0.157, method="MPR",
                      cat.predictors = "Satisfaction", int.predictors = c("Gender:JobControl"),
                      direction = "BW")

   stab_res <- psfmi_stab(psfmi_res, direction="FW", start_model = TRUE,
      boot_method = "single", nboot=20, p.crit=0.05)
  
   stab_res$bif
   
  stab_res$bif_perc
  
  stab_res$model_stab
  

```  

Back to [Examples]

### Stability analysis of Cox regression model

```{r}

  library(psfmi)
  pool_lr <- psfmi_coxr(formula = Surv(Time, Status) ~ Pain + factor(Satisfaction) + rcs(Tampascale,3) +
                        Radiation + Radiation*factor(Satisfaction) + Age + Duration + Previous +
                        Radiation*rcs(Tampascale, 3), data=lbpmicox, p.crit = 0.157, direction="FW",
                        nimp=5, impvar="Impnr", keep.predictors = NULL, method="D1")
  pool_lr$RR_Model
  pool_lr$multiparm

  stab_res <- psfmi_stab(pool_lr, direction="FW", start_model = TRUE,
      boot_method = "single", nboot=20, p.crit=0.05)
  
  stab_res$bif
  
  stab_res$bif_perc
  
  stab_res$model_stab

```  

Back to [Examples]

