---
title: "Bootstrap Simulation for model prediction"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Bootstrap Simulation for model prediction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

You can make a bootstrap simulation for model prediction. When writing bootPredict function and this vignette, I was inspired from package finalfit by Ewen Harrison. For example, you can predict survival after diagnosis of breast cancer.

```{r}
library(autoReg)
library(dplyr)  # for use `%>%`
data(GBSG2,package="TH.data")
head(GBSG2)
```

Data `GBGS2` in TH.data package is a data frame containing the observations from the German Breast Cancer Study Group 2. In this data, the survival status of patients is coded as 0 or 1 in the variable `cens`. Whether the patient receive the hormonal therapy or not is recorded as 'no' or 'yes' in variable `horTh`. The number of positive lymph nodes are recoded in pnodes. You can make a logistic regression model with the following R code.

```{r}
GBSG2$cens.factor=factor(GBSG2$cens,labels=c("Alive","Died"))
fit=glm(cens.factor~horTh+pnodes+menostat,data=GBSG2,family="binomial")
summary(fit)
```

You can make a publication-ready table with the following R command.

```{r}
autoReg(fit) %>% myft()
```

You can draw a plot summarizing the model.

```{r,fig.width=8,fig.height=7}
modelPlot(fit)
```

For bootstrapping simulation, you can make new data with the following R code.
```{r}
newdata=expand.grid(horTh=factor(c(1,2),labels=c("no","yes")),
                    pnodes=1:51,
                    menostat=factor(c(1,2),labels=c("Pre","Post")))
```

You can make a bootstrapping simulation with bootPredict() function. You can set the number of simulation by adjusting R argument.
```{r}
df=bootPredict(fit,newdata,R=500)
head(df)
```


With this result, you can draw a plot showing bootstrapping prediction of breast cancer.

```{r,fig.width=8,fig.height=7}
library(ggplot2)
ggplot(df,aes(x=pnodes,y=estimate))+
  geom_line(aes(color=horTh))+
  geom_ribbon(aes(ymin=lower,ymax=upper,fill=horTh),alpha=0.2)+
  facet_wrap(~menostat)+
  theme_bw()+
  labs(x="Number of positive lymph nodes", y="Probability of death")
```