---
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  pdf_document:
    toc: true
  word_document:
    toc: true
  html_document:
    toc: true
---

---
params:
  ctr: ""
  title: ""
---
---
subtitle: `r params$ctr$save_dir`
title: `r params$title `
---

```{r setup, include=FALSE}
library(ggplot2)
knitr::opts_chunk$set(echo = FALSE,message=FALSE)
cats <- ctr %>% get_cats

```
\newpage

# Key model diagnostics

## Parameter Table
```{r,echo=FALSE,warning=FALSE,message=FALSE}
ctr %>% param_table(return_table = FALSE)
```

## NPDE vs PRED
```{r npde_pred,fig.align="center",fig.height=5, fig.width=6}
ctr %>% pmx_plot_npde_pred
```
\newpage

## NPDE vs TIME
```{r npde_time,fig.align="center",fig.height=5, fig.width=6}
ctr %>% pmx_plot_npde_time
```
\newpage

## IWRES vs IPRED
```{r iwres_ipred,fig.align="center",fig.height=5, fig.width=6}
ctr %>% pmx_plot_iwres_ipred
```
\newpage

## IWRES vs TIME
```{r iwres_time,fig.align="center",fig.height=5, fig.width=6}
ctr %>% pmx_plot_iwres_time
```
\newpage

## Distribution of random effects 
```{r eta_box,message=FALSE,fig.align="center",fig.height=7, fig.width=7}
ctr %>% pmx_plot_eta_box
```
\newpage

## VPC
```{r vpc,fig.align="center",fig.height=6, fig.width=7}
ctr %>% pmx_plot_vpc
```

\newpage

# Supplementary diagnostics

## Distribution of IWRES

### QQ plot of IWRES
```{r iwres_qq, fig.height=7, fig.width=7}
ctr %>% pmx_plot_iwres_qq
```

\newpage

### Distribution of IWRES
```{r iwres_dens, fig.width=7, fig.height=7}
ctr %>% pmx_plot_iwres_dens
```
\newpage


## EBE-based diagnostics

### Distribution and correlation of EBEs

```{r eta_matrix,message=FALSE,fig.align="center",fig.height=7, fig.width=7}
ctr %>% pmx_plot_eta_matrix
```


\newpage
### EBE vs discrete covariates
```{r eta_cats,message=FALSE,fig.align="center",fig.height=9, fig.width=7}
ctr %>% pmx_plot_eta_cats
```
\newpage

### EBE vs continuous covariates
```{r eta_conts,message=FALSE,fig.align="center",fig.height=9, fig.width=7}
ctr %>%  pmx_plot_eta_conts
```
\newpage

## Observations vs predictions

### DV vs PRED
```{r dv_pred, fig.height=7, fig.width=7}
ctr %>% pmx_plot_dv_pred
```
\newpage

### DV vs IPRED
```{r dv_ipred, fig.height=7, fig.width=7}
ctr %>% pmx_plot_dv_ipred
```
\newpage

## Residual plots

### |IWRES| vs IPRED
```{r dv_abs_iwres_ipred,fig.align="center",fig.height=5, fig.width=6}
ctr %>% pmx_plot_abs_iwres_ipred
```
\newpage

# Individual plots (selection)

```{r indiv,message=FALSE,fig.align="center",fig.height=10, fig.width=8}
 ctr %>% pmx_plot_individual
```
\newpage

# Stratified key diagnostics 

## NPDE vs TIME by categorical covariates
```{r npde_time_strats,fig.align="center"}
ctr %>% pmx_plot_cats("npde_time")
```
\newpage

## IWRES vs TIME by categorical covariates
```{r iwres_time_strats,fig.align="center"}
ctr %>% pmx_plot_cats("iwres_time")
```
\newpage

## VPC by categorical covariates
```{r vpc_strats,fig.align="center"}
ctr %>% pmx_plot_cats("pmx_vpc")
```
