---
title: "Replicate your study"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Replicate your study}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r, results='asis', echo=F, message=F, warning=F}
if (campsis::onCran()) {
  cat("This vignette was not built on CRAN. Please check out the online version [here](https://calvagone.github.io/campsis.doc/articles/v10_replicate_study.html).")
  knitr::knit_exit()
}
```

```{r, results='hide', echo=F, message=F, warning=F}
library(campsis)
```

This vignette shows how a simulation can be replicated.

### Simulate uncertainty on percentiles

Assume the following model is used. This model is a 2-compartment model without absorption compartment which has been fitted on some data.

```{r}
model <- model_suite$testing$other$my_model1
```

It contains a variance-covariance matrix with the uncertainty on all the estimated parameters.

```{r}
model
```

We are interested to see the uncertainty on the simulated concentration percentiles over time.
Let's mimic the protocol that was implemented in the study.

```{r}
ds <- Dataset(50) %>%
  add(Infusion(time=(0:6)*24, amount=1000, compartment=1)) %>%
  add(Observations(times=seq(0, 7*24)))
```

Let's now simulate this model with parameter uncertainty.  
Argument `replicates` specifies how many times the simulation is replicated.  
Argument `outfun` is a function that is going to be called after each simulation on the output data frame.

```{r, message=F}
results <- model %>% simulate(dataset=ds, replicates=10, outfun=~PI(.x, output="Y"), seed=1)
results %>% head()
```

Function `vpcPlot` allows to quickly visualize such results.

```{r replicate_your_study_varcov , fig.align='center', fig.height=4, fig.width=8}
vpcPlot(results)
```

