README.Rmd

---
title: ""
output: github_document
---

```{r,setup,include=FALSE}
knitr::opts_chunk$set(comment = '.', message=FALSE, warning = FALSE, 
                      fig.path="man/figures/README-")
```

# vera
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## Overview
local sensitivity analysis in R with mrgsolve


## Model
Load the vera package and a PBPK model from the mrgsolve package.  We 
decrease the tolerance a bit as well as the maximum step size.
```{r,message=FALSE}
library(vera)

mod <- modlib(
  "pbpk", 
  end = 12,     delta = 0.1, 
  atol = 1E-20, rtol  = 1E-12, hmax = 0.5
) %>% obsonly
```

## Scenario
Create a function that uses the model to simulate a certain scenario.  For now, 
we just simulate a single dose.
```{r}
fun <- function(p,dose) {
  mod %>% 
    param(p) %>% 
    ev(dose) %>% 
    mrgsim()
}

d <- ev(amt = 100)

fun(param(mod),d) %>% plot(Cp+Amu~time)
```

## Sensitivity analysis

Use `vera::lsa()`.  We pick the __parameters__ that we want to fiddle with 
(`par`):

- `Kpli` liver partition coefficient
- `Kpmu` muscle partition coefficient
- `Kpad` adipose tissue partition coefficient
- `BW` body weight 

Also specify the __output__ that we want to look at (`var`):

- `Cp` - venous concentration
- `Amu` - amount in the muscle compartment

`d` gets passed through to the function as `dose`. 

```{r}
out <- lsa(mod, fun, par = "Kpli,Kpmu,Kpad,BW", var = "Cp,Amu", dose = d)
```

## Ouput

The output is long and ready to send in to `ggplot2`.  
```{r}
head(out)
```

There is a default plotting method as well.

```{r}
plot(out)
```

## Plot with ggplot2
```{r}
library(ggplot2)

filter(out, par=="Kpmu") %>% 
  ggplot(aes(time,sens,col=var)) + 
  geom_line(lwd=1) + theme_bw() + 
  scale_color_brewer(palette="Set2") + 
  theme(legend.position="top") + 
  facet_wrap(~par) + geom_hline(yintercept=0,lty=2)
```


<hr>

## More info

See [inst/doc/about.md (on GitHub only)](inst/doc/about.md) for more details.