# Plot fitted values from MBNMA model

`fitplot.Rd`

Plot fitted values from MBNMA model

## Usage

```
fitplot(
mbnma,
treat.labs = NULL,
disp.obs = TRUE,
n.iter = round(mbnma$BUGSoutput$n.iter/4),
n.thin = mbnma$BUGSoutput$n.thin,
...
)
```

## Arguments

- mbnma
An S3 object of class

`"mbnma"`

generated by running a time-course MBNMA model- treat.labs
A character vector of treatment labels with which to name graph panels. Can use

`mb.network()[["treatments"]]`

with original dataset if in doubt.- disp.obs
A boolean object to indicate whether raw data responses should be plotted as points on the graph

- n.iter
number of total iterations per chain (including burn in; default: 2000)

- n.thin
thinning rate. Must be a positive integer. Set

`n.thin`

> 1 to save memory and computation time if`n.iter`

is large. Default is`max(1, floor(n.chains * (n.iter-n.burnin) / 1000))`

which will only thin if there are at least 2000 simulations.- ...
Arguments to be sent to

`ggplot2::ggplot()`

## Value

Generates a plot of fitted values from the MBNMA model and returns a list containing
the plot (as an object of class `c("gg", "ggplot")`

), and a data.frame of posterior mean
fitted values for each observation.

## Details

Fitted values should only be plotted for models that have converged successfully.
If fitted values (`theta`

) have not been monitored in `mbnma$parameters.to.save`

then additional iterations will have to be run to get results for these.

## Examples

```
# \donttest{
# Make network
painnet <- mb.network(osteopain)
#> Reference treatment is `Pl_0`
#> Studies reporting change from baseline automatically identified from the data
# Run MBNMA
mbnma <- mb.run(painnet,
fun=temax(pool.emax="rel", method.emax="common",
pool.et50="abs", method.et50="random"))
#> 'et50' parameters must take positive values.
#> Default half-normal prior restricts posterior to positive values.
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 417
#> Unobserved stochastic nodes: 194
#> Total graph size: 8210
#>
#> Initializing model
#>
# Plot fitted values from the model
# Monitor fitted values for 500 additional iterations
fitplot(mbnma, n.iter=500)
#> `theta` not monitored in mbnma$parameters.to.save.
#> additional iterations will be run in order to obtain results
# }
```