`vignettes/plots-mixpoissonreg.Rmd`

`plots-mixpoissonreg.Rmd`

The first, and maybe the easiest, customizable option is the choice of plots to be displayed. The current available plots are (together with their corresponding numbers):

*Residuals vs Obs. number**Normal Q-Q**Cook’s distance**Generalized Cook’s distance**Cook’s dist vs Generalized Cook’s dist**Response vs Fitted means*

These plots can be chosen by giving a list of the numbers of the wanted plots in the `which`

argument.

If the argument `which`

is not provided, then, by default, the plots 1, 2, 5 and 6 will be displayed:

```
library(mixpoissonreg)
fit <- mixpoissonreg(daysabs ~ gender + math + prog | gender + math + prog,
data = Attendance)
plot(fit)
```

If we want only a single plot, we simply indicate its number. For instance, if we only want the plot of Cook’s distances, we simply set `which = 3`

:

`plot(fit, which = 3)`

If we want more than one, we provide a list with the desired plots. Suppose we want the global influence-related plots, that is, plots 3, 4 and 5. Then, we set `which = c(3,4,5)`

:

In this section we will describe how to customize titles and subtitles.

First of all, by default, the type of the fitted model (that is, if it is a Negative-Binomial or Poisson Inverse Gaussian regression) is included in each caption. See, for instance, the plot below:

`plot(fit, which = 1)`

To remove the model type, simply set `include.modeltype`

to `FALSE`

:

`plot(fit, which = 1, include.modeltype = FALSE)`

One should notice that in both plots, the type of the residual was only included in the *y*-axis label. To also include the type of the residual at the caption, simply set `include.residualtype`

to `TRUE`

:

`plot(fit, which = 1, include.residualtype = TRUE)`

One can define one common title to all plots by setting the `main`

parameter to such common title. For instance,

`plot(fit, main = "Mixed Poisson Regression")`

The `main`

parameter also works to provide an additional title to a single plot, by also setting the `which`

parameter to the desired plot:

`plot(fit, which = 1, main = "Mixed Poisson Regression")`

The captions (displayed above the plot) can be altered by setting the `caption`

parameter to a list containing the wanted captions. One drawback is that we have to provide a list containing the captions we want in the positions of the plots we want. For example, if we only have one plot, the following example will change the caption:

`plot(fit, which = 1, caption = "My caption")`

However, it will not work for the remaining plots, for instance:

`plot(fit, which = 2, caption = "My caption")`

A simple way to circumvent the above situation is to create an empty list and set the titles at the positions we want:

```
my_captions <- rep("",6)
my_captions[2] <- "My caption 2"
my_captions[4] <- "My caption 4"
plot(fit, which = c(2,4), caption = my_captions)
```

Notice that, since we did not change the `include.modeltype`

argument, the model type are added in the new captions.

We can change the size of the captions with the argument `cex.caption`

. The default size is `1`

. So, for instance,

```
my_captions <- rep("",6)
my_captions[2] <- "My caption 2"
my_captions[4] <- "My caption 4"
plot(fit, which = c(2,4), caption = my_captions, cex.caption = 1.5)
```

We can also change the subcaption. By default, the subcaption is a simplified version of the call to the `mixpoissonreg`

function that was used to fit the model.

We must have some caution on describing the subcaption. If each plot is given in one window (without using the `par(...)`

command, for example), then the subcaption is the caption below the *x*-axis label. However, if multiple plots are given at once, and there is space on the upper part of the plot, then the subcaption is a general caption for all the plots.

We will illustrate the above description with examples to make it clearer.

The subcaption can be altered by setting the `sub.caption`

parameter to the desired caption.

Thus, we begin by providing the plots without the usage of `par`

function.

`plot(fit, which = 1, sub.caption = "My subcaption")`

Notice that the subcaption is below the *x*-axis label. The same happens even if we there is more than one plot and we do not use the `par`

function:

`plot(fit, sub.caption = "My subcaption")`

Now, notice the position of the subcaption when we gather multiple plots using the `par`

function (while we provide room the subcaption by using the `oma`

argument):

```
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0), mar=par("mar")/2)
plot(fit, sub.caption = "My subcaption")
```

In the previous case, that is, the case in which we have the subcaption above all plots, one can change the size of the subcaption using the argument `cex.oma.main`

.

For instance,

In this section we show how to customize colors, sizes and types of lines and points.

First of all, notice that several graphical parameters may be passed as additional parameters through the three dots ellipsis. For instance, we may pass the argument `pch`

from `plot.default`

to change the type of points:

`plot(fit, which = 1, pch = 2)`

Another example is the `main`

color. Let us set a main title with the `main`

argument, and change its color with the `col.main`

argument:

`plot(fit, which = 1, main = "Plot 1", col.main = "red")`

We can also change the `main`

size, by using the `cex.main`

argument:

`plot(fit, which = 1, main = "Plot 1", col.main = "red", cex.main = 2)`

Similarly, we can change the *x* and *y* label’s colors by using the `col.lab`

argument:

`plot(fit, which = 1, col.lab = "red")`

As well as change their sizes by using the `cex.lab`

argument:

`plot(fit, which = 1, cex.lab = 2)`

We can change the `sub.caption`

color by using the `col.sub`

argument:

`plot(fit, which = 1, col.sub = "red")`

and change its size by using the `cex.sub`

argument:

`plot(fit, which = 1, cex.sub = 2)`

Now, we deal with specific arguments of the `plot.mixpoissonreg`

. We begin by dealing with the point colors. To change the colors of the points, we use the `col.points`

argument:

`plot(fit, which = 1, col.points = "red")`

To change the point sizes, we use the argument `cex.points`

:

`plot(fit, which = 1, cex.points = 2)`

We will now deal with point labels. First of all, we may change the color of the point labels by using the argument `col.id`

:

`plot(fit, which = 1, col.id = "red")`

We can change the point labels’ sizes by using the `cex.id`

argument:

`plot(fit, which = 1, cex.id = 2)`

Let us now customize the lines in the Cook’s distance plots, namely plots 3 and 4. By default, the line type for Cook’s distance plots is `'h'`

:

Let us change the line type for Cook’s distance plots to points and change the point types to crosses:

Let us now change the colors of titles and captions. To change the caption’s color, we use the `col.caption`

argument:

`plot(fit, which = 1:2, col.caption = "red")`

Finally, let us customize the quantile-quantile plots with and without simulated envelopes, namely, plot 2. The first customization is to remove the diagonal Q-Q line in the quantile-quantile plot without simulated envelopes:

`plot(fit, which = 2, qqline = FALSE)`

We can change the qqline color by using the `col.qqline`

argument:

`plot(fit, which = 2, col.qqline = "red")`

Finally, let us consider a fitting with simulated envelopes:

```
fit_env <- mixpoissonregML(daysabs ~ gender + math + prog | gender +
math + prog, envelope = 100, data = Attendance)
plot(fit_env, which = 2)
```

Let us first change the color of the lines of the upper and lower bands of the simulated envelopes:

`plot(fit_env, which = 2, line_col_env = "red")`

Let us now change the color of the median curve of the simulated envelopes:

`plot(fit_env, which = 2, line_col_median = "red")`

Let us also change the fill color of the simulated envelopes:

`plot(fit_env, which = 2, line_col_env = "red", fill_col_env = "red")`

Let us change the color transparency. To such an end we use the argument `fill_alpha_env`

:

```
plot(fit_env, which = 2, line_col_env = "red", fill_col_env = "red",
fill_alpha_env = 0.3)
```

By default the `plot.mixpoissonreg`

function always identifies the 3 “most extreme” points. We can change it so that it does not identify any points by setting the argument `id.n`

to 0:

`plot(fit, id.n = 0)`

We can also increase the number of identified points. For instance:

`plot(fit, id.n = 5)`

Finally, we can change the labels of the identified points with the argument `labels.id`

. For instance, we may want to have the value of the `prog`

covariate instead:

`plot(fit, labels.id = model.frame(fit)$prog)`