`vignettes/prior.Rmd`

`prior.Rmd`

`simulate_iid()`

generates a number of iid random intercepts from a zero mean Gaussian distribution with variance \(\sigma^2\). The variance is either specified through the standard deviance \(\sigma\) or the precision \(\tau = 1 /\sigma ^2\). Specifying both leads to an error, even if both arguments have compatible arguments.

```
library(inlatools)
str(x <- simulate_iid(sigma = 1))
#> 'sim_iid' num [1:1000] 0.228 -0.491 -0.998 -0.641 -0.87 ...
#> - attr(*, "sigma")= num 1
str(y <- simulate_iid(tau = 100))
#> 'sim_iid' num [1:1000] 9.05e-06 -6.23e-02 2.33e-02 -6.30e-02 1.31e-02 ...
#> - attr(*, "sigma")= num 0.1
```

```
simulate_iid(sigma = 0.1, tau = 100)
#> Error: either 'sigma' or 'tau' must be NULL
```

The default `plot()`

on the simulated random intercepts yields a density with indication of the baseline and quantiles. The plot title displays the standard deviation, variance and precision used to simulate the random intercepts. This makes it easy to simulate the random intercept based on a standard deviation and get the precision required for the prior.

`plot(x)`

Often we are using distributions were the relationship between the mean and the linear predictor is defined through a link function. Therefore we added a `link`

argument to the plot function. This will apply the back transformation to the random intercepts, making it easier to interprete their effect on the original scale.

`plot(x, link = "log")`

The plot shows the effect of the random intercept + the baseline. The default baseline are chosen to get an informative plot. `link = "identity`

uses `baseline = 0`

as default. `link = "log"`

uses `baseline = 1`

, so that the random intercepts can be interpreted as relative effects. In case of `link = "logit"`

, the absolute effect on the natural scale, depends on the baseline. Therefore we use `baseline = seq(0, 0.5, length = 21)`

as default. When more than one baseline is defined, a line plot is used instead of a density. Each random intercept is depicted as a separate line.

`plot(x, link = "logit")`

The user can override the default baseline. So if you want a density plot in combination with `link = "logit"`

, then you need to specify a single baseline. Note that the baseline is always expressed on the natural scale.

`plot(x, link = "logit", baseline = 0.7)`

Likewise you can get a line plot for `link = "identity"`

or `link = "log"`

if you specify a vector of baselines. The result of `plot()`

is a `ggplot()`

object, so you can alter it using standard ggplot functions.

```
library(ggplot2)
plot(x, link = "log", baseline = c(0.01, 0.1, 0.5)) +
scale_x_log10("baseline") +
scale_y_log10("effect")
#> Scale for 'x' is already present. Adding another scale for 'x', which
#> will replace the existing scale.
#> Scale for 'y' is already present. Adding another scale for 'y', which
#> will replace the existing scale.
```

By default the random intercepts are center so that their mean matches the baseline. The alternative are `center = "bottom"`

and `center = "top"`

. In those cases, the random effects are centered to that the baseline matches the lowest (`center = "bottom"`

) or the highest (`center = "top"`

) quantile. This is useful in case you want to get an idea of the difference between the lowest quantile and some other point (e.g. the largest quantile). The plot below illustrates that ratio of more that 50 between extreme random intercepts are not uncommon when \(\sigma = 1\) with a log link.

`plot(x, link = "log", center = "bottom")`

The user has the option to specify custom quantiles.

INLA assumes Gaussian priors for the fixed effects. Hence we can use `simulate_iid()`

to get a feeling of these priors too. Below we give two examples. The first shows the default prior for fixed effects. The second one shows an informative prior for a fixed effect.

```
fixed <- simulate_iid(tau = 0.001)
plot(fixed)
```

```
fixed <- simulate_iid(tau = 0.1)
plot(fixed, baseline = 14)
```