# Visualizing marginal effects in sdmTMB models with ggeffects

#### Julia Indivero, Sean Anderson, Lewis Barnett, Philina English, Eric Ward

#### 2022-11-24

Source:`vignettes/ggeffects.Rmd`

`ggeffects.Rmd`

**If the code in this vignette has not been evaluated, a
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The package ggeffects can be used to plot marginal effects of given predictor variables in sdmTMB models.

A advantage to this approach over visreg, is that ggeffects calculates marginal effects with the effects package using the parameter covariance matrix. This is nearly instant compared to visreg, which has to calculate conditional effects by calculating predictions with TMB.

A disadvantage to using ggeffects is that it will only work for
regular linear effects in the main model formula. I.e., it will not work
with smoothers (internally these are random effects) or breakpoint
(`breakpt()`

) effects.

Another important distinction is that
`ggeffects::ggeffect()`

is plotting **marginal**
effects. This means the effects are “marginalized” or “averaged” over
the other fixed effects. `visreg::visreg()`

is plotting
**conditional** effects. This means they are conditional on
the other predictors being set to certain values.
`ggeffects::ggpredict()`

also does conditional effects, but
this has not yet been set up in sdmTMB using the CRAN version of
ggeffects.

## Example with Pacific cod presence

To start, we will use the Pacific cod example data. We will fit a model of fish presence/absence with covariates of depth and a fixed effect of year using a Tweedie distribution.

```
pcod$fyear <- as.factor(pcod$year)
mesh <- make_mesh(pcod, c("X", "Y"), cutoff = 20)
#> as(<dgCMatrix>, "dgTMatrix") is deprecated since Matrix 1.5-0; do as(., "TsparseMatrix") instead
fit <- sdmTMB(present ~ poly(depth, 2) + fyear,
data = pcod,
mesh = mesh,
spatial = "on",
family = binomial()
)
#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
#> TMB was built with Matrix version 1.5.3
#> Current Matrix version is 1.5.1
#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
```

We can then use `ggeffects::ggeffect()`

to see the effect
of depth on the probability of Pacific cod being present. We can control
what range and interval of depths are predicted within the function
(e.g. `[0:500 by=1]`

).

We can also plot the effects of each year.

We can add in data points

`plot(g, add.data = TRUE)`

We can also use `ggeffect`

to plot multiple variables by
listing them in `terms = c()`

, with the first term listed
indicating the variable to be plotted on the x-axis, and the remaining
listed terms (up to four total) indicating the groups. Adding
`facet = TRUE`

will show each year as a separate plot,
instead of overlain on one plot.

Adding `facet = TRUE`

will show each year as a separate
plot, instead of overlain on one plot.

`plot(dat, facet = TRUE)`

We can also use make our own ggplot plot by calling the ggeffects
object `dat`

as the data frame.

Plotting using with a continuous response (here `density`

)
rather than presence-only is similar. For instance:

We can fit a model with an interaction of two continuous variables:

For plotting two continuous variables, `ggeffect()`

will
make the non-target (2nd) variable discrete by selecting different
levels.

`plot(g5, facet = TRUE)`

To specify the levels rather than letting `ggeffect()`

choose them, use brackets with the selected values within the term list,
for instance