Real datasets are often fairly complex, and information on the
species other than their location data may also be collected. Additional
information could be the length of a specific plant, or the weight of a
group of mammals, something which describes the underlying
characteristics of the studied species’. Using such information results
in a *marked point process (*see Illian,
Sørbye, and Rue (2012) for an overview); and this framework may
be incorporated in the *PointedSDMs* R package*.*

This vignette gives an illustration on how include marks in the model
framework, by using data on Eucalyptus globulus (common name: blue gum)
on the *Koala Conservation Center on Philip Island* (Australia),
collected by a community group between 1993 and 2004 (Moore et al. 2010). The dataset contains a
multitude of different marks, however for this study we will be focusing
on two: *food*, which is some index of the food value of the tree
(calculated as dry matter intake multiplied by foliage palatability),
and *koala*, describing the number of koala visits to each tree.
No inference of the model is completed in this vignette due to
computational intensity of the model. However the *R* script and
data are provided below so that the user may carry out inference.

```
library(spatstat)
library(PointedSDMs)
library(sp)
library(inlabru)
library(INLA)
```

```
data(Koala)
<- Koala$eucTrees
eucTrees <- Koala$boundary boundary
```

```
data(euc) ##will add this in the future when data is on archive
```

Lets first create a model for the tree locations only: in this
example, we will assume that these locations are treated as
*present-only* data.

```
<- intModel(euc, Coordinates = c('x', 'y'),
points Projection = proj, Mesh = mesh)
<- fitISDM(points, options = list(control.inla = list(int.strategy = 'eb')))
pointsModel
<- predict(pointsModel, mask = boundary,
pointsPredictions mesh = mesh, predictor = TRUE)
plot(pointsPredictions)
```

Now lets add the marks to this framework by specifying the name of
the response variable of the marks with the argument
`markNames`

, and the family of the marks with the argument
`markFamily`

. Doing so will model each marks as a separate
observation process, based on the family specified.

```
<- intModel(euc, Coordinates = c('x', 'y'), Projection = proj,
marks markNames = c('food', 'koala'), markFamily = c('gaussian', 'poisson'),
Mesh = mesh)
<- fitISDM(marks, options = list(control.inla = list(int.strategy = 'eb')))
marksModel
<- predict(marksModel, mask = boundary,
foodPredictions mesh = mesh, marks = 'food', spatial = TRUE)
<- predict(marksModel, mask = boundary,
koalaPredictions mesh = mesh, marks = 'koala', spatial = TRUE)
plot(foodPredictions)
plot(koalaPredictions)
```

For the second mark model we only include the *food* mark, but
this time we use a *log-linear* model with additive Gaussian
noise. This is specified using the `.$updateFormula`

slot
function, and then by adding the scaling component to the
*inlabru* components with the `.$addComponents`

slot
function to ensure that it is actually estimated. Moreover we assume
*penalizing complexity* priors for the two spatial effects, as
well as specify *bru_max_iter* in the *options* argument
to keep the time to estimate down.

```
<- intModel(euc, Coordinates = c('x', 'y'), Projection = proj,
marks2 markNames = 'food', markFamily = 'gaussian',
Mesh = mesh, pointsSpatial = 'individual')
$updateFormula(markName = 'food',
marks2newFormula = ~ exp(food_intercept + (euc_spatial + 1e-6)*scaling + food_spatial))
$changeComponents(addComponent = 'scaling')
marks2
$specifySpatial(datasetName = 'euc',
marks2prior.sigma = c(0.1, 0.01),
prior.range = c(10, 0.01))
$specifySpatial(Mark = 'food',
marks2prior.sigma = c(0.1, 0.01),
prior.range = c(10, 0.01))
<- fitISDM(marks2, options = list(control.inla = list(int.strategy = 'eb'),
marksModel2 bru_max_iter = 2))
<- predict(marksModel2, mask = boundary, mesh = mesh,
predsMarks2 formula = ~ (food_intercept + (euc_spatial + 1e-6)*scaling + food_spatial))
plot(predsMarks2)
```

Illian, Janine B, Sigrunn H Sørbye, and Håvard Rue. 2012. “A
Toolbox for Fitting Complex Spatial Point Process Models Using
Integrated Nested Laplace Approximation (INLA).” *The Annals
of Applied Statistics* 6 (4): 1499–1530. https://doi.org/10.1214/11-AOAS530.

Moore, Ben D, Ivan R Lawler, Ian R Wallis, Colin M Beale, and William J
Foley. 2010. “Palatability Mapping: A Koala’s Eye View of Spatial
Variation in Habitat Quality.” *Ecology* 91 (11): 3165–76.
https://doi.org/10.1890/09-1714.1.