# predict.slrm

##### Predicted or Fitted Values from Spatial Logistic Regression

Given a fitted Spatial Logistic Regression model, this function computes the fitted probabilities for each pixel, or the fitted point process intensity, or the values of the linear predictor in each pixel.

##### Usage

```
## S3 method for class 'slrm':
predict(object, ..., type = "intensity", newdata=NULL)
```

##### Arguments

- object
- a fitted spatial logistic regression model.
An object of class
`"slrm"`

. - ...
- Optional arguments passed to
`pixellate`

determining the pixel resolution for the discretisation of the point pattern. - type
- Character string (partially) matching one of
`"probabilities"`

,`"intensity"`

or`"link"`

. - newdata
- Optional. List containing new covariate values for the prediction. See Details.

##### Details

This is a method for `predict`

for spatial logistic
regression models (objects of class `"slrm"`

, usually obtained
from the function `slrm`

).

The argument `type`

determines which quantity is computed.
If `type="intensity"`

), the value of the point process intensity
is computed at each pixel. If `type="probabilities"`

) the
probability of the presence of a random point in each pixel is
computed. If `type="link"`

, the value of the linear predictor is
computed at each pixel.

If `newdata = NULL`

(the default), the algorithm computes
fitted values of the model (based on the data that was originally used
to fit the model `object`

).

If `newdata`

is given, the algorithm computes predicted values
of the model, using the new values of the covariates provided by
`newdata`

. The argument `newdata`

should be a list;
names of entries in the list should correspond
to variables appearing in the model formula of the `object`

.
Each list entry may be a pixel image or a single numeric
value.

##### Value

- A pixel image (object of class
`"im"`

) containing the predicted values for each pixel.

##### See Also

##### Examples

```
X <- rpoispp(42)
fit <- slrm(X ~ x+y)
plot(predict(fit))
data(copper)
X <- copper$SouthPoints
Y <- copper$SouthLines
Z <- distmap(Y)
fitc <- slrm(X ~ Z)
pc <- predict(fitc)
Znew <- distmap(copper$Lines)[copper$SouthWindow]
pcnew <- predict(fitc, newdata=list(Z=Znew))
```

*Documentation reproduced from package spatstat, version 1.23-2, License: GPL (>= 2)*