How to have a Raster output of a ppm model in spastat

Question

I am trying to compare the outputs of species distribution models. My current code uses the following function to calculate TSS scores.

TSS_Score<-function(TrueBinary, Raster) {

  Raster[]<-as.numeric(Raster[] > 0.5)

  ConfusionMatrix<-crosstab(TrueBinary,Raster)

  a<-ConfusionMatrix[2,2]

  b<-ConfusionMatrix[2,1]

  c<-ConfusionMatrix[1,2]

  d<-ConfusionMatrix[1,1]

  Sensitivity<-a/(a+c)

  Specificity<-d/(b+d)

  TSS<-Sensitivity+Specificity-1

  return(TSS)
}

This code works but I need a raster as the model output for it to work. I have run a ppm model from spatstat and then used the predict.ppm function. However the output of predict.ppm is an image class.

Is there anyway to get a Raster as a predict output?

Or to convert the image into a raster?

EDIT: Here is the code for my ppm model. The covariates are images files as the function doesn't seem to take rasters?

PPMmodel<-ppm(PointsPPP, ~slope, covariates = list(slope =

AMtemp,MDRtemp,Isotherm,AMprecip,SeasonPrecip, TreeCover50))

predictPPM<-predict(PPMmodel)

predictPPM

real-valued pixel image

128 x 128 pixel array (ny, nx)

enclosing rectangle: [92.192, 141.01] x [-10.999, 28.549] units

Answer 1

Here's something from example(predict.ppm) that looks like your object:

> trend
real-valued pixel image
32 x 32 pixel array (ny, nx)
enclosing rectangle: [0, 1] x [0, 1] units

and here is what happens when you call raster from the raster package on it:

> raster(trend)
class       : RasterLayer 
dimensions  : 32, 32, 1024  (nrow, ncol, ncell)
resolution  : 0.03125, 0.03125  (x, y)
extent      : 0, 1, 0, 1  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
data source : in memory
names       : layer 
values      : 61.62107, 107.6241  (min, max)

which was a very pleasant surprise.