I'm having problems with predicting a raster using a linear model.

Firstly i create my model from the data found in my polygons.

 # create model poly <- st_read("polygon.shp") df <- na.omit(poly) df <- df[df$gdp > 0 & df$ntl2 > 0 & df$pop2 > 0,] x <- log(df$ntl2) y <- log(df$gdp*df$pop2) c <- df$iso d <- data.frame(x,y,c) m <- lm(y~x+c,data=d)

Then i want to use raster::predict to estimate an output raster

 # raster data iso <- raster("iso.tif") viirs <- raster("viirs.tif") x <- log(viirs) c <- iso ## predict with models s <- stack(x,c) predicted <- raster::predict(x,model=m)

however i get following response:

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : object is not a matrix

I don't know what the problem is and how to fix it. My current throughts are that its something to do with the factors/country codes:

My model includes country codes, as I would like to include some country fixed effects. Maybe there is a problems with including these. However even when excluding the country codes from the model and the entire dataframe, i still get the same error message.

Futhermore, my model is based on regional values from the whole world and the prediction datasets only include the extent of Turkey. Maybe this is the problem?

And here is the data:

4

1 Answer

Perhaps it works if you do like this:

iso <- raster("iso.tif")
viirs <- raster("viirs.tif")
s <- stack(log(viirs), iso)
names(s) <- c("x", "c")
predicted <- raster::predict(s, model=m) 

It won't work if the values in df$iso and iso.tif don't match (is one a factor, and the other numeric?).

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