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I have two lists of raster objects. One contains validation plots and the other contains results from a random forest binary classification of those plots. I would like to loop through both lists with caret::confusionMatrix to assess accuracy of the classifier. However, some rasters only have one class present, and caret::confusionMatrix returns the

Error: there must be at least two levels in the data.

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  • How are you casting the rasters to factors for confusionMatrix? Supply a levels argument to factor giving all the possible levels.
    – Spacedman
    Jun 2 '20 at 18:28
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Sample data - you should make something like this in your question to illustrate:

> r1 = raster(matrix(sample(1:3,25,TRUE),5,5))
> r2 = raster(matrix(sample(1:3,25,TRUE),5,5))

Confusion matrix of a raster is an error:

> confusionMatrix(r1,r2)
Error: `data` and `reference` should be factors with the same levels.

So convert to a factor with the same levels:

> confusionMatrix(factor(r1[],levels=1:3),factor(r2[],levels=1:3))
Confusion Matrix and Statistics

          Reference
Prediction 1 2 3
         1 5 1 2
         2 3 5 5
         3 3 0 1

Then if one of your rasters is lacking levels, then it still works:

> r3 = raster(matrix(1,5,5))
> confusionMatrix(factor(r1[],levels=1:3),factor(r3[],levels=1:3))
Confusion Matrix and Statistics

          Reference
Prediction  1  2  3
         1  8  0  0
         2 13  0  0
         3  4  0  0

The only condition here is that you have to know all the possible values in advance. If a sudden "42" sneaks in somewhere then it will break. Get all unique values in your rasters beforehand and then create factors with that.

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