Geographic Information Systems Asked by RandomForester on November 9, 2021
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.
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.
Answered by Spacedman on November 9, 2021
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