Fixing tuning.r to work with single-raster models#1
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darcyj wants to merge 1 commit intocran:masterfrom
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Fixing tuning.r to work with single-raster models#1darcyj wants to merge 1 commit intocran:masterfrom
darcyj wants to merge 1 commit intocran:masterfrom
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Before this change, ENMevaluate does NOT work when forming a model using a single raster layer. NOTE: this is NOT the same issue that arises when a single raster layer is used instead of a raster stack with only one layer in it. That's a different problem. The bug I'm fixing here occurs when env (the input to tuning() ) is a raster stack containing one raster. Instead, there's some data.frame subsetting that happens within the tune() function that assumes a 2-dimensional data.frame. Usually that's fine, until you only have one raster layer and therefore the data.frame objects "train.val", "test.val" and "bg.val" each have a single column. When they are subset using [some_logical, ], R( in its infinite wisdom) returns a vector, NOT a data.frame. This is solved by adding "drop=FALSE", which I've done here.
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Before this change, ENMevaluate does NOT work when forming a model using a single raster layer.
NOTE: this is NOT the same issue that arises when a single raster layer is used instead of a raster stack with only one layer in it. That's a different problem. The bug I'm fixing here occurs when env (the input to tuning() ) is a raster stack containing one raster.
Instead, there's some data.frame subsetting that happens within the tune() function that assumes a 2-dimensional data.frame. Usually that's fine, until you only have one raster layer and therefore the data.frame objects "train.val", "test.val" and "bg.val" each have a single column. When they are subset using [some_logical, ], R( in its infinite wisdom) returns a vector, NOT a data.frame. This is solved by adding "drop=FALSE", which I've done here.