Hi All,
I fitted several classifiers in a two class problem. I then used the package
'yaImpute' - to apply my predictive models to asciigrids and thereby generate a
probability maps. So far I successfully used yaImpute to generate maps for
Random Forests, Classification trees, Generalized Linear Models (GLMs) and
Generalized Additive Models (GAMs). But when I try to use it to two other
classifiers
- support vector machine (SVM) and linear discriminant analysis (LDA) - I am
getting an error message which I am not really sure what it is trying to tell
me. Can you please take few minutes of your time to help me understand what
these error messages are?
I used the following piece of code to apply my models to grids once I fit the
model for both LDA and SVM:
AsciiGridPredict(lda.fit,xfiles=namelist,outfiles = as.character(outfile))
AsciiGridPredict(svm.fit,xfiles=namelist,outfiles = as.character(outfile))
I am getting the following error message for LDA:
Rows per dot: 1 Rows to do: 163
ToDo:
...................................................................................................................................................................
Done: .
First six lines of predicted data for map row: 2
predict.class predict.posterior.0 predict.posterior.1 predict.LD1
1
<NA>
-9999
-9999 -9999
2
<NA>
-9999
-9999 -9999
3
<NA>
-9999
-9999 -9999
4
<NA>
-9999
-9999 -9999
5
<NA>
-9999
-9999 -9999
6
<NA>
-9999
-9999 -9999
Error in AsciiGridImpute(object, xfiles, outfiles, xtypes = xtypes, lon =
lon, :
predict is not present in the predicted data
In addition: Warning message:
In `[<-.factor`(`*tmp*`, ri, value = c(-9999, -9999, -9999, -9999, :
invalid factor level, NAs generated
I am getting the following error message for SVM:
Rows per dot: 1 Rows to do: 163
ToDo:
...................................................................................................................................................................
Done:
...................................................................................................................................................................
Legend of levels in output grids:
predict
1 0
2 1
There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In `[<-.factor`(`*tmp*`, ri, value = c(-9999, -9999, -9999, ... :
invalid factor level, NAs generated
2: In `[<-.factor`(`*tmp*`, ri, value = c(-9999, -9999, -9999, ... :
invalid factor level, NAs generated
3: In `[<-.factor`(`*tmp*`, ri, value = c(-9999, -9999, -9999, ... :
invalid factor level, NAs generated
4: In `[<-.factor`(`*tmp*`, ri, value = c(-9999, -9999, -9999, ... :
invalid factor level, NAs generated
5: In `[<-.factor`(`*tmp*`, ri, value = c(-9999, -9999, -9999, ... :
invalid factor level, NAs generated
I hope my writing is clear and my questions make sense.
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