1. Stepwise selection might be inadequate for many problems.
2. You get a warning if an estimation step for one possible model does
not work.
3. In your data, many variables do have variance 0 (identical values)
within several classes. You might want to choose another kind of model,
perhaps by dichotomizing at least some variables or so and applying a
tree based method ...
Best wishes,
Uwe Ligges
Silvia Lomascolo wrote:> I use Windows, R version 2.5.1
>
> When I try to run stepclass (klaR) I get an error message/warning saying:
>
> 1: error(s) in modeling/prediction step in: cv.rate(vars = c(model,
tryvar),
> data = data, grouping = grouping, ...
>
> Actually, I look 16 warnings of this type. Can anyone tell me what this
> means?
> Also, it returns only 2 out of the 79 variables as important, however these
> variables don't make any biological sense... Might this be a problem of
my
> coding?
>
> Here's some code and sample matrix:
>
>> library(klaR)
>> var <- read.table("C:\\Documents and Settings\\My
> Documents\\silvia\\data\\variables.txt", header=T) ## matrix of 79
variables
>> disp<- read.table("C:\\Documents and Settings\\My
> Documents\\silvia\\data\\disperser.txt", header=T) ## vector defining
my
> groups
>> disp<- as.factor(disp$disperser)
>> data.step <- stepclass(var, disp, "lda", improvement =
0.05)
>
> Sample matrix:
>
> var:
> P5.38 P6.45 P6.55 P6.63 P6.78 P6.87 P7.12 P7.42 P8.10 P8.88 P9.09 P9.30
> P9.49 P9.55
> 0.00 11.08 3.16 0.76 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.64 0.00
> 0.00 0.00 0.00 0.00 0.00 1.63 0.00 6.89 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 4.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 131.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 5.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.00 9.59
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.88 0.48 0.89 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.00 0.21 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.41 20.62 8.13 8.87 8.27 12.51 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 133.24 0.00 0.73 0.00 0.00 0.00 0.00 1.34 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.32
> 0.00 1.81 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 7.26 8.16 1.50 0.00 0.00 0.00 1.97 1.28 0.00 0.00 0.00 1.16 0.00 0.00
> 0.00 1.48 0.22 0.00 0.00 0.00 0.00 0.00 1.80 0.66 0.47 0.47 0.75 0.00
> 0.00 1.34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.14 0.00
> 0.00 72.65 103.26 1.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.82 0.00 0.00 0.00 0.00 0.00 4.79 0.00 0.00 0.00 0.00 11.44 2.33 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.13 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.83 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 23.14 0.00 0.00 0.00 1.19 4.81 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 7.92 0.00 14.29 36.64 0.00 82.87 0.00 0.00 0.00 0.00 0.00 0.00
> 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
>
> disp:
> disperser
> 1
> 1
> 1
> 1
> 1
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 2
> 3
> 3
> 4
> 4
> 4
> 4
> 4
> 4
> 4
> 4
> 4
> 4
>
>