Perhaps my response is not showing up on the r-help list due to the
large number of recipients generated by the reply-all option
triggering some sort of spam filter. So I am trimming them.
On Dec 26, 2009, at 3:53 AM, Vishal Thapar wrote:
Hi All,
Thank you for your replies so far. I was hoping I could get some
more input from you on this issue. It seems to me that I have hit a
dead end here and would really appreciate some feedback. I have
followed all the suggestions you have mentioned but they still this
is stuck. Earlier I thought that it was a "factor" issue but now
even that is not the error. Here is the script and the error. Thanks
for your help. I have attached the sample test file as well as the
training file in case you would like to run it locally.
---------------------------------
library(seqinr)
library("kernlab")
### Reading in the data
mars500_1_fasta = read.fasta("toClassify500_1.fasta")
mars500_1_seq = t(getSequence(mars500_1_fasta)) # get the sequences
from the fasta object
mars500_1_df = as.data.frame(mars500_1_seq,stringsAsFactors=FALSE) #
convert it to a Data Frame
class = append(rep("+",times=128),rep("-",times=128)) # add the
Class field to the data frame for classification
mars500_1_df = cbind(Class=class,mars500_1_df)
mars500_1_df = data.frame(lapply(mars500_1_df,factor)) #Finally
apply the factor() function
#####
##### Call the ksvm() function to create a model
mars500_1 <- ksvm(Class ~ ., data = mars500_1_df, kernel = "rbfdot",
kpar = "automatic", C = 60, cross = 3, prob.model = TRUE)
> str(mars500_1_df)
'data.frame': 256 obs. of 501 variables:
All of which are factors with 4 levels
testSeq_fa=read.fasta("temp1.fasta")
testSeq_seq=t(getSequence(testSeq_fa))
testSeq_df=as.data.frame(testSeq_seq,stringsAsFactors=FALSE)
testSeq_df = cbind(Class="-",testSeq_df)
testSeq_df = data.frame(lapply(testSeq_df,factor))
> str(testSeq_df)
'data.frame': 20 obs. of 501 variables:
$ V9 : Factor w/ 3 levels "a","c","t": 2 1 2 1 3 2 3 2 3 1 ...
$ V9 : Factor w/ 3 levels "a","c","t": 2 1 2 1 3 2 3 2 3 1 ...
$ V26 : Factor w/ 3 levels "a","g","t": 2 1 1 1 1 3 1 3 1 3 ...
...and about 10 more...
So I think you were closer but not quite there yet.
> for(i in 11:501){if (length(levels(testSeq_df[,i])) == 3)
levels(testSeq_df[,i])<- c(a="a",g="g",c="c",c="t")}
> predict(mars500_1,testSeq_df)
[1] - - - - + - + - - - - + + + - - - - - +
Levels: - +
YES, it WAS (and still is) a "factor issue".
testSeq_fa=read.fasta("temp1.fasta")
testSeq_seq=t(getSequence(testSeq_fa))
testSeq_df=as.data.frame(testSeq_seq,stringsAsFactors=FALSE)
testSeq_df = cbind(Class="-",testSeq_df)
testSeq_df = data.frame(lapply(testSeq_df,factor))
predict(mars500_1,testSeq_df)
Error in .local(object, ...) : test vector does not match model !
Thanks in advance.
Sincerely,
Vishal
On Fri, Dec 25, 2009 at 8:10 AM, Vishal Thapar
<vishaltha...@gmail.com> wrote:
Hi,
I seem to have made some headway on this problem but its still not
solved. It seems like this is a "factor" issue. When I read my
training set, I read it with read.csv() which converts each of the
columns as "factors". From this if I take a single row as my
<snipped>
Williams Bldg
1 Bungtown Road
Cold Spring Harbor, NY - 11724
<toClassify500_1.fasta><temp1.fasta>
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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