Displaying 5 results from an estimated 5 matches for "ymatrix".
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2007 Aug 14
0
kernlab ksvm() cross-validation prediction response vector
Hello,
I would like to know, whether for the support vector classification function ksvm()
the response values stored in object at ymatrix are cross validated outputs/predictions:
Example code from package kernlab, function ksvm:
library(kernlab)
## train a support vector machine
filter <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=5,cross=3)
filter
filter at ymatrix
if not:
what is the easiest way to...
2006 Oct 12
3
Cross two dataframe
Dear r-users!
I would like to cross two data frame which have the same row number but
different in the number of column. Can anybody help me for this case ?
Thanks a lot in advance
--------------------------------------------------------------------------------
Majid Iravani
PhD Student
Swiss Federal Research Institute WSL
Research Group of Vegetation Ecology
Z?rcherstrasse 111
2012 May 15
1
Regression Analysis or Anova?
...ough I do not use Y as composed by 4 variables and
X by 6, and I do not consider the negative values
too, I get a very low score as my R^2.
If I act with anova instead I have this problem:
________________________________________________________________________________________________________
> Ymatrix<- as.matrix(Y)
> Xmatrix<- as.matrix(X) #where both this Y and X are in their first form,
thus composed by more variables (4 and 6) and with
#negative values as well.
> Errore in UseMethod("anova") :
no applicable method for 'anova' applied to an object of class
&qu...
2008 Sep 14
0
ksvm accessing the slots of S4 object
...ef" "alphaindex" "b"
[6] "obj" "SVindex" "nSV" "prior" "prob.model"
[11] "alpha" "type" "kernelf" "kpar" "xmatrix"
[16] "ymatrix" "fitted" "lev" "nclass" "error"
[21] "cross" "n.action" "terms" "kcall" "class"
>ksvm.model
Support Vector Machine object of class "ksvm"
SV type: C-svc...
2010 Sep 24
0
kernlab:ksvm:eps-svr: bug?
...[svindex,,drop=FALSE]
812 b(ret) <- -sum(alpha(ret))
813 obj(ret) <- resv[(m + 1)]
814 param(ret)$epsilon <- epsilon
815 param(ret)$C <- C
816 }
817
818
819 kcall(ret) <- match.call()
820 kernelf(ret) <- kernel
821 ymatrix(ret) <- y
822 SVindex(ret) <- sort(unique(svindex),method="quick")
823 nSV(ret) <- length(unique(svindex))
824 if(nSV(ret)==0)
825 stop("No Support Vectors found. You may want to change your parameters")
826 fitted(ret) <- if (fit)
827...