Hi
I cannot help you with kernlab
> > pred = predict(mod, df, type = "probabilities")
> > acc = table(pred, df$cons)
> Error in table(pred, df$cons) : all arguments must have the same length
> which again is weird since mod, df and df$cons are made from the same
> dataframe.
Why not check length of those objects?
length(pred)
length(df$cons)
> > plot(mod, data = df)
> > kernlab::plot(mod, data = df)
> but I get this error:
>
> Error in .local(x, ...) :
> Only plots of classification ksvm objects supported
>
seems to me selfexplanatory. What did maintainer said about it?
Cheers
Petr
> -----Original Message-----
> From: R-help <r-help-boun...@r-project.org> On Behalf Of Luigi Marongiu
> Sent: Monday, January 7, 2019 1:26 PM
> To: r-help <r-help@r-project.org>
> Subject: [R] error in plotting model from kernlab
>
> Dear all,
> I have a set of data in this form:
> > str <data>
> 'data.frame': 1574 obs. of 14 variables:
> $ serial: int 12751 14157 7226 15663 11088 10464 1003 10427 11934 3999
> ...
> $ plate : int 43 46 22 50 38 37 3 37 41 11 ...
> $ well : int 79 333 314 303 336 96 235 59 30 159 ...
> $ sample: int 266 295 151 327 231 218 21 218 249 84 ...
> $ target: chr "HEV 2-AI5IQWR" "Dientamoeba fragilis-AIHSPMK" "Astro
> 2 Liu-AI20UKB" "C difficile GDH-AIS086J" ...
> $ ori.ct: num 0 33.5 0 0 0 ...
> $ ct.out: int 0 1 0 0 0 0 0 1 0 0 ...
> $ mr : num -0.002 0.109 0.002 0 0.001 0.006 0.015 0.119 0.003 0.004 ...
> $ fcn : num 44.54 36.74 6.78 43.09 44.87 ...
> $ mr.out: int 0 1 0 0 0 0 0 1 0 0 ...
> $ oper.a: int 0 1 0 0 0 0 0 1 0 0 ...
> $ oper.b: int 0 1 0 0 0 0 0 1 0 0 ...
> $ oper.c: int 0 1 0 0 0 0 0 1 0 0 ...
> $ cons : int 0 1 0 0 0 0 0 1 0 0 ...
> from which I have selected two numerical variables correspondig to x
> and y in a Cartesian plane and one outcome variable (z):
> > df = subset(t.data, select = c(mr, fcn, cons))
> > df$cons = factor(c("negative", "positive"))
> > head(df)
> mr fcn cons
> 1 -0.002 44.54 negative
> 2 0.109 36.74 positive
> 3 0.002 6.78 negative
> 4 0.000 43.09 positive
> 5 0.001 44.87 negative
> 6 0.006 2.82 positive
>
> I created an SVM the method with the KERNLAB package with:
> > mod = ksvm(cons ~ mr+fcn, # i prefer it to the more canonical "." but the
> outcome is the same
> data = df,
> type = "C-bsvc",
> kernel = "rbfdot",
> kpar = "automatic",
> C = 10,
> prob.model = TRUE)
>
> > mod
> Support Vector Machine object of class "ksvm"
>
> SV type: C-bsvc (classification)
> parameter : cost C = 10
>
> Gaussian Radial Basis kernel function.
> Hyperparameter : sigma = 42.0923201429106
>
> Number of Support Vectors : 1439
>
> Objective Function Value : -12873.45
> Training error : 0.39263
> Probability model included.
>
> First of all, I am not sure if the model worked because 1439 support
> vectors out of 1574 data points means that over 90% of the data is
> required to fix the hyperplane. this does not look like a model but a
> patch. Secondly, the prediction is rubbish -- but this is another
> story -- and when I try to create a confusion table of the processed
> data I get:
> > pred = predict(mod, df, type = "probabilities")
> > acc = table(pred, df$cons)
> Error in table(pred, df$cons) : all arguments must have the same length
> which again is weird since mod, df and df$cons are made from the same
> dataframe.
>
> Coming to the actual error, I tried to plot the model with:
> > plot(mod, data = df)
> > kernlab::plot(mod, data = df)
> but I get this error:
>
> Error in .local(x, ...) :
> Only plots of classification ksvm objects supported
>
> Would you know what I am missing?
> Thank you
> --
> Best regards,
> Luigi
>
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