Re: [R] kernlab ksvm rbfdot kernel - prediction returning fewer rows than provided for input

forgot to mention, the training and testing dataframes are composed of 4 IVs (one double numeric IV and three factor IVs) and one DV (dichotomous factor, i.e. true or false). The training dataframe consists of 48819 rows and test dataframe consists of 24408 rows. Thanks again.

[R] kernlab ksvm rbfdot kernel - prediction returning fewer rows than provided for input

Hi everyone. I'm using the kernlab ksvm function with the rbfdot kernel for a binary classification problem and getting a strange result back. The predictions seem to be very accurate judging by the training results provided by the algorithm, but I'm unable to generate a confusion

Re: [R] r kernlab find best cost parameter automatically

That giving the best trade between sensitivity and specificity. On Sat, May 25, 2019 at 12:47 AM Abby Spurdle wrote: > > > Would be possible to automate the selection of the best value? > > Can you define "best", precisely? > > -- Best regards, Luigi

Re: [R] r kernlab find best cost parameter automatically

> Would be possible to automate the selection of the best value? Can you define "best", precisely? [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see

[R] r kernlab find best cost parameter automatically

Dear all, I am using kernlab to implement an SVM analysis. The model I am building has the syntax: `ksvm( ~ , data = , type = "C-svc", kernel = "rbfdot", kpar = "automatic", C = , prob.model = TRUE)` Here, I can use different values of `k` to give different costs to the model. Each time I give a

[R] Kernlab Error in .local(x, ...) when plot model

Dear all, I have generated a model with KERNLAB using the following steps: the data is a dataframe df of two numerical variables x and y, and each point is assigned a z value that is a factor of two levels: positive and negative. The data has the strucutre: > str(df) 'data.frame': 1574 obs. of 3

[R] KERNLAB

I'm using "Kernlab" to apply the "Weighted Nadaraya Watson" by Kato (2012) and Hall, Wolff, and Yao (1999). I need to find this Gaussian Kernel in weights'calculation , where u= (x-x0): Kh(u) = h^(−1)*K(u/h). I used: rbf1 <- rbfdot(sigma = NULL) but I have to find out "sigma" as the inverse

[R] kernlab multicore usage

I am hoping there are other users of the kernlab package in R who will be able to solve a puzzle. In the past, I've used the relevance vector machine engine (rvm) in kernlab and was pleased to see it use all four cores on my PC (running Windows 8). But now it only runs on one core and I can't

Re: [R] kernlab::kkmeans initial centers

I am not asking about k-means. I am asking about passing initial assignments to the kernel k means algorithm. In kernel k-means, centroids are not defined explicitly. I tried passing initial centroids in the original feature space though. But, it did not work. The provided example just sets the

Re: [R] kernlab::kkmeans initial centers

...@gmail.com À : r-help@r-project.org Cc : Pascal Oettli kri...@ymail.com Envoyé le : Mercredi 3 avril 2013 22h53 Objet : Re: [R] kernlab::kkmeans initial centers I am not asking about k-means. I am asking about passing initial assignments to the kernel k means algorithm. In kernel k-means

Re: [R] kernlab::kkmeans initial centers

by a 3x4 matrix. HTH, Pascal -- *De :* Ahmed Elgohary aagoh...@gmail.com *À :* r-help@r-project.org *Cc :* Pascal Oettli kri...@ymail.com *Envoyé le :* Mercredi 3 avril 2013 22h53 *Objet :* Re: [R] kernlab::kkmeans initial centers I am not asking about k-means. I

[R] kernlab::kkmeans initial centers

Hi, I am trying to pass initial cluster assignments to the kkmeans methodhttp://rss.acs.unt.edu/Rdoc/library/kernlab/html/kkmeans.htmlof kernlab. It is not clear to me how I can set the parameter *centers* with initial cluster centers as stated in the documentation? thanks, --ahmed

Re: [R] kernlab::kkmeans initial centers

Hi, I would say that if you know what k-means algorithm is, you know the meaning of initial cluster centers. You can also check the output of the provided example. Regards, Pascal On 04/03/2013 09:27 AM, Ahmed Elgohary wrote: Hi, I am trying to pass initial cluster assignments to the

Re: [R] kernlab | ksvm error

On 26.08.2012 15:33, Reza Salimi-Khorshidi wrote: Thanks Uwe, Am I right that in ksvm's internal cross-validation, there is no guarantee for having *at least one* of each classes in each subset? That is my guess, but I haven't read the code. Please read it yourself in case you want more

[R] kernlab`s custom kernel of ksvm freeze

Hello, together I'm trying to use user defined kernel. I know that kernlab offer user defined kernel(custom kernel functions) in R. I used data spam including package kernlab. (number of variables=58 number of examples =4061) i'm user defined kernel's form, kp=function(d,e){ as=v*d bs=v*e

Re: [R] kernlab | ksvm error

On 25.08.2012 02:12, Reza Salimi-Khorshidi wrote: Dear Uwe, I appreciate that if you let me know why, when using the attached file, the following script (two lines) doesn't work once in 10s of times. Best, Reza svm.pol4- ksvm(class.labs~ ., data= train.data, prob.model= T, scale= T, kernel=

Re: [R] kernlab | ksvm error

Thanks Uwe, Am I right that in ksvm's internal cross-validation, there is no guarantee for having *at least one* of each classes in each subset? Some randomness is involved, and when you get an unfortunate subsample (e.g. if in the internal cross-validation one class is not selected at all) it

Re: [R] kernlab | ksvm error

Dear Uwe, I appreciate that if you let me know why, when using the attached file, the following script (two lines) doesn't work once in 10s of times. Best, Reza svm.pol4 - ksvm(class.labs ~ ., data = train.data, prob.model = T, scale = T, kernel = polydot) svm.pol.prd4 - predict(svm.pol4,

[R] kernlab | ksvm error

Dear list, I am using the ksvm function from kernlab as follows: (1) learning svm.pol4 - ksvm(class.labs ~ ., data = train.data, prob.model = T, scale = T, kernel = polydot) (2) prediction svm.pol.prd4 - predict(svm.pol4, train.data, type = probabilities)[,2] But unfortunately, when calling

Re: [R] kernlab | ksvm error

On 19.08.2012 11:06, Reza Salimi-Khorshidi wrote: Dear list, I am using the ksvm function from kernlab as follows: (1) learning svm.pol4 - ksvm(class.labs ~ ., data = train.data, prob.model = T, scale = T, kernel = polydot) (2) prediction svm.pol.prd4 - predict(svm.pol4, train.data, type

Re: [R] kernlab | ksvm error

Hi Uwe, I can attach the data file to an email or send you a link so you can download it. Which one do you prefer? Thanks for your help ... Best, Reza On Sun, Aug 19, 2012 at 4:10 PM, Uwe Ligges lig...@statistik.tu-dortmund.de wrote: On 19.08.2012 11:06, Reza Salimi-Khorshidi wrote: Dear

[R] kernlab kpca predict

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions

Re: [R] kernlab kpca predict

Hm.. seems like its a problem with loading it in the profile.. If i load it again in the console it works fine. Must have something to do with the masking. -- Changed load-order with the package which required stats that did the masking, and it works now, so, nevermind. On

[R] kernlab kpca code

Hi! how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ? (And if anyone has too much time: Now if i got that right, the @pcv attribute consists of the principal components, and for kpca, these are defined as projections of some

Re: [R] kernlab kpca code

Hi Jessica, On Thu, Apr 26, 2012 at 11:59 AM, Jessica Streicher j.streic...@micromata.de wrote: Hi! how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ? Probably you have to do kernlab:::predict.kpca from your R workspace, but why

Re: [R] kernlab kpca code

Thanks a lot, totally forgot cran there. Hm.. so they're multiplying some specifically computed Kernelmatrix with the pcv's.. interesting.. too tired to check the math there, guess i'll just accept its possible and go to sleep. Am 26.04.2012 um 18:10 schrieb Steve Lianoglou: Hi Jessica,

[R] kernlab - rvm error message: Error in if (length(data) != vl)

Hi, I am trying to perform relevance vector machines with the rvm-function from kernlab. On one dataset I get this message: Setting default kernel parameters Error in if (length(data) != vl) { : RMate stopped at line 0 of selection missing value where TRUE/FALSE needed Calls: rvm ...

[R] kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

Hi, For another trainingset I get this error message, which again is rather cryptic to me: Setting default kernel parameters Error in array(0, c(n, p)) : 'dim' specifies too large an array RMate stopped at line 0 of selection Calls: rvm ... .local - backsolve - as.matrix - chol - diag -

Re: [R] kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

I am using a linear kernel (vanilladot). By switching the kernel, I actually get rid of the error message, but I would like to stick to the linear one ... On 13.02.2012, at 16:23, Martin Batholdy wrote: Hi, For another trainingset I get this error message, which again is rather cryptic

Re: [R] kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

On Feb 13, 2012, at 10:23 AM, Martin Batholdy wrote: Hi, For another trainingset I get this error message, which again is rather cryptic to me: Just imagine how it seems to us! Setting default kernel parameters Error in array(0, c(n, p)) : 'dim' specifies too large an array RMate

Re: [R] kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

Ok, I am sorry, My trainingset consists of a 60 x 204 matrix (independent_training – 204 features). I have 60 continuous labels (dependent_training, ranging from 2.25 to 135). this is all the code I use: library(kernlab) rvm(as.matrix(independent_training), dependent_training, type=regression,

Re: [R] kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

Hi, On Mon, Feb 13, 2012 at 10:53 AM, Martin Batholdy batho...@googlemail.com wrote: Ok, I am sorry, My trainingset consists of a 60 x 204 matrix (independent_training – 204 features). I have 60 continuous labels (dependent_training, ranging from 2.25 to 135). this is all the code I use:

Re: [R] kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array

Sorry, this: options(error=utils:::dum.frames) Should be: options(error=utils:::dump.frames) -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology  | Memorial Sloan-Kettering Cancer Center  | Weill Medical College of Cornell University Contact Info:

[R] kernlab: ksvm() bug?

Hello all, I'm trying to run a gird parameter search for a svm. Therefore I'M using the ksvm function from the kernlab package. svp - ksvm(Ktrain,ytrain,type=nu-svc,nu=C) The problem is that the optimization algorithm does not return for certain parameters. I tried to use

[R] kernlab ipop solver value dual

Hi all, Can anyone tell me for what the kernlab ipop return value dual is? How does it relate to the solution for a Support Vector Machine solution? I am trying to use the ipop solver in my (toy) example of a Support Vector Machine and I am noting that the bias (essentially the offset for

[R] Kernlab: multidimensional targets in rvm(), ksvm(), gausspr()

Hi there, I'm trying to do a regression experiment on a multidimensional dataset where both x and y in the model are multidimensional vectors. I'm using R version 2.9.2, updated packages, on a Linux box. I've tried gausspr(), ksvm() and rvm(), and the models are computed fine, but I'm always

[R] Kernlab - Splinedot Kernel

Hi, I am trying to use the splinedot kernel as part of the kernlab package, but I get the following error: Error in votematrix[i, ret 0] - votematrix[i, ret 0] + 1 : NAs are not allowed in subscripted assignments The parameters that I have used to build the model are:

[R] kernlab - custom kernel

hi, I am using R's kernlab package, exactly i am doing classification using ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some error. # Following R code works (with promotergene dataset): library(kernlab) s - function(x, y) { sum((x*y)^1.25) } class(s) - kernel

[R] kernlab - custom kernel

hi, I am using R's kernlab package, exactly i am doing classification using ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some error. # Following R code works (with promotergene dataset): library(kernlab) s - function(x, y) { sum((x*y)^1.25) } class(s) - kernel

[R] kernlab and gram matrix

Hi, this is a question about the R package kernlab. I use kernlab as a library in a C++ program. The host application defines a graph kernel (defined by me), generates a gram matrix and trains kernlab directly on this gram matrix, like this: regm-ksvm(K,y,kernel=matrix), where K is the n x n

[R] kernlab/ ksvm: class.weights prob.model in binary classification

Hello list, I am faced with a two-class classification problem with highly asymetric class sizes (class one: 99%, class two: 1%). I'd like to obtain a class probability model, also introducing available information on the class prior. Calling kernlab/ksvm with the line