search for: crossvalid

Dear R-experts, Here below my R code with an error message. Can somebody help me to fix this error?? Really appreciate your help. Best, ############################################################ #?MSE CROSSVALIDATION Lasso regression? library(glmnet) ? x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,202,231,154,21,34,26,56,78,99,83,46,58,91) x2=c(1,3,2,4,5,6,7,3,8,9,10,11,12,1,3,4,2,3,4,5,4,6,8,7,9,4,3,6,7,9,8,4,7,6,1,3,2,5,6,8,7,1,1,2,9) y=c(2,6,5,4,...

...a R-help <r-help at r-project.org> wrote: > > Dear R-experts, > > Here below my R code with an error message. Can somebody help me to fix this error? > Really appreciate your help. > > Best, > > ############################################################ > # MSE CROSSVALIDATION Lasso regression > > library(glmnet) > > > x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,202,231,154,21,34,26,56,78,99,83,46,58,91) > x2=c(1,3,2,4,5,6,7,3,8,9,10,11,12,1,3,4,2,3,4,5,4,6,8,7,9,4,3,6,7,9,8,4,7,6,1,3,2,5,6,...

Hi all, I'm relatively new to using R, and have been trying to fit an L1 regularization path using coxpath from the glmpath library. I'm interested in using a cross validation framework, where I crossvalidate on a training set to select the lambda that achieves the lowest error, then use that value of lambda on the entire training set, before applying to a test set. This seems to entail somehow using cv.coxpath , inspecting the cv.error attribute, then using the corresponding lambda in coxpath. H...

...new to R. Does any of packages have something like glm+regularization? So far i see probably something close to that as a ridge regression in MASS but I think i need something like GLM, in particular binomial regularized versions of polynomial regression. Also I am not sure how some of the K-fold crossvalidation helpers out there (cv.glm) could be used to adjust reg rate as there seems to be no way to apply them over data not used for training (or i am not seeing a solution here as training is completely separated from crossvalidation error computation here) . The example here in cv.glm doesn't...

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata)...

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

...y predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE 1) One of these models is the best, according to its crossvalidation (PICTURE 2), the red line corresponds to the best crossvalidation. Its produced by cv <- cv.glmpath(x=as.matrix(X), y=unclass(T)-1, family="binomial", type ="response", plot.it=TRUE, se=TRUE) abline(v= cv$fraction[max(which(cv$cv.error==min(cv$cv.error)))], col=&quo...

...gt;? ? ? >> this error? >? ? ? >> >> Really appreciate your help. >? ? ? >> >> >? ? ? >> >> Best, >? ? ? >> >> >? ? ? >> >> ############################################################ >? ? ? >> >> # MSE CROSSVALIDATION Lasso regression >? ? ? >> >> >? ? ? >> >> library(glmnet) >? ? ? >> >> >? ? ? >> >> >? ? ? >> >> >? ? ? >> x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,...

...gt; >> this error? > >> >> Really appreciate your help. > >> >> > >> >> Best, > >> >> > >> >> ############################################################ > >> >> # MSE CROSSVALIDATION Lasso regression > >> >> > >> >> library(glmnet) > >> >> > >> >> > >> >> > >> x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,65,34,57,68,98,86,45,65,34,78,98,123,...

...t; ? ? ? >> >> Really appreciate your help. >> ? ? ? >> >> >> ? ? ? >> >> Best, >> ? ? ? >> >> >> ? ? ? >> >> ############################################################ >> ? ? ? >> >> # MSE CROSSVALIDATION Lasso regression >> ? ? ? >> >> >> ? ? ? >> >> library(glmnet) >> ? ? ? >> >> >> ? ? ? >> >> >> ? ? ? >> >> >> ? ? ? >> x1=c(34,35,12,13,15,37,65,45,47,67,87,45,46,39,87,98,67,51,10,30,6...

I found this on CrossValidated: "A medical statistician once told me, that they use SAS because if they make mistakes due to software bugs and it comes to lawsuits, SAS will recompensate them. R comes without warranty." Kjetil

Hi: I have asked this question on Cross-Validated. So it might be a cross posting but havent received any responses to it. I am trying to see which distribution will best fit the data I am working on. The dataset is as following: Site Nausea headache Abdominal Distension 1 17 5 10 2 12

...squared:? 7.038e-05,??? Adjusted R-squared: 3.705e-05 F-statistic: 2.112 on 1 and 29998 DF,? p-value: 0.1462 ``` The strange thing is that the code works perfectly for N=200 or N=2000. It's only for larger N that this thing happen U(for example, N=20000). I have tried to ask for example in CrossValidated <https://stats.stackexchange.com/questions/410050/increasing-number-of-observations-worsen-the-regression-model> but the code works for them. Any help? I am runnign R 3.6.0 on Kubuntu 19.04 Best regards Raffaele [[alternative HTML version deleted]]

Hi My name is karthikeyan. I am using svm in R for my data set. my data set contain 60 finance ratio as variables and i want to classify into group of good and bad. I want to know how to do the crossvalidation for the svm . first i am doing modelling and i am predict and i am calculating the probabilities how can i do the cross validation and how can i plot the svm for this variables waiting for your reply thanking you karthik [[alternative HTML version deleted]]

Hi, another question about validate() in Design library. The arugment "B" of this function is number of repetition for method="bootstrap", which is easy to understand; but for method="crossvalidation", B is the number of groups of omitted observations. This is confusing, I don't understand what it means. Let's say 5-fold cross validation, all samples are divided into 5 groups of equal number of samples, 4 groups will be used as training and the model developed there will be te...

Hi, Users, I am a new user. I am trying to partition data into training and test. Is there any R package or function that can partition dataset? Also, is there any package do crossvalidation? Any help will be appreciated. Best, Pat [[alternative HTML version deleted]]

Dear r-help I am trying to run LDA on a training data set, and test it on another data set with the same variables. I found examples using crossvalidation, and using training and testing data sets set up with sample, but not when they are preassigned. Here is what I tried # FIRST SET UP A DATAFRAME WITH ALL THE DATA AND CREATE NEW VARIABLES traintest1 <- arnaudnognod1[arnaudnognod1$DISC_USE1 == 1.01|arnaudnognod1$DISC_USE1 == 1.03|arnaudn...

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