search for: crossvalidated

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) ?

No error message shown Please include the error message so that it is not necessary to rerun your code. This might enable someone to see the problem without running the code (e.g. downloading packages, etc.) -- Bert On Sun, Oct 22, 2023 at 1:36?PM varin sacha via R-help <r-help at r-project.org> wrote: > > Dear R-experts, > > Here below my R code with an error message. Can

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

Hello, Thank you for probably not so new question, but i am 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

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) mytest

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,

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially 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

Dear R-experts, I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. Many thanks for your help. Error in UseMethod("predict") : ? no applicable method for 'predict' applied to an object of class "c('matrix', 'array', 'double', 'numeric')" > mean(unlist(lst)) [1] NA

For what it's worth it looks like spm2 is specifically for *spatial* predictive modeling; presumably its version of CV is doing something spatially aware. I agree that glmnet is old and reliable. One might want to use a tidymodels wrapper to create pipelines where you can more easily switch among predictive algorithms (see the `parsnip` package), but otherwise sticking to glmnet

?s 20:12 de 23/10/2023, varin sacha via R-help escreveu: > Dear R-experts, > > I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. > > Many thanks for your help. > > > Error in UseMethod("predict") : > ? no applicable method for 'predict' applied to an object of class

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

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

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 <-

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible URL : <https://stat.ethz.ch/pipermail/r-help/attachments/20110704/68ecf4d2/attachment.pl>