similar to: How to see a R code from a package?

Dear my R friends, I want to get a number from a list using paste function. In my example, lambda.rule <- "lambda.1se" cvtest is a list (result from cv.glmnet) and cvtest$lambda.1se [1] 1.308973 I want to call the value using paste function. I used get function but there was an error. test <- get(paste("cvtest$",lambda.rule, sep="")) Error in

I want to change leaf color by group in hclust plot. I've seen several answers about A2R package but I cannot install A2R and Rtools in windows. Do you know how to install A2R package in windows or how to change leaf color by group in hclust plot? Thank you in advance, Soyeon

Dear R users, Hi. I am using prediction function in ROCR package. y consists of two classes 0 and 1. However, since I am using cross-validation, a sampled small number of y may consist of only one class >y [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 In this case, prediction function gives an error: Error in prediction(predic, y) : Number of classes is not equal to 2. ROCR currently supports

Dear All, I have data which contains 14 variables. And I have to regress one of variables on each variable (simple 13 linear regressions) I try to make a loop and store only R-squared colnames(boston) [1] "CRIM" "ZN" "INDUS" "CHAS" "NOX" "RM" "AGE" [8] "DIS" "RAD"

Dear All, I have vn variable > vn [1] "V300" "V376" What I want to get is 300 376 without V and "" from vn variable. Could you help me about this issue? Thank you, Soyeon [[alternative HTML version deleted]]

Dear All, This is hard to describe so I made a simple example. set.seed(1001) total <- 0 data <- vector("list", 30) for(i in 1:30) { data[[i]] <- runif(50) } Let's call a data set runif(50). While the for loop is running, 100 data sets are generated. I want to restore 23th data set (the data set generated in 23th for loop) without the loop. I've tried set.seed(1023)

Dear All, I made a function which gives 3 plots in one window(I used par(mfrow=c(1,3)) in the function). Using that function 3 times, I want to produce 9 plots in one window. I tried par(mfrow=c(3,1)) or par(mfrow=c(3,3)) but it didn't work. For example, pf <- function(p) { par(mfrow=c(1,3)) plot(c(p:(p+10)),c(1:11)) plot(c(p:(p+10)),c(2:12)) plot(c(p:(p+10)),c(3:13)) } p <-

Hi. I am trying to generate a correlated binary data set. I've tried to use mvtBinaryEP, binarySimCLF, and bindata packages but none of them works in R version 2.15.1. Do you know any package to generate correlated binary covariates and work in R version 2.15.1, or how to generate it? Thanks, [[alternative HTML version deleted]]

Hi all, I am using the glmnet R package to run LASSO with binary logistic regression. I have over 290 samples with outcome data (0 for alive, 1 for dead) and over 230 predictor variables. I currently using LASSO to reduce the number of predictor variables. I am using the cv.glmnet function to do 10-fold cross validation on a sequence of lambda values which I let glmnet determine. I then take

Hello fellow R users, I have been getting a strange error message when using the cv.glmnet function in the glmnet package. I am attempting to fit a multinomial regression using the lasso. covars is a matrix with 80 rows and roughly 4000 columns, all the covariates are binary. resp is an eight level factor. I can fit the model with no errors but when I try to cross-validate after about 30 seconds

Hi there, I am trying to use predict() with an object returned by cv.glmnet(), and get the following error: no applicable method for 'predict' applied to an object of class "cv.glmnet" What's wrong? my code: x=matrix(rnorm(100*20),100,20) y=rnorm(100) cv.fit=cv.glmnet(x,y) predict(cv.fit,newx=x[1:5,]) coef(cv.fit) Thanks so much, Asaf -- View this message in context:

Dear List, I'm running simulations using the glmnet package. I need to use an 'automated' method for model selection at each iteration of the simulation. The cv.glmnet function in the same package is handy for that purpose. However, in my simulation I have p >> N, and in some cases the selected model from cv.glmet is essentially shrinking all coefficients to zero. In this case,

Hi, I am trying to do multinomial regression using the glmnet package, but the following gives me an error (for no reason apparent to me): library(glmnet) cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6), nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5, nfolds=2) The error i get is: Error in if (outlist$msg != "Unknown error") return(outlist) : argument is of

Hi, I am trying to do logistic regression for data of 104 patients, which have one outcome (yes or no) and 15 variables (9 categorical factors [yes or no] and 6 continuous variables). Number of yes outcome is 25. Twenty-five events and 15 variables mean events per variable is much less than 10. Therefore, I tried to analyze the data with penalized regression method. I would like please some of the

Dear R users, if all my numerical variables in my datasets having the same units, may I leave them unnormalized, just do cv.glmnet directly(cv.glmnet(data,standardize=FALSE))? i know normally if there is a mixture of numerical and categorical , one has to standardize the numerical part before applying cv.glmnet with standardize=fase, but that's due to the different units in the numerical

All, For my understanding, I wanted to see if I can get glmnet predictions using both the predict function and also by multiplying coefficients by the variable matrix. This is not worked out. Could anyone suggest where I am going wrong? I understand that I may not have the mean/intercept correct, but the scaling is also off, which suggests a bigger mistake. Thanks for your help. Juliet Hannah

Hi All,  I have been trying to use glmnet package to do LASSO linear regression. my x data is a matrix n_row by n_col and y is a vector of size n_row corresponding to the vector data. The number of n_col is much more larger than the number of n_row. I do the following: fits = glmnet(x, y, family="multinomial")I have been following this

Hi all I am using the caret package and having difficulty in obtaining the results using regression, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE);

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

Hi I'm getting the following warning msg after ?cv.glmnet and I'm wondering what it means... dim(x) 10 12000; dim(y) 10; #two groups case=1 and control=0 cv.glmnet(x, y) Warning message: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold Thanks, .kripa [[alternative HTML version deleted]]