similar to: parcor 0.2-2 - Regularized Partial Correlation Matrices with (adaptive) Lasso, PLS, and Ridge Regression

Dear R users, an update of the package ppls - Penalized Partial Least Squares - is now available on CRAN. It implements the methods described in N. Kr?mer, A.-L. Boulesteix, G. Tutz "Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data" Chem. Intell. Lab. Sys. 2008 http://dx.doi.org/10.1016/j.chemolab.2008.06.009 Features of the package

Dear R users, an update of the package ppls - Penalized Partial Least Squares - is now available on CRAN. It implements the methods described in N. Kr?mer, A.-L. Boulesteix, G. Tutz "Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data" Chem. Intell. Lab. Sys. 2008 http://dx.doi.org/10.1016/j.chemolab.2008.06.009 Features of the package

A new package ppls is now available on CRAN. The ppls package implements penalized Partial Leasts Squares (PLS). In a nutshell, supervised dimensionality reduction via PLS is combined with penalization techniques. Features of the package include * estimation of linear regression models with penalized PLS, * estimation of generalized additive models with penalized PLS based on splines

A new package ppls is now available on CRAN. The ppls package implements penalized Partial Leasts Squares (PLS). In a nutshell, supervised dimensionality reduction via PLS is combined with penalization techniques. Features of the package include * estimation of linear regression models with penalized PLS, * estimation of generalized additive models with penalized PLS based on splines

I wish to estimate sparse causal networks from simulated time series data. Although there's some discussion about this problem in the literature (at least a few authors have used lasso and l(1,2) regularization to enforce sparsity in multivariate autoregressive models, e.g., http://user.cs.tu-berlin.de/~nkraemer/papers/grplasso_causality.pdf), I can't find any R packages with these

Hi, There is an excellent article at http://www.biomedcentral.com/1471-2105/9/307 by Stroble, et al. describing variable importance in random forests. Does anyone have any suggestions (besides imputation or removal of cases) for how to deal with data that *have* missing data for predictor variables? Below is an excerpt of some code referenced in the article. I have commented out one line and

Full_Name: Anne-Laure Boulesteix Version: 1.6.0 OS: Linux Submission from: (NULL) (141.84.161.156) Hi, I noticed the following problem with 1.6.0 (which was not present in 1.5.1): I work with very large datasets and encountered a problem with memory allocation, (probably a memory leak). I wrote a programm with a loop. In this loop in each iteration I create a very large matrix and assign

Dear R community, I'm curious to know how people go about estimating standard errors for parameter estimates after model selection by ridge regression and the lasso. Do you have any practical or theoretical advice? Warmly, Andrew -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax:

Dear All The problem is about regularization methods in multiple regression when the independent variables are collinear. A modified regularization method with two tuning parameters l1 and l2 and their product l1*l2 (Lambda 1 and Lambda 2) such that l1 takes care of ridge property and l2 takes care of LASSO property is proposed The proposed method is given

Hello All, I have a data.frame with this structure: m <- matrix(sample(c(rep('yes', 10, replace = TRUE), rep('no', 10, replace = TRUE), NA), 500, replace = TRUE), nrow = 100, ncol = 5) colnames(m) <- colnames(m, do.NULL = FALSE, prefix = "col") m <- as.data.frame(m) I need to generate a Venn diagram from this data.frame, displaying the various intersections of

Hello all, here's a collection of answers I got on my question concerning partial correlation coefficients: Some people gave a simple formula for the three-variable-case, as did Dave Lucy: pcor <- function(v1, v2, v3) { c12 <- cor(v1, v2) c23 <- cor(v2, v3) c13 <- cor(v1, v3) partial <- (c12-(c13*c23))/(sqrt(1-(c13^2)) * sqrt(1-(c23^2)))

Dear Stefan, In the paper "Improved Forward-Adaptive Prediction for MPEG-4 Audio Lossless Coding", a non-linear compander is applied to the parcor coefficients prior to quantization. This compander is designed in order to minimize quantization error, especially for magnitudes close to unity. If you determine the typical distribution of magnitudes of the LPC coefficients, you could

Gene names being misinterpreted by spreadsheet software (read.csv is no different) is a classic issue in bioinformatics. It seems like every practitioner ends up encountering this issue in due time. E.g. https://pubmed.ncbi.nlm.nih.gov/15214961/ https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7 https://www.nature.com/articles/d41586-021-02211-4

Hello, I have questions regarding penalized Cox regression using survival package (functions coxph() and ridge()). I am using R 2.8.0 on Ubuntu Linux and survival package version 2.35-4. Question 1. Consider the following example from help(ridge): > fit1 <- coxph(Surv(futime, fustat) ~ rx + ridge(age, ecog.ps, theta=1), ovarian) As I understand, this builds a model in which `rx' is

Dear list members, I'm running some tests looking at differences between means for various levels of a factor, using Tukey's HSD method. I would like to plot the data as boxplots or dotplots, with horizontal significance lines indicating which groups are statistically significantly different, according to Tukey HSD. Here's a nice image showing an example of such a graphical

Dear all, I am trying to do a n-fold cross-validation for a regularized discrimant function analysis using rda from the package klaR. However, I have problems to predict the groups from the test/validation sample. The exmaples of the R documantation and some online webpage also do not work. Does anybody know what I have done wrong? Here my code # I want to use the first 6 observations for

Hi all, I would greatly appreciate if someone was so kind and share with us a package or method that uses a regularized regression approach that balances a regression model performance and model complexity. That said I would be most grateful is there is an R-package that combines Ridge (sum of squares coefficients), Lasso: Sum of absolute coefficients and Best Subsets: Number of coefficients as

Sorry, I forgot to mention that I used the package "klaR". > > Dear all, I am trying to do a n-fold cross-validation for a > regularized discrimant function analysis using rda from the package > klaR. However, I have problems to predict the groups from the > test/validation sample. The exmaples of the R documantation and > some online webpage also do not work.

Hello, I am trying to explore the use of random forests for classification and am certain about the interpretation of the importance measurements. When having the option "importance = T" in the randomForest call, the resulting 'importance' element matrix has four columns with the following headings: 0 - mean raw importance score of variable x for class 0 (where

Hello, I'm an MD working in an eye clinic. I'm learning by myself to use R for use in my research works and for implementation in a software project. There are some authors who recomends the use of Cox regression as a substitute for Logistic regression (<a href="http://www.biomedcentral.com/1471-2288/3/21.pdf"> Barros AJD, Hirakata VN. BMCMedical Research Methodology, 2003;