similar to: Multivariate autoregressive models with lasso penalization

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

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

Hi Lauren, Did you ever receive an answer on this? I?ve been having the same errors, and am stumped, so I?d love to hear how you sorted this out with your models. Thanks for your help! Cheers, Rachel -- Rachel Smith PhD Candidate Odum School of Ecology University of Georgia Athens, GA 30602

Dear community, I am currently attempting to perform a (L1) penalized ordinal logistic regression with proportional odds. For the moment I only found R packages allowing to perform forward or backward continuation ratio model with several penalizations. Does anyone have a clue of what R package I could use ? I am not even quite sure that penalized logistic regression with proportional odds has

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y

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

Dear all: I have a question about how to get the optimal estimate of coefficients using the penalized quantile regression (LASSO penalty in quantile regression defined in Koenker 2005). In R, I found both rq(y ~ x, method="lasso",lambda = 30) and rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06) can give the estimates. But, I didn't find a way using either of

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

Hello, in my doctoral thesis i try to model time series crash count data with an inar(1)-process, but i have a few problems in writing the r-code. is there someone, who works with inar-processes. i would be very grateful, if someone gives me some ideas in writing the code. nazli

Hi, I am trying to replicate Engle and Manganelli's paper Conditional Autoregressive Value at Risk (CAViaR) by Regression Quantiles. I have the Matlab code which I cannot get to work as I have never used Matlab before, does anyone know if there is the same code available to estimate the CAViaR models in R? Thanks, Shane -- View this message in context:

folks, does anyone know if the SEMIFAR model has been implemented in R? i see that there's a S-FinMetrics function SEMIFAR() that does the job, but I have no access to that software. essentially, this semiparametric fractional autoregressive model introduces a deterministic trend to the FARIMA(p,d,0) model (which, as i understand it, takes care of the random trend and short and long memory).

Hey, all, I may just be missing something, but I'm trying to construct a temporal autoregression with an independant variable other than just what is happened at a previous point in time. So, the model structure would be something like y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a*x(t) I'm even considering a model of y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a1*x(t)+a2*x(t-1)... So, my data looks like

Hello - I need to do simple linear autoregressive model with R software for my thesis. I looked into all your documentation and I am not able to find anything too helpful. Can someone help me with the codes? Thanks Emil [[alternative HTML version deleted]]

Dear R users, I would like to simulate, for 20000 replications, an autoregressive process: y(t)=0.8*y(t-1)+e(t) where e(t) is i.i.d.(0,sigma*sigma), Thank you in advance ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts musicaux

I am having difficulty fitting a mgcv::gamm model that includes both a random smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth terms with a factor interaction using the 'by=' option work fine. Both on my actual data and a toy example (below) I am getting the same error so am inclined to wonder if this is either a bug or a model that gamm is simply unable

Dear R users, I am trying to find a package that can estimate an autoregressive model for discrete data. I am imagining a Poisson or Gamma process in which the mean (say mu) follows a process such as mu_t = a + b*x + c*mu_{t-1} Suppose I have data on the time-series Poisson outcomes and x and would like to obtain ML estimates for b and c. Does anyone know of a package that can do this

Hi, I am using the mAr package to calibrate an Multivariate model (size 3, order 12). I am trying to do the two following things: 1. I would like to calibrate the model using not a single time series, but several of them: each time series should be seen as one "independent" realisation of the mAr process; for instance this happens when you have a time series with lacking data