similar to: Oracle Approximating Shrinkage in R?

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

Dear R-users, I am using stepplr for L2 regularized logistic regression. Since number of attribute is too large i discarded interaction terms. Everything is fine but only problem i have faced that i cannot choose a good shrinkage coefficient (lambda). If CV is the best way to estimate, can you please elaborately tell me how to select lambda in stepplr using CV? Except CV is there any other

1. According to the doc of PAMR, the shrinkage threshold is determined by cross-validation. Does this mean that user need not tune any parameter? 2. I tried two applications using PAMR, the results are very disappointing. The attached are the cross-validation results. You can see that the classification errors are relatively high (0.2 at the best), in the case of two categories classification,

In Dr. Wood's book on GAM, he suggests in section 4.1.6 that it might be useful to shrink a single smooth by adding S=S+epsilon*I to the penalty matrix S. The context was the need to be able to shrink the term to zero if appropriate. I'd like to do this in order to shrink the coefficients towards zero (irrespective of the penalty for "wiggliness") - but not necessarily all the

does anyone know how to extract Shrinkage intensity lambda and lambda.var values after run cov.shrink(x)? thanks, KZ [[alternative HTML version deleted]]

While I don't know anything about Box's M test, I googled around and found a Matlab M-file that computes it. Below is my straight-forward translation of the code, without knowing Matlab or the formula (and done in a few minutes). I hope this demonstrates one of Prof. Ripley's point: If you really want to shoot yourself in the foot, you can probably program R to do that for you. [BTW:

Anyone from Chicago area interested in this course? Please email XLSolutions so they can schedule it in Chicago. We ran out of travel budget in my company :( Date: Wed, 2 Aug 2006 13:20:23 -0700From: elvis@xlsolutions-corp.comSubject: [S] Course***Dr Frank Harrell's Regression Modeling Strategies in R/Splus course *** September 2006 near you (San Francisco, Washington DC, Atlanta)To:

Dear Group Members, Forgive me if I am a little bit out of subject. I am looking for a good way to test the homogeneity of two variance-covariance matrices using R, prior to a Hotelling T test. Youll probably tell me that it is better to use a robust version of T, but I have no precise idea of the statistical behaviour of my variables, because they are parameters from the harmonics of

> Date: Mon, 20 Feb 2006 16:43:55 -0600 > From: Aldi Kraja <aldi at wustl.edu> > > Hi, > Using package gclus in R, I have created some graphs that show the > trends within subgroups of data and correlations among 9 variables (v1-v9). > Being interested for more details on these data I have produced also the > var-covar matrices. > Question: From a pair of two

CRAN (and crantastic) updates this week New packages ------------ * allan (1.0) Alan Lee http://crantastic.org/packages/allan Automates Large Linear Analysis Model Fitting * andrews (1.0) Jaroslav Myslivec http://crantastic.org/packages/andrews Andrews curves for visualization of multidimensional data * anesrake (0.3) Josh Pasek http://crantastic.org/packages/anesrake This

Is there a function in r that let's you efficiently invert a positive definite symmetric Block Toeplitz matrix? My matrices are the covariance matrices of observations of a multivariate time series and can be 1000*1000 or larger. I know the package 'ltsa' which seems to use the Trench algorithm to compute the inverse of a Toeplitz matrix. I am looking for a so to say

I am creating covariance matrices from sets of points, and I am having frequent problems where I create matrices that are non-positive definite. I've started using the corpcor package, which was specifically designed to address these types of problems. It has solved many of my problems, but I still have one left. One of the matrices I need to calculate is a cross-covariance matrix. In other

Hi, I've been trying to figure out a special set of covariance matrices that causes some symmetric zero elements in the inverse covariance matrix but am having trouble figuring out if that is possible. Say, for example, matrix a is a 4x4 covariance matrix with equal variance and zero covariance elements, i.e. [,1] [,2] [,3] [,4] [1,] 4 0 0 0 [2,] 0 4

Dear guRus, is there a package that calculates the Approximate Entropy (ApEn) of a time series? RSiteSearch only gave me a similar question in 2004, which appears not to have been answered: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/28830.html RSeek.org didn't yield any results at all. Happy holidays (where appropriate), Stephan

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Dear list, I am looking for an implementation of random rotation matrix generation in R to do a rotation test: I want to use the matrices to create random multivariate normal matrices with common covariance structure and mean based on an observed data matrix. The rRotationMatrix-function in the mixAK-package is an option, but as far as I can tell I need to draw rotation matrices with determinant

Hello R-help, I need to compute matrices of first derivatives of a covariance matrix C with entries given by c_ij=theta*exp(-0.5* sum(eta*(x[i,]-x[j,])^2)), wrt to elements of eta, a m-dimensional vector of parameters, given a n*m data matrix x. So far, I have been computing matrices for each parameter (given by par[index]) analytically, using the following kmatder<- function(x, par, index) {

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%

I am having trouble getting a covariance matrix for each cluster which is output by kmeans(). My input looks like: > imagedat <- read.table("table", header=TRUE) > imagedat Red Green Blue 0_0 5 7 8 1_0 5 5 18 2_0 7 8 49 3_0 22 8 76 4_0 54 10 67 5_0 50 9 28 6_0 18 10 15 7_0 9 7 6

I have conducted a discriminant function analysis with lda() in the MASS Package, and I am interested in testing that the covariance matrices of the groups are equal. Does anybody have any suggestions on how I could test for equality between covariance matrices? Any help would be great. Thank you in advance. Cheers -Rob -- [[alternative HTML version deleted]]