similar to: Very small estimated random effect variance (lme)

The command: rpcclient -U jarboed -c 'adddriver "Windows NT x86" "RICOH Aficio 700 PS:RINH7PS5.DLL:RICNH703.PPD:RINH7PSU.DLL:RINH7PSU.HLP:NULL:RAW:RNH7kmUI .DLL,RNH7PS.DLL,RNH7km.INI,RINH7PS5.NTF,RNH7Help.HLP,TrackID.DLL,TIFmtA. DLL,TIBase64.DLL,TISHMEM.DLL,TICMD.EXE"' LINPS1 fails on 2.2.8a and 3rc4 (substituting server name appropriately in the above command). User

Dear list, I do apologize if these are basic questions. I am fitting some GAM models using the mgcv package and following the model selection criteria proposed by Wood and Augustin (2002, Ecol. Model. 157, p. 157-177). One criterion to decide if a term should be dropped from a model is if the estimated degrees of freedom (EDF) for the term are close to their lower limit. What would be the

Dear R-helpers, i am trying to make a shared library from a Fortran subroutine, and i therefore used (after reading the documentation): Rcmd SHLIB forfile.f #(R1.70, Win2000) And the error is: " 'perl' is not recognized as an internal or external command,operable program or batch file." So i went ahead and tried to install Perl (from the suggested website in

Hi all, recently, i tried making a package with my own functions - which worked fine, until i reinstalled MikTex (in Win2000). Now i get the error message: * checking for working latex ... NO * using log directory 'C:/Rlibs/R4PG.Rcheck' * checking for file 'R4PG/DESCRIPTION' ... OK * checking if this is a source package ... OK ERROR Installation failed. But i set the path to

Dear R-helpers, i have the following situation: i have a bunch of y=b0 + b1*x from different studies, and want to estimate a "general" y=f(x). I only have the b0,b1's and R-squareds. Should i weigh the separate equations by their R-squared? thanks Remko ^'~,_,~'^'~,_,~'^'~,_,~'^'~,_,~'^'~,_,~'^'~,_,~' Remko Duursma, Ph.D. student

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

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Dear R experts, currently I'm converting a bit of trivial Splus code to R. In Splus I came across the stamp function. Does R have something similar? Thank you very much for your time. Ananke P.S.: Keep up the good work on R. It's great! HotBot - Search smarter. http://www.hotbot.com -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list --

Dear R users, I hope that this question is not too dumb, but I can't figure out how to do it. I plot a function, e.g. plot(sin, -pi, 2*pi). Then I would like to have the aerea from the x-axis up to the graph from x ranging from -3 to 0, shaded red and the other part (x from 0 to 6) under the graph e.g. green or sth. like that.. Any hints on how to do that? I could draw lines (different

Dear R users, I would like to produce gifs or jpegs with R, as I would like to display them on a webpage. Is there an easier/faster/better way to do it than to generate postscript files and then use a utility like pstogif. The problem is that if possible I would like to get away without installing ghostscript, as my quota is too small. Thank you very much for your time and bandwidth, Ananke

Hi all, In Tibshirani's PNAS paper about nearest shrunken centroid analysis of microarrays (PNAS vol 99:6567), they used cross validation to choose the amount of shrinkage used in the model, and then test the performance of the model with the cross-validated shrinkage in separate independent testing set. If I don't have the luxury of having independent testing set, can I just use the

Hi, I have optimized the shrinkage parameter (GCV)for ridge and got my r2 value is 70% . to check the sensitivity of the result, I did permutation test. I permuted the response vector and run for 1000 times and draw a distribution. But now, I get r2 values highest 98% and some of them more than 70 %. Is it expected from such type of test? *I was under impression that, r2 with real data set

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

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

Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below: > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula +

Hi, I am trying to understand the alternative methods that are available for selecting variables in a regression without simply imposing my own bias (having "good judgement"). The methods implimented in leaps and step and stepAIC seem to fall into the general class of stepwise procedures. But these are commonly condemmed for inducing overfitting. In Hastie, Tibshirani and Friedman

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

Dear All, Probably it is not highly relevant question: Why do stepwise regression functions in R (step() or stepAIC()) add/delete categorical variables as a set? For example, I have a four-level factor variable d, so dummies are d1,d2,d3, as stepwise regression operates d, adding or removing, d1,d2,d3 are simultaneously added/removed. What's the concern here if operating dummies individually?

Dear R-users, I have used the nomogram function from the rms package for a logistic regresison model made with lrm(). Everything works perfectly (r version 2.15.1 on a mac). My question is this: if my final model is not the one created by lrm, but I internally validated the model and 'shrunk' the regression coefficients and computed a new intercept, how can I build a nomogram using that

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]