similar to: cross validation for lme

Hello, is there a special package/method to cross-validate linear fixed effects and mixed effects models (from lme)? I've tried cv.glm on an lme (hoping that it may deal with any kind of linear model ...), but it raises an error: Error in eval(expr, envir, enclos) : couldn't find function "lme.formula" so I guess it's not dealing with an lme. I've realized that

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Hi, all When I am using mle.cv(wle), I find a interesting problem: I can't do leave-one-out cross-validation with mle.cv(wle). I will illustrate the problem as following: > xx=matrix(rnorm(20*3),ncol=3) > bb=c(1,2,0) > yy=xx%*%bb+rnorm(20,0,0.001)+0 > summary(mle.cv(yy~xx,split=nrow(xx)-1,monte.carlo=2*nrow(xx),verbose=T), num.max=1)[[1]] mle.cv: dimension of the split subsample

Hello: I am new to R and statistics and I have two questions. First I need help to interpret the cross-validation result from the R linear discriminant analysis function "lda". I did the following: lda (group ~ Var1 + Var2, CV=T) where "CV=T" tells the lda to do cross-validation. The output of lda are the posterior probabilities among other things, but I can't find an

Folks; I am having a problem with the cv.glm and would appreciate someone shedding some light here. It seems obvious but I cannot get it. I did read the manual, but I could not get more insight. This is a database containing 3363 records and I am trying a cross-validation to understand the process. When using the cv.glm, code below, I get mean of perr1 of 0.2336 and SD of 0.000139. When using a

Hi All, I have a fitted model called glm.fit which I used glm and data dat is my data frame pred= predict(glm.fit, data = dat, type="response") to predict how it predicts on my whole data but obviously I have to do cross-validation to train the model on one part of my data and predict on the other part. So, I searched for it and I found a function cv.glm which is in package boot.

Good Day All, I have a negative binomial model that I created using the function glm.nb() with the MASS library and I am performing a cross-validation using the function cv.glm() from the boot library. I am really interested in determining the performance of this model so I can have confidence (or not) when it might be applied elsewhere If I understand the cv.glm() procedure correctly, the

Hi, I have developed a logistic regression model in the form of (factor_1~ numeric + factor_2) and would like to perform a cross-validation or some similar form of sensitivity analysis on this model. using cv.glm() from the boot package: # dataframe from which model was built in 'z' # model is called 'm_geo.lrm' # as suggested in the man page for a binomial model: cost <-

Dear mixed-modelers, I have built a mixed model and I'm having serious trouble with interpreting the output. I want to test differences in the coefficient of variation (CV) of light across 3 tree crown exposures (Depth). I have measured direct and diffuse radiation (RF) and I want to test for differences for each kind of radiation between crown exposures, as well as differences between

Using the "ergoStool" data cited in Mixed-Effects Models in S and S-PLUS by Pinheiro and Bates as an example, we have ======== > library(nlme) > fm <- lme(effort~Type-1, data=ergoStool, random=~1|Subject) > summary(fm) Linear mixed-effects model fit by REML Data: ergoStool AIC BIC logLik 133.1308 141.9252 -60.5654 Random effects: Formula: ~1 | Subject

If my aim is to select a good subset of parameters for my final logit model built using glm(). What is the best way to cross-validate the results so that they are reliable? Let's say that I have a large dataset of 1000's of observations. I split this data into two groups, one that I use for training and another for validation. First I use the training set to build a model, and the the

Hi everybody, I'm trying to use cross-validation (cv.glm) for count data. Does someone know which is the appropriate cost function for Poisson distribution? Thank you in advance. Valerio. Conservation Biology Unit Department of Environmental and Territory Sciences University of Milano-Bicocca Piazza della Scienza,1 20126 Milano, Italy.

Dear authors, I?m an Italian PhD student and I?m dealing with linear mixed models. I?d like to use your lme library, but I have a problem. I?m able to estimate a null model. For example, using SAS data, I can estimate the model: lme(y ~ 1, data = Mississippi, random = ~ 1|influent, method="ML") As suggested in the literature I want to ?test? the significance of the second level

This post is NOT a question, but an answer. For readers please disregard all earlier posts by myself about this question. I'm posting for two reasons. First to say thanks, especially to Dimitris, for suggesting the use of errorest in the ipred library. Second, so that the solution to this problem is in the archives in case it gets asked again. If one wants to run a k-fold cross-validation

I apologize for posting on this question again, but unfortunately, I don't have and can't get access to MASS for at least three weeks. I have found some code on the web however which implements the prediction error algorithm in cv.glm. http://www.bioconductor.org/workshops/NGFN03/modelsel-exercise.pdf Now I've tried to adapt it to my purposes, but since I'm not deeply familiar

<< repost because previous attempt was not plain text, sorry! >> Hi Folks, I have a pretty simple problem: after building a multivariate linear model, I need to report my 95% confidence interval for predictions based on future observations. I know that one option is to use predict(interval="prediction") but I'm curious about less parametric ways to get an estimate. I

I have taken the liberty of including the R-help mailing list on this reply as that is the appropriate place to discuss lmer results. On 4/5/06, Andreas Svensson <andreas.svensson at bio.ntnu.no> wrote: > Hello again > I have now recieved some helpful hints in this matter and will summarize them but first let me reiterate the problem: > > I had two treatments: 2 types of food

Dear all, when performing a meta analysis I have two results obtained with logLik and lme, which I do not quite understand. The results are based on these data: study or var 1 0.10436 0.299111 2 -0.03046 0.121392 3 0.76547 0.319547 4 -0.19845 0.025400 5 -0.10536 0.025041 6 -0.11653 0.040469 7 0.09531 0.026399 8 0.26236 0.017918 9 -0.26136 0.020901 10 0.45742 0.035877 11

I'm trying to use "predict" with a linear mixed-effects logistic regression model fitted with nlmmPQL from the MASS library. Unfortunately, I'm getting an error "non-conformable arguments" in predict.lme, and I would like to understand why. I have used the same call to "predict" with "glm" models without problems. I assume I'm doing