search for: dmbate

This is slightly off-topic but I would be interested in whether anyone has succeeded in creating a filter expression for Google's gmail system that will select messages sent through the R-help and R-devel lists. It seems as if it should be easy to select on '[R]' or '[Rd]' in the subject line but I haven't been able to work out the exact syntax that would do this and not

Hi, I have an unbalanced dataset on which I would like to perform a one-way anova test using R (aov). According to Wannacott and Wannacott (1990) p. 333, one-way anova with unbalanced data is possible with a few modifications in the anova-calculations. The modified anova calculations should take into account different sample sizes and a modified definition of the average. I was wondering if the

Dear all, One can use ks.test(x,y) for a one-sample kolmogorov-smirnov test: x being the data sample y being a string specifying a distribution I notice the help on ks.test does not tell you how to get such a list. Is this a hole in my R knowledge? Where can I get a list of the strings specifying the possible distributions? and more specifically What would be the string and following

Hi All, I am currently conducting a mixed model. I have 7 repeated measures on a simulated clinical trial. If I understand the model correctly, the outcome is the measure (as a factor) the predictors are clinical group and trial (1-7). The fixed factors are the measure and group. The random factors are the intercept and id and group. I tried using 2 functions to calculate mixed effects.

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

I need to wrap a loop inside a function and am having a small bit of difficulty getting the results I need. Below is a replicable example. # define functions pcm <- function(theta,d,score){ exp(rowSums(outer(theta,d[1:score],'-')))/ apply(exp(apply(outer(theta,d, '-'), 1, cumsum)), 2, sum) } foo <- function(theta,items, score){ like.mat <-

I would like to draw a sample from a linear mixed model y=Xb+Zu+e which has been fitted with lme(), i.e. a model y ~ N(Xb, C), where C=Z cov(u) Z' + cov(e). I've tried to figure out how to extract C from an lme object, because that would solve my problem when also using the predict() function, but without any luck. Can anyone help on that?

Hi, Does anyone know how to extract fixed effects SE values from generalized linear mixed models estimated using the lmer function in the lme4 library? I searched attributes and structure with no luck. Thanks Frank A. La Sorte, Ph.D. Department of Fisheries and Wildlife Sciences University of Missouri Columbia, MO 65211 USA

...math.ethz.ch wrote: > From: Dave Atkins <datkins at u.washington.edu> > Date: 1 January 2006 1:40:45 AM > To: r-help at stat.math.ethz.ch > Subject: Re: [R] lme X lmer results > > > > Message: 18 > Date: Fri, 30 Dec 2005 12:51:59 -0600 > From: Douglas Bates <dmbates at gmail.com> > Subject: Re: [R] lme X lmer results > To: John Maindonald <john.maindonald at anu.edu.au> > Cc: r-help at stat.math.ethz.ch > Message-ID: > <40e66e0b0512301051i2dc0f257r745c70e749c250f0 at mail.gmail.com> > Content-Type: text/plain; charset=ISO-885...

Hello, In my study, mixed effects model is required and the number of fixed effects is very large. When lme package is employed, a model error is displayed once the number of fixed factors in the formula reaches 200. Is this the maximum number of fixed factors can be handled by lme package? If not, what is possible reason of the error message? Thanks Yongxiang

I have been looking into both of these approaches to conducting a GLMM, and want to make sure I understand model specification in each. In particular - after looking at Bates' Rnews article and searching through the help archives, I am unclear on the specification of nested factors in lmer. Do the following statements specify the same mode within each approach? m1 = glmmPQL(RICH ~ ZONE,

Hi All, I am currently preparing some form of slideshow introducing R and its capabilities for some colleagues. The thing will be about 30 mins, and I'd like to have some "pretty pictures" and some "amazing facts" (I'm trying to sell, obviously :)). Can I ask if it's possible to easily retrieve a gross figure of the number of functions in R considering the

Hi, Thanks for reading. I am running a process in R for microarray data analysis. RedHat Enterprise Linux 4, dual AMD CPU, 6G memory. However, the R process use only a total of <200M memory. And the CPU usage is total to ~110% for two. The program takes at least 2 weeks to run at the current speed. Is there some way we can increase the usage of CPUs and memories and speed up? Any

I'll address two issues. The first is today's error message and the other is change management for contributed packages on CRAN. TODAY'S ERROR MESSAGE I switched from the 0.995-1 versions of lme4 and Matrix to those referenced in the subject line this afternoon. Prior to using these packages on anything else, I applied them to code that 'worked' (provided numerical results

I have uploaded version 0.96-1 of both Matrix and lme4 to CRAN. The source package should migrate to CRAN over the weekend and binary packages should be available some time next week. As for previous releases, the versions of these two packages are interdependent. The lme4 package requires Matrix_0.96-1 or later but we cannot enforce the other dependency. Please remember that if you upgrade

Version 0.98-1 of the lme4 package and of the Matrix package are now on CRAN. This version provides the adaptive Gauss-Hermite quadrature (AGQ) method for fitting generalized linear mixed models. A new generic function mcmcsamp has been added with a method for objects in the "lmer" (linear mixed model fit or generalized linear mixed-effects model fit) class. This function provides a

This is a "heads up" to those using the lmer function for fitting linear mixed models or generalized linear mixed models. In the next few days Martin and I will upload a new version of the Matrix package that employs a new representation of linear mixed models based on the supernodal Cholesky factorization provided by Tim Davis's CHOLMOD library of sparse matrix code. This

I have uploaded version 0.96-1 of both Matrix and lme4 to CRAN. The source package should migrate to CRAN over the weekend and binary packages should be available some time next week. As for previous releases, the versions of these two packages are interdependent. The lme4 package requires Matrix_0.96-1 or later but we cannot enforce the other dependency. Please remember that if you upgrade

Version 0.98-1 of the lme4 package and of the Matrix package are now on CRAN. This version provides the adaptive Gauss-Hermite quadrature (AGQ) method for fitting generalized linear mixed models. A new generic function mcmcsamp has been added with a method for objects in the "lmer" (linear mixed model fit or generalized linear mixed-effects model fit) class. This function provides a

This is a "heads up" to those using the lmer function for fitting linear mixed models or generalized linear mixed models. In the next few days Martin and I will upload a new version of the Matrix package that employs a new representation of linear mixed models based on the supernodal Cholesky factorization provided by Tim Davis's CHOLMOD library of sparse matrix code. This