similar to: memory.size

Dear all, I am performing a binomial glmm analysis using the lmer function in the lme4 package (last release, just downloaded). I am using the "Laplace method". However, I am not sure about what I should do to test for the significance of fixed effects in the binomial case: Is it correct to test a full model against a model from which I remove the fixed effect I want to test

hi, sorry for a possibly naive question but I am a bit of a beginner in R programming... I wrote a function which simulates some data and performs two different kinds of analyses on it. as an output I get the statistics for the two analyses (t-values). Now I would like to have an other function which reruns my first function say a 1000 times and attaches the resulting statistics in a data.frame

Hi, I'm just getting started with R and I'm having problems with some simple operations: I want to get the the sum of the column "SStot" for each year using the function by. The data set is named "SS". I've tried this: by (SS, year, sum(SStot)) and it's not working. Is it because there's a different number of rows for each year? How else can I do

Hi list! I did the following regression: reg1 <- glm(alti~sp + ovent + vivent + nuage, family=gaussian, data=meteo1) I was interested in knowing the effect of the species (sp) in reg1 and so I used the function «effect»: effect.sp <- effect ("sp", reg1, se=TRUE) with this output: sp AK BW NH OS RT SS 2.730101 2.885363 2.753774 2.750311

I can't seem to be able to change the font size in an effect display. I've tried the following: > par(cex.lab=4) > plot(effect ("alti",reg8), ylab="detection probability") and > plot(effect ("alti",reg8), ylab="detection probability", cex=4) but nothing changes. Can anyone help me? thanks. Emilie Berthiaume graduate student

Hi, I use eCS as client for samba. with samba 2.2.x I have never problems after configuration. With update my server from suse 8.1 to 9.1 was also samba changed from 2.2.x to 3.0.x. Since this I have only truble. max protocol lanman1 works without problems, only .. I have no longnames. The docs says, lanman1 is the first with long names support, also not about the pm (desktop from os2). With

Hi all, I am lookng for a truble ticket to install on my Cents os 4.4 server. RPM is always prefferd. Souce is also welcome. Have you done somethink like this before? What are the packages that you recomend for me. -- Thank you Indunil Jayasooriya -------------- next part -------------- An HTML attachment was scrubbed... URL:

Is there a way to apply a function, say cor(), to each combination of some number of variables, and this, without using loops? For example, I have day, hour, var1 and var2. How could I compute cor(var1,var2) for each day*hour combination and obtain a matrix with day, hour and the cor value for each combination? Thanks for your time, Marc =================== Marc Bélisle Professeur adjoint

Hello. I have to fit a mixed model from a repeated measures split-plot experiment in which the response variable is binary. This requires a generalised linear mixed model in which I can specify a binomial distribution. I can’t find the appropriate package in R. I have looked at glmmML, but it doesn’t seem to allow any mixed structure beyond a simple 2-level one. Can anyone point me to the

Hello. I want to plot the distribution of a continuous variable (y) in each of two groups on the same graph as histograms. I suppose one could call this a 2-d histogram? Can this be done in R? Here is a typical data.set: y group 1.2 1 3.3 1 2.4 2 5.7 1 0.2 2 etc. Bill Shipley Subject Matter Editor, Ecology North American Editor, Annals of

Hello. I cannot find out how to extract the residuals from a mixed model using the lmer function. Can someone help? Bill Shipley North American Editor, Annals of Botany Editor, "Population and Community Biology" series, Springer Publishing Département de biologie, Université de Sherbrooke, Sherbrooke (Québec) J1K 2R1 CANADA Bill.Shipley@USherbrooke.ca

Howdee, One of my student spotted something I can't explain: a probability >1 vs a normal probability density function. > dnorm(x=1, mean=1, sd=0.4) [1] 0.9973557 > dnorm(x=1, mean=1, sd=0.39) [1] 1.022929 > dnorm(x=1, mean=1, sd=0.3) [1] 1.329808 > dnorm(x=1, mean=1, sd=0.1) [1] 3.989423 > dnorm(x=1, mean=1, sd=0.01) [1] 39.89423 > dnorm(x=1, mean=1, sd=0.001) [1]

Hello. I am having trouble downloading the lme4 package from the CRAN site. The error is: > local({a <- CRAN.packages() + install.packages(select.list(a[,1],,TRUE), .libPaths()[1], available=a, dependencies=TRUE)}) trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 26129 bytes opened URL downloaded 25Kb

Hello. I am about to do a rather complicated analysis and am not sure how to do it. The experiment has a split-plot design and also repeated measures. Both of these complications require one to define an error term and it seems that one cannot specify two such terms. The split-plot command is: aov(y~covariates +A*B+Error(C), data=) where A and B are the fixed effects and C is the

Hello. Please excuse this off-topic request, but I know that the question has been debated in summary form on this list a number of times. I would find a paper that lays out the advantages and disadvantages of using different types of SS in the context of unbalanced data in ANOVA, regression and ANCOVA, especially including the use of different types of contrasts and the meaning of the

Hello. I am trying to fit a non-rectangular hyperbola function to data of photosynthetic rate vs. light intensity. There are 4 parameters that have to be estimated. I find the nls function very difficult to use because it often fails to converge and then gives out cryptic error messages. I have tried playing with the control parameters but this does not always help. Is there another

Hello. I want to estimate the predicted values and standard errors of Y=f(t) and its first derivative at each unique value of t using the smooth.spline function. However, the data (plant growth as a function of time) show substantial heterogeneity of variance since the variance of plant mass increases over time. What is the consequence of such heterogeneity of variance in terms of bias in the

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using the

Howdee, My question appears at #6 below: 1. I want to model the growth of each of a large number of individuals using a 4-parameter logistic growth curve. 2. nlme does not converge with the random structure that I want to use. 3. nlsList does not converge for some individuals. 4. I decided to go around nlsList using: t(sapply(split(data, list(data$id)), function(subd){coef(nls(mass ~

Howdee, *** I know that the lmList() function exists, yet I don't want to use it. *** Would anyone be kind enough to tell how I can apply the function lm() to each level of a given factor so to obtain the intercept and slope for each factor level within a matrix? For instance, suppose a dataframe containing 3 variables: id, x and y. I want to compute the function lm() for each level