similar to: lme library

Before reading water chemistry into a data frame I removed all missing data. Yet when I try to run cenros() to summarize a specific chemical I get an error that I do not understand: with( subset(chem, param=='Ag'), cenros(quant,ceneq1) ) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'y' I would like to learn what I did incorrectly

Hello, I would like to treat a very simple case : to fit a linear model with 5 parameters (including main terms, interactions and quadratic terms using a central composite design with 27 runs) for a constant response (e.g resp = 100.0). The fitting process works and return me a good intercept value (the value of my constant) and some negligeable effects (around e-15). But, I don't understand

Hello All, I am trying to create a legend for a black-white graph. The package I use is ggplot2. It can add colors to the legend key but not line types. Can you please help? # example from Wickman (2009, ggplot2 - elegant graphics for data analysis, page 109) library(ggplot2) huron <- data.frame(year=1875:1972, level=LakeHuron) ggplot(huron, aes(year)) +

I'm using R version 1.5.1, and Microsoft Developer Studio 97 (yes, I know it's old, but it's expensive to upgrade) on Windows 98. I created the DLL file with the compiler. The name of the file containing the subroutine is leaf.f and the subroutine is named leaf.f. In my R program (which defines a function called leaf), I have x <- .Fortran("leaf",...) where the ...

windows Vista R 2.10.1 What is the difference (or differences) between lme and lmer? Both appear to perform mixed effects regression analyses. Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone)

What is the least surprising way of initializing a factor with predefined levels and with length 0? as.factor(c("eins", "zwei", "drei"))[FALSE] does the job but looks a bit weird. -- Johannes H?sing There is something fascinating about science. One gets such wholesale returns of conjecture mailto:johannes at

Dear R users, I'm trying to fit a simple random intercept model with a fixed intercept. Suppose I want to assign a weight w_i to the i-th contribute to the log-likelihood, i.e. w_i * logLik_i where logLik_i is the log-likelihood for the i-th subject. I want to maximize the likelihood for N subjects Sum_i {w_i * logLik_i} Here is a simple example to reproduce

[i'm not subscribe to this list, please put me on CC...] I've asked this on samba-it (italian) mailing list, but with no clue. In my installation (debian sarge, samba 3.0.14a, kernel 2.6 and xfs filesystem) i've put roaming profile on quotas, and something i think strange happens. The client say to the user that cannot update the profile, also if it was under the soft (and hard)

I am failing to uncover Sweave chunks step by step using the LaTeX beamer class. The following minimal example: \documentclass{beamer} \usepackage{Sweave} \begin{document} \begin{frame}[fragile] In the year \uncover<2->{25}\uncover<3->{\Sexpr{5*5}} \uncover<4->{ <<echo=TRUE, print=TRUE>>= 5*5*101 @ } \end{frame} \end{document} leads to an error message when

I am absolutely new to R and I am aware of only a few basic command lines. I was running a robust regression in R, using the following command line library (MASS) rfdmodel1 <- rlm (TotalEmployment_2004 ~ MISSISSIPPI + LOUISIANA + TotalEmployment_2000 + PCWhitePop_2004 + UnemploymentRate_2004 + PCUrbanPop2000 + PCPeopleWithACollegeDegree_2000 + PCPopulation.of.or.over.65.years.of.age_2004)

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

I am trying to model survival data with a Weibull distribution using survreg. Units are clustered two apiece, sometimes receiving the same treatment and sometimes opposing treatment.

Using the weight argument with a variance function in lme (nlme), you can allow for heteroscedasticity of the within-group error. Is there a way to do this for the other variance components? For example, suppose you had subjects, days nested within subjects, and visits nested within days within subjects (a fully nested two-way design) and you had, say men and women subjects. Could you allow for

Dear All, I have a complex survey data with observations having differing probabilities of selection into the sample. I would like to run a linear mixed effects model and also to use weighting that takes this into consideration. As far as I know, however, the weighting option for the lme command from nlme package (or glmmPQL from MASS, for that matter) is related to heteroscedasticity. Is there a

Dear R Users, I have 12 data frames, each of 12 rows and 2 columns. e.g. FeketeJAN MEAN SUM_ AMAZON 144.4997874 68348.4 NILE 5.4701955 1394.9 CONGO 71.3670036 21196.0 MISSISSIPPI 18.9273250 6511.0 AMUR 1.8426874 466.2 PARANA 58.3835497 13486.6 YENISEI 1.4668313 592.6 OB 1.4239179 559.6 LENA 0.9342164

Hi folks, I'm just learning how to use maps. As an initial foray, I'm mapping the states that have "duty to retreat" (blue) and "stand your ground" (red) self-defense standards. Here is my extremely naive script: dtr <- c('alabama', 'arizona', 'conneticut', 'delaware', 'dist of columbia' , 'hawaii',

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

Whenever I try to load the Matrix package, I get the following error message: libRlapack.so: cannot open shared object file: No such file or directory A file with that name is indeed not on the hard disk. I am using the R version which comes with Ubuntu Hardy Heron LTS. Here is the output of R.Version(): R.Version() $platform [1] "i486-pc-linux-gnu" $arch [1] "i486" $os [1]

Dear all, I have a matrix called combine86 which looks as follows: > combine86 Sim Mean Obs Mean Sim Sum Obs Sum AMAZON 1172.0424 1394.44604 553204 659573 NILE 262.4440 164.23921 67973 41881 CONGO 682.8007 722.63971 205523 214624 MISSISSIPPI 363.0758 142.59883 124535 49054 AMUR 143.5857 89.30434 36040 22594 PARANA

Dear all, Sorry if this is too obvious. I am trying to fit my multiple regression model using lm() Before starting model simplification using step() I checked whether the model presented heteroscedasticity with ncv.test() from the CAR package. It presents it. I want to correct for it, I used hccm() from the CAR package as well and got the Heteroscedasticity-Corrected Covariance Matrix. I am not