similar to: data.frame subsetting

Graham, Confirmed. I will pass that along to the R team. As 1.8.0 is in code freeze, this may not get addressed, unfortunately. Regards, Dirk On Fri, Oct 03, 2003 at 03:16:35PM +1000, Graham Williams wrote: > Package: r-base-core > Version: 1.7.1.cvs.20030927-1 > Severity: normal > > > $ R > > R : Copyright 2003, The R Development Core Team > Version 1.8.0 beta

I am embarrassed to have to ask this but can anyone tell me of a Tukey multiple comparisons procedure available for R? I have looked through the search page, through the FAQ, and in the index of V&R (1999), and I still can't find such a thing. I see there is a ptukey function and a qtukey function but that is as far as I got. Do I need to roll my own? -- Douglas Bates

Dear WizaRds, A run of nls produces the following concise summary: > summary(cs.wt) Formula: 0 ~ wt.MM(conc, time, A1, a1, A2, a2) Parameters: Estimate Std. Error t value Pr(>|t|) A1 4.814e+02 2.240e+01 21.495 0.0296 * a1 7.401e-01 7.435e-02 9.956 0.0637 . A2 1.613e+02 1.738e+01 9.280 0.0683 . a2 1.770e-02 7.324e-03 2.417 0.2497

Would it be possible to include a new command in the ${RHOME}/etc path, which updates the Library index (Rd and html) via "R LIBINDEX". I need it because I use RPM to manage three different R installations (at home, at the institute and in the seminar rooms). I put all libraries into several packages, so it is easy for me to update a single library at all these different places. But

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

I've noticed quite a few messages relating to large data sets bedeviling R users, and having just had to program my way through one that actually caused a "Bus error" when I tried to read it in, I'd like to ask two questions. 1) Are there any facilities for aggregation of data in R? ( I admit that this will not do much for the large data set problem immediately) 2) Is there any

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

The goal: simulate chi square mixture distributions as a way of simulating likelihood ratio test statistics for some mixed models where the more specific model has some zero variance components (a la Pinheiro and Bates pg. 84-87) The problem: R doesn't have the function ms which is apparently used by simulate.lme In the current version of nlme for R, is there a way around this? Is it

Dear all, In the SASmixed package there is an example of an analysis of a split-plot experiment. The model is fm1Semi <- lme( resistance ~ ET * position, data = Semiconductor, random = ~ 1 | Grp) where Grp in the Semiconductor dataset is defined as ET*Wafer. Is it possible to specify the grouping directly some way, e.g. like fm1Semi <- lme( resistance ~ ET * position, data =

Dear r-help, With lme, is there a way to specify multiple fixed factors under one level of grouping? For example, for a single fixed factor, I use the following: fm1.lme <- lme(fixed=resp ~ fact1, random=~1|subj/fact1, data=mydata) I would like to have multiple factors under subj, like the following for a two-way design, but I get an error: fm2.lme <- lme(fixed=resp ~ fact1*fact2,

Hi, df <- read.table("data.txt", header=T); library(nls); fm <- nls(y ~ a*(x+d)^(-b), df, start=list(a=max(df->y,na.rm=T)/2,b=1,d=0)); I was using the following routine which was giving Singular Gradient, Error in numericDeriv(form[[3]], names(ind), env) : Missing value or an Infinity produced when evaluating the model errors. I also tried the

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Dear All, I am analysing some data for a colleague (not my data, gotta be published so I cannot divulge). My response variable is the number of matings observed per day for some fruitlies. My factors are: Day: the observations were taken on 9 days Regime: 3 selection regimes Line: 3 replicates per selection regime. I have 81 observations in total The lines are coded A to I, so I do not need

R developers, 2 things: a bug in model.frame and a question about setup of generic functions. I don't understand the following behavior for generic functions: Suppose I'm working with the cats data in the MASS library and I want to create a formula object to model Hwt on Sex: # This works: > formula(Hwt ~ Sex) Hwt ~ Sex # But the following does not: > formula(Hwt ~ Sex,

I have installed the binary packages for Debian GNU/Linux release 2.3 (woody) in the U.S. mirror of the CRAN archive. They should propagate to the main CRAN archive within a day and to the other mirrors within two days. These packages have been compiled with gcc-3.0 and g77-3.0. I believe the testing distribution currently provides only a snapshot of gcc-3.0, not the latest released version, so

I have installed the binary packages for Debian GNU/Linux release 2.3 (woody) in the U.S. mirror of the CRAN archive. They should propagate to the main CRAN archive within a day and to the other mirrors within two days. These packages have been compiled with gcc-3.0 and g77-3.0. I believe the testing distribution currently provides only a snapshot of gcc-3.0, not the latest released version, so

Hi, I have the following linear mixed model: y(g,i,j,k,l)=u + L(g) + T(i) + D(j) + S(k) + (TS)(i,k) + error(g,i,j,k,l) where S(k) and the combined effect (TS)(i,k) are random effects whereas the rest are fixed effects. How do I specifiy the random part of the model formula in lme(), especially concerning the combined effect (TS)? Moreover, when I run the model as a fixed effect model I get

Dear Friends, I am writing to seek any help on "lme" error message. I am using lme to do Mixed-model linear regression. I use my simulated data. However, sometimes, I get the following error message, which I do not understand. "Error in solve.default(pdMatrix(a, fact=TRUE)): system is computationally sigular" I would appreciate any help about it. Thanks a lot Jingyuan Fu

Hello, I am trying to do some parallel programming with R. I programmed with C and MPI before. I heard that there is a package called Rmpi and one called snow. What is the difference? I know the administrator installed snow in our system so I wonder if this mean there is Rmpi in it. I believe the Rmpi will have similar functions than MPI that is why I am specially interested in it. Thanks

Hi, everyone, I have a question on using lme on a mixed effects model with nested error structure. After applying lme to the data, and put the outcome in, say TR.lme. I can extract the fixed effects by TR.lme$coef$fixed. However, when I use TR.lme$coef$random.effects, it does not give the variance components that I need, but a vector of values at each nested level. What I want are the