similar to: Possible bug?

Full_Name: W.B.Kloke Version: 1.8.0 OS: FreeBSD-4.7 Submission from: (NULL) (195.253.22.63) I found a parameter evaluation inconsistency in NLME package. I tried to use gls() inside a function, and I wanted use gls() for different subsets of a data frame: prgls <- function(name){ gls( log10(Y)~(cond-1)+(cond-1):t ,pr,subset=subject==name)} Applying this function with a string as parameter

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

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Hi all, My apologies for a very simple question. I just downloaded R 2.6.0. I want to bring in all of the objects from 2.5.0 that I see when I type ls(). I have no idea how to do that. Thanks in advance. David -- ======================================================================= David Kaplan, Ph.D. Professor Department of Educational Psychology University of Wisconsin - Madison

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

Hi all, I'm looking for a contributed package that can provide a detailed account of missing data patterns and perhaps also provide imputation procedures, such as mean imputation or hot deck imputation and the like. Is there anything out there? Thanks in advance, David -- =========================================================================== David Kaplan, Ph.D. Professor

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

Hi all, I'm looking for R packages that estimate multivariate time-series models or vector-autoregression (VAR) time-series models. Thanks David -- =========================================================================== David Kaplan, Ph.D. Professor Department of Educational Psychology University of Wisconsin - Madison Educational Sciences, Room, 1061 1025 W. Johnson Street Madison,

Greetings, I'm interested in generating data from various bivariate or mulitivariate distributions (e.g. gamma, t, etc), where I can specify the parameter values, including the correlations among the variables. I haven't been able to dig anything up on the faq, but I probably missed something. A nudge in the right direction would be appreciated. David --

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

Greetings all, I have two questions. I have a data set that is arranged in the example below. I wish to obtain a plot of the performance of each ID over Year on v1. It's not clear how I set this up? ID Year V1 1 1980 1 1 1981 2 1 1982 6 1 1983 4 2 1980 5 2 1981 5 2 1982 5 2 1983 6 Also,I would like to transpose the data to have the

Greetings all, I have two logistic plots coming from two calls to plogis. The code is .x <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x, location=0, scale=1), xlab="x", ylab="Density", main="Logistic Distribution: location = 0, scale = 1", type="l") abline(h=0, col="gray") .y <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x,

Greetings I have a very simple question that I have not been able to solve by reading the manual. When I produce a stripchart with two straight columns of dots representing individual observations, one representing one group of subjects and the other representing another, the columns wind up at the far left and far right sides of the plot, and I'd like them to be closer together, about as far

What is a simple way to convert a factor into a matrix of dummy variables? fm<-lm(y~f) where f is a factor takes care of this in the estimation. I'd like to save the result of expanding f into a matrix for later use. Thanks. Charles -- Charles H. Franklin Professor, Political Science University of Wisconsin, Madison franklin at polisci.wisc.edu chfrankl at wisc.edu

//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, 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

I have a tiny R script performing two tasks, the first one of which may contain error. mammon(12)% cat z.R version options(show.error.messages = FALSE) try(b <- log("foo")) ## task 1 1 + 2 ## task 2 Running in batch mode from a solaries machine, the second task never got started; see below: mammon(13)% R BATCH --vanilla -q z.R mammon(14)% cat z.Rout >

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