similar to: R crashing inconsistently within for loops

Hi everyone, I ran a binomial GAM consisting of a tensor product of two continuous variables, a continuous parametric term and crossed random intercepts on a data set with 13,042 rows. When trying to plot the tensor product with vis.gam(), I get the following error message: Error in persp.default(m1, m2, z, col = col, zlim = c(min.z, max.z), xlab = view[1], : invalid 'z' limits In

Hello. I'm trying to analyze data, which is looking at the relationship between temperature and survival for fish (from fertilization to emergence). Looking at the raw data, there appears to be a bell shaped relationship. Ordinarily for survival data, I would run a generalized linear model (because the data has a binomial error structure). However, I am thinking that running a generalized

Dear List, This may be related to this email thread initiated by Ben Bolker last month: https://stat.ethz.ch/pipermail/r-devel/2011-July/061630.html In answering this Question on StackOverflow http://stackoverflow.com/q/7195628/429846 I noticed that `methods()` was not listing some S3 methods for `plot()` provided by the mgcv package. At the time I wanted to check the development version of R as

Howdy, I was playing around with the malloc/free D script provided by Philip Beevers: http://www.opensolaris.org/jive/thread.jspa?threadID=4224&tstart=15 And decided to change the free:entry probe from: pid24169::free:entry / sz[arg0] / { printf("Freeing %p (size %d)\n", arg0, sz[arg0]); sz[arg0] = 0; } to: pid24169::free:entry / ! sz[arg0] / { printf("[ *

Hi, I'm having difficulty configuring Printing with Samba 2.2.4 and Windows 2000 clients. The samba installations is running as a PDC against LDAP. All the sharing of network file systems seem to be working perfectly.. I have a share [printers] path = /usr/spool/samba guest ok = yes printable = yes browsable = no guest ok = yes writable =

Hello, what does this error message indicate and how do I avoid this? (sample code below) Thank you. -Tosh #read in the data table co<-read.table("co.txt",header=T,as.is=T) for (i in 1:3){ paste("logco",i, sep="")<-log(co$co[co$day==i]) } Gives the error: Error: Target of assignment expands to non-language object

Hi all, Reading the wikipedia page on R, I stumbled across the following: http://fluff.info/blog/arch/00000172.htm It does seem interesting that the C execution is that much slower from R than from a native C program. Could any of the more technically knowledgeable people explain why this is so? The author also have some thought-provoking opinions on R being no-good and that you should write

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

Hi, I need to correct for ar(1) behavior of my residuals of my model. I noticed that there are multiple gls models in R. I am wondering if anyone has experience in choosing between gls models. For example, how should one decide whether to use lm.gls in MASS, or gls in nlme for correcting ar(1)? Does anyone have a preference? Any advice is appreciated! Thanks, -- Tom [[alternative HTML

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

I found this paper on ANOVA on unequal error variance. Has this be incorporated to any R package? Is there any textbook that discuss the problem of ANOVA on unequal error variance in general? http://www.jstor.org/stable/2532947?cookieSet=1

Dear R users, I have used the following model: M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD, weights=varIdent(form=~ 1 | Season*Concentration)) to assess the effect of Concentration and Season on nitrogen uptake by leaves (Nblad). I accounted for the difference in variance across the factor levels by using the varIdent function. Then I wanted to perform multiple

I'd like to know why I cannot get a plot and the QQnorm in the same sheet. The commands are simple but: library(nlme) glmod1 <- gls(upfmla,correlation=corAR1(),method="ML") summary(glmod1) par(mfrow = c(2,1)) plot(glmod1, main="GLS Residuals vs. GLS Fitted") qqnorm(glmod1) No matter what (I tried different permutations of the plotting commands) the second drawing

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call:

Could someone tell me how to access the log likelihood of a poisson model? I've done the following.... <BEGIN R STUFF> freq.mod <- glm(formula = nfix ~ gls.gls + pol.gls + pol.rel + rac.gls + rac.pol + rac.rac + rac.rel + white + gls.gls.w + pol.gls.w + pol.rel.w + rac.gls.w + rac.pol.w + rac.rac.w + rac.rac.w + rac.rel.w, family = poisson, data = Complex2.freq, offset = lnoffset)

Hi I have a huge array with series of data. For each cell in the array I fit a linear model, either using lm() or gls() with lm() there is no problem, but with gls() I get an error: Error in glsEstimate(glsSt, control = glsEstControl) : computed gls fit is singular, rank 2 as soon as there are data like this: > y1 <- c(0,0,0,0) > x1 <- c(0,1,1.3,0) > gls(y1~x1)