similar to: Skipping Results with Errors in a Loop

I'm fitting a mixed effects model: fit.1 <- lme(y~x,random=~1|id,data=df) There are two different observations for each id for both x and y. When I use plot(fit.1), there is a strong increasing linear trend in the residuals versus the fitted values (with no outliers). This also happens if I use random=~x|id. Am I specifying something incorrectly? Rick B.

I'm using groupedData from nlme. I set up a groupedData data.frame with outer=~group1. When I try to plot with outer=TRUE, I get "subscript out of bounds." This happens most of the time. When it works, I get spaghetti-type plots for comparing groups. But I don't understand why it doesn't usually work. > longa.mod.1.gd <- groupedData(mod1.logit~time|

Full_Name: Antonio, Fabio Di Narzo Version: 2.6.1 OS: linux Submission from: (NULL) (213.140.16.187) In 'outer' man page, there is no mention of the Vectorize function. Moreover, I think it isn't underlined enough that the FUN argument to 'outer' must be a vectorized function (doc speaks about a function which has to 'operate elementwise'). A cross-reference from

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

Dear all, When using gdb to debug my C code, I use R_PV to show the content of SEXP variables: SEXP sexp; // it is a data.frame SEXP colNames = getAttrib(sexp, R_NameSymbol); A strange thing is that after halting the program: (gdb) p R_PV(colNames) does not show the content of colNames. I am positive my code is right because if I insert "PrintValue(colNames);" in the c code,

Hi guys. I recently stumbled on an unexpected behavior of R when using functions created in a loop. The problem is silly enough to me that I had hard time choosing a good mail subject, not talking about searching in the archives... After some experiments, I trimmed down the following minimal reproducible example: ####### makeF <- function(i) function() i fList <- list(makeF(1), makeF(2))

Hi all. I have unexpected reshape results on datasets with certain variable names. Here a reproducible example: d <- matrix(seq_len(7*7), 1, 7*7) vnames <- c('acc','ppeGross','CF','ROA','DeltaSales','invTA','DeltaRevDeltaRec') varying <- unlist(lapply(vnames, paste, 1:7, sep='.')) d <- data.frame(d) names(d) <- varying

Hi all. I'm looking for robust ways of building lagged variables in a dataset with multiple individuals. Consider a dataset with variables like the following: ## set.seed(123) d <- data.frame(id = rep(1:2, each=3), time=rep(1:3, 2), value=rnorm(6)) ## >d id time value 1 1 1 -0.56047565 2 1 2 -0.23017749 3 1 3 1.55870831 4 2 1 0.07050839 5 2 2 0.12928774 6

Hi I use the following code and it stores the results of density() in the list dr: dens <- function(run) { density( positions$X[positions$run==run], bw=3, cut=-2 ) } dr <- lapply(1:5, dens) but the results are stored in dr[[i]] and not dr[i], i.e. plot(dr[[1]]) works, but plot([1]) doesn't. Is there any way that I can store them in dr[i]? Thanks a lot, Rainer -- Rainer M. Krug,

Thompson's Manual to Accompany Agresti's book refers to a package named "repeated". It's not on CRAN from what I can see. I have seen rpm's for it. Where is the best place to download this package? Rick B.

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Where I work a lot of people end up using Excel spreadsheets for storing data. This has limitations and maybe some less than obvious problems. I'd like to recommend a uniform way for storing and archiving data collected in the department. Most of the data could be stored in simple csv type files but it would be nice to have something that stores more information about the variables and units.

Dear listers, a few days ago I asked a question about a problem I had with a C function programmed by myself to be called from R. Thanks to your help, I have been able to look a bit further into it, so now I can be a bit more specific when telling what happens. The fact is that, when my C function finishes its execution (after calling it via the ".C" interface), R seems ok but

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

I'm trying to fit a generalized mixed effects model to a data set where each subject has paired categorical responses y (so I'm trying to use a binomial logit link). There are about 183 observations and one explanatory factor x. I'm trying to fit something like: (lmer(y~x+(1|subject))) I also tried fitting the same type of model using glmmPQL from MASS. In both cases, I get a

Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS. A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation

Are there any packages that help with statistical analysis in situations where fractal geometry is relevant? Perhaps something that supports computation of fractal related statistics, such as the Hurst exponent or fractal dimension? Or perhaps which support obvious tasks such as taking samples from a dataset at different levels of granularity (such as sampling spatial data at cm, m, km

Are there any packages in R that are suitable for doing time series forecasting using neural networks? I have looked in the nnet package and neural package and they both seem geared towards classification. thanks, Spencer [[alternative HTML version deleted]]

Hi All, Does anybody know if there is a simple way to draw numbers from an empirical distribution? I know that I can plot the empirical cumulative distribution function this easy: plot(ecdf(x)) Now I want to pick a number between 0 and 1 and go back to domain of x. Sounds simple to me. Any suggestion? Thank you, Your culprit (everybody needs a culprit) -- View this message in context: