similar to: Problems with lme random slope+intercept model

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Following lme model runs fine in general under R.2.1.1 but only for 9 out of my 11 response variables under R.2.2.0. model for one of my response variables: lme(Yresp~F1fix,random=list(const=pdBlocked(list(~F2mix-1,~Ass:F1fix-1,~F3mix-1,~F1fix:F3mix-1,~F2mix:F3mix-1),pdClass="pdIdent"))) Yresp is my response variable, F1fix is a fixed effect factor whereas F2mix and F3mix are random

To those who might be interested -- following is the solution to my previous post regarding reproducing output from SAS Proc Mixed for a two-factor crossed random effects ANOVA model. I am graciously endebted to the kind replys from two statisticians for this solution whose names I will refrain from mentioning for the sake of privacy. I hope this helps someone?! -- Phil Turk > hw7 <-

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Hello, I work with a mixed model with 4 predictor variables Time, Size, Charge, Density and Size, Charge, Density are factors, all with two levels. Hence I want to put their interactions with Time into the model. But, I have two data sets (Replication 1 and 2) and I want that Replication is random effect. Here is my code: knots <- default.knots(Time) z <- outer(Time, knots, "-")

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

I'm trying to run an SEM, but I keep getting the following error message. In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I have 4 latent variables (plant, AMF, abiotic, and soilAgg) with 2 or 4 indicator variables for each latent variable.My model is specified as: >

Dear all, I am working on a data set in which I have sequentially measured egg temperatures ("eggtemp") in birds incubating in different ambient temperatures ("treat", sample data set below), "id" is not replicated within treatment. id treat eggtemp 1 79 3 30.90166 2 42 3 34.94044 3 10 3 32.69945 4 206 3 36.64127 5 23 3 31.80055 6

Dear list, I am relatively new to ordinal models and have been working through the example given by Frank Harrell in the predict.lrm {Design} help All of this makes sense to me, except for the responses, i,e how do i interpret them? i would be extremely grateful if someone could explain the results? First i establish the date and model - > y <- factor(sample(1:3, 400, TRUE), 1:3,

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hello I want to batch job the calculation of many GLM-models, extract some values and store them in a file. Almost everything in the script below works (read file, extract values and write them to file) except I fail in indexing the GLM with the modelstructure it should run. Running GLM's conventionally is no problem. Conventionally a GLM is calculated as:

hello dear list! as I am currently helping someone with their statistical analysis of a count survey, I stumbled upon a very basic question upon model optimization: when fitting a model like: model<-lmer(abundance~(x+y+z)^3,family=poisson,...) in which x,y,z are continuous abiotic parameters such as po4 concentration, no2-concentration, which terms / interaction terms would you recommend

Hi I have found some postings referring to the fact that one can try and understand why a particular model is failing to solve/converge from the verbose output one can generate when fitting a nonlinear mixed model. I am trying to understand this output and have not been able to find out much: **Iteration 1 LME step: Loglik: -237.4517 , nlm iterations: 22 reStruct parameters: subjectno1

Hi all, I think I may be confused by different people/programs using the word rotation differently. Does prcomp not perform rotations by default? If I understand it correctly retx=TRUE returns ordinated data, that I can plot for individual samples (prcomp()$x: which is the scaled and centered (rotated?) data multiplied by loadings). What does it mean that the data is rotated from the

I've got a small problem. I have some observational data (environmental samples: abiotic explanatory variable and biological response) to which I've fitted both a multiple linear regression model and also a gam (mgcv) using smooths for each term. The gam clearly fits far better than the lm model based on AIC (difference in AIC ~ 8), in addition the adjusted R squared for the gam is

Dear all, Edmond Ng (http://multilevel.ioe.ac.uk/softrev/reviewsplus.pdf) provides an example to fit the mixed effects meta-analysis in Splus 6.2. The syntax is: lme(fixed=d~wks, data=meta, random=~1|study, weights=varFixed(~Vofd), control=lmeControl(sigma=1)) where d is the effect size, study is the study number, Vofd is the variance of the effect size and meta is the data frame.

Dear all, I noticed the following in the call of lme using msVerbose. fm1 <- lme(distance ~ age, data = Orthodont, control = lmeControl(msVerbose=T)) 9 318.073: -0.567886 0.152479 1.98021 10 318.073: -0.567191 0.152472 1.98009 11 318.073: -0.567208 0.152473 1.98010 fm2 <- lme(distance ~ age, random =~age, data = Orthodont,

Dear all, We are trying to do a meta regression in R using the lme function. The reason for doing this with lme function is that we have covariates and studies within references. In S-Plus this is possible by using the following command: lme(outcome ~ covars, random = ~1 | reference/study, weights = varFixed(~var.outcome), data = mydata, control = lmeControl(sigma = 1)) This means that the

Dear R developers, is there a preferred format or strategy for making a patch to contribute to a package that is maintained by R-core? Berwin Turlach and I have written a very minor extension to lmeControl to allow it to pass an argument to nlminb for the maximum number of evaluations of the objective function. I've edited the nlme/R/lme.R and nlme/man/lmeControl.Rd files. I can diff the

Hi, I am a graduate student applying published R scripts to compare the classification accuracy of 2 predictive models, one built using discriminant function analysis and one using random forests (webpage link for these scripts is provided below). The purpose of these models is to predict the biotic integrity of streams. Specifically, I am trying to compare the classification accuracy (i.e.,