similar to: Rows function in nlme package

Dear Douglas Bates. I just downloaded the compiled version (I'm a poor Windows devil, not yet having found the time to move to a more advanced platform...) from NT- the files are dated 30.5-1999 so they are not old - and the problem persisted....wonder what I did wrong ? R : Copyright 1999, The R Development Core Team Version 0.64.0 Patched (May 3, 1999) R is free software and comes with

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

Hi, I'm trying to compare models, one of which has all parameters fixed using offsets. The log-likelihoods seem reasonble in all cases except the model in which there are no free parameters (model3 in the toy example below). Any help would be appreciated. Cheers, Jarrod x<-rnorm(100) y<-rnorm(100, 1+x) model1<-lm(y~x) logLik(model1) sum(dnorm(y, predict(model1),

Dear R users, i'm using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

Hi (R 2.13.1, OSX 10.6.8) I am trying to use predict.rma with continuous and categorical variables. The argument newmods in predict.rma seems to handle coviariates, but appears to falter on factors. While I realise that the coefficients for factors provide the answers, the goal is to eventually use predict.rma with ANCOVA type model with an interaction. Here is a self contained example

I've run into persistent problems with OpenBUGS crashing when using BRugs .53 and .71, and am hoping someone has suggestions. There is obviously something unusual going on in my environment, but I'm at a loss as to where to begin to try to solve it. In a nutshell, what happens is that, as soon as I call "modelCheck()" in BRugs, it gets an error or crashes ... but only some of

Dear R users, Assume I have three models with the following AIC values: model AIC df model1 -10 2 model2 -12 5 model3 -11 2 Obviously, model2 would be preferred, but it "wastes" 5 df compared to the other models. Would it be allowed to select model3 instead, simply because it uses up less df and the delta-AIC between model2 and model3 is just 1? Many thanks for any

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Hi, I am trying to install the nlme package, but during installation I get the message <snip> pdSymm text html latex example plot.ACF text html latex example missing link(s): xyplot plot.Variogram text html latex example missing link(s): xyplot plot.augPred

I have a question about how to compare a GLM with a GAM model using anova function. A GLM is performed for example: model1 <-glm(formula = exitus ~ age+gender+diabetes, family = "binomial", na.action = na.exclude) A second nested model could be: model2 <-glm(formula = exitus ~ age+gender, family = "binomial", na.action = na.exclude) To compare these two GLM

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list

Dear, I am trying to get a prediction of my GAM on a response type. So that I eventually get plots with the correct values on my ylab. I have been able to get some of my GAM's working with the example shown below: * model1<-gam(nsdall ~ s(jdaylitr2), data=datansd) newd1 <- data.frame(jdaylitr2=(244:304)) pred1 <- predict.gam(model1,newd1,type="response")* The problem I am

Hi all, I'm trying to fit a nonlinear regression by "nlrob": model3=nlrob(y~a1*x^a2,data=transient,psi=psi.bisquare, start=list(a1=0.02,a2=0.7),maxit=1000) However an error message keeps popping up saying that the function psi.bisquare doesn't exist. I also tried psi.huber, which is supposed to be the default for nlrob: model3=nlrob(y~a1*x^a2,data=transient,psi=psi.huber,

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

I am using anova.lm to compare 3 linear models. Model 1 has 1 variable, model 2 has 2 variables and model 3 has 3 variables. All models are fitted to the same data set. anova.lm(model1,model2) gives me: Res.Df RSS Df Sum of Sq F Pr(>F) 1 135 245.38 2 134 184.36 1 61.022 44.354 6.467e-10 *** anova.lm(model1,model2,model3) gives

Hi, I ran the following model using nlme: model2<-lme(log(malrat1)~I(year-1982),random=~1|Continent/Country,data=wbmal10) I'm trying to run a Poisson GlMM to avoid the above transformation but I don't know how to specify the model using lmer in the lme4 library: model3<-lmer((malrat1)~I(year-1982) + ??,data=wbmal10,family=poisson) How can I introduce a random factor of the

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Hello all: Please help me with this "can't find object" issue. I'm trying to get leave-one-out predicted values for Beta-binomial regression. It may be the gamlss issue because the code seems to work when %do% is used. I have searched for similar issues, but haven't managed to figure it out. This is on Windows 10 platform. Thanks in advance, Nik #