similar to: Error message: attempt to set rownames on object with no dimensions

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Dear princely R developers to whom I owe so much of my daily productivity level, I am wondering how indexing by row and column names are implemented. That is, when I have a matrix with named rows (or columns) and I then index into that matrix using the string names (rather than integers), how does R scan through the list of names in order to figure out which row/column to use? My current

Dear R wizards: I am trying (finally) to build a function that might be useful to others. In particular, I want to create a summary.lme (extended lm) method that [a] adds normalized coefficients and [b] white heteroskedasticity adjusted se's and T's. I believe I already know how to do the programming to do these two, at least in simple unweighted cases. Now my challenges are just [1]

Hi, I am trying to run a linear mixed effect model on data. I have 17 longitudinal subjects and 36 single subjects, and this is the code I'm using (below). So, INDEX1 is the column with brain volumns, and the predictors are gort and age, by time ID (time they were seen). I believe my data is set up the right way, but when I run it, I get DF for Intercept is 49, and DF for slope is 13?

Dear R experts, I would like to get an R^2 - like value for a multilevel regression using lme. I followed an archived suggestion by José Pinheiro to use the squared correlation between fitted and observed values, i.e., (cor(fitted(model.lme), getResponse(model.lme))^2 but getResponse returns the error message Error in val[, level] : incorrect number of dimensions The same happens with

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

Dear All, I wish to model random effects that have known between-group covariance structure using the lme() function from library nlme. However, I have yet to get even a simple example to work. No doubt this is because I am confusing my syntax, but I would appreciate any guidance as to how. I have studied Pinheiro & Bates carefully (though it's always possible I've missed

I want to pass a predefined string ww ("fa*fb+fc") to function lme so that I can run > lme(y ~ fa*fb+fc, random = ~1|subj, model) I've tried something like > lme(y ~ paste(ww), random = ~1|subj, model) and > lme(y ~ sprintf(ww), random = ~1|subj, model) but both give me the following error: Error in model.frame(formula, rownames, variables, varnames, extras,

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)+

Dear R - Users I have some problems fitting a linear mixed effects model using the lme function (nlme library). A sample data is as shown at the bottom of this mail. I fit my linear mixed model using the following R code: bmr <-lme (outcome~ -1 + as.factor(endpoint)+ as.factor(endpoint):trt, data=datt, random=~-1 + as.factor(endpoint) + as.factor(endpoint):trt|as.factor(Trial),

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

Hi, I'm experimenting with random effect natural splines, and I've encountered an odd problem. library(nlme); library(splines) a <- data.frame(x = 1:10, y = 1:10 + runif(10, min=-3, max=3), c = rep(c(1,2),5)) df <- 10 lml <- lmList(y ~ ns(x,df=df) | c, a) Error in df - 1 : non-numeric argument to binary operator I presume this is because the formula isn't evaluated

Hi, I was wondering if there is a way to figure out why in SAS random beta coefficients are 0 vs. in R the beta-s are non zero. The variables of the data are nidl, time, and sub (for subject). Time and nidl are continuous variables. I am applying random coefficients model. Any input is greatly appreciated, Thanks, Aldi 1. mixed model in SAS: ====================== ods output SolutionR =

Hi List I'm running a series of mixed models using lme, and I wonder if there is a way to sort them by AIC prior to testing using anova (lme1,lme2,lme3,....lme7) other than by hand. My current output looks like this. anova (lme.T97NULL.ml,lme.T97FULL.ml,lme.T97NOINT.ml,lme.T972way.ml,lme.T97fc. ml, lme.T97ns.ml, lme.T97min.ml) Model df AIC BIC logLik

Thanks. I wonder if there is a general way of extracting var(u) and var(e), which would be needed to simulate u and e. Clearly, one can get the estimated parameters, but is there a clever way of 'setting up' the matrices?? Best S??ren On 6/19/05, S??ren H??jsgaard <Soren.Hojsgaard at agrsci.dk> wrote: > I would like to draw a sample from a linear mixed model y=Xb+Zu+e which has

When I use a model fit with LME, I get an error if I try to use "predict" with a dataset consisting of a single line. For example, using this data: > simpledata Y t D ID 1 -1.464740870 1 0 1 2 1.222911373 2 0 1 3 -0.605996798 3 0 1 4 0.155692707 4 0 1 5 3.849619772 1 0 2 6 4.289213902 2 0 2 7 2.369407737 3 0 2 8 2.249052533 4 0 2 9 0.920044316 1

Hello R Users, I am investigating the basic use of the LME function, using the following example; Response is Weight, covariate is Age, random factor is Genotype model.lme <- lme (Weight~Age, random=~ 1|Genotype) After summary(model.lme), I find that the estimate of Age is 0.098 with p=0.758. I am comparing the above model with the AOV function; model.aov <- aov (Weight~Age + Genotype)

Greetings all The help file for GAMM in mgcv indicates that the log likelihood for a GAMM reported using summary(my.gamm$lme) (as an example) is not correct. However, in a past R-help post (included below), there is some indication that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is valid. How can I tell if anova.lme results are meaningful (are AIC, BIC, and logLik

Hi I am comparing four different linear mixed effect models, derived from updating the original one. To compare these, I want to use anova(). I therefore do the following (not reproducible - just to illustration purpose!): dat <- loadSPECIES(SPECIES) subs <- expression(dead==FALSE & recTreat==FALSE) feff <- noBefore~pHarv*year # fixed effect in the model reff <-