similar to: Random structure of nested design in lme

All, I'm trying to analyze the results of a reciprocal transplant experiment using lme(). While I get the error-term right in aov(), in lme() it appears impossible to get as expected. I would be greatful for any help. My experiment aimed to identify whether two fixed factors (habitat type and soil type) affect the development of plants. I took soil from six random sites each of two types

Full_Name: Joerg Rauh Version: 2.5.0 OS: Windows 2000 Submission from: (NULL) (84.168.226.163) Following Michael J. Crawley "Statistical Computing" on page 9 the worms.txt is required. After downloading it from the book's supporting website, which is http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ I visually check the data against the book and they look identical. Then I do

I'm not sure if this is a bug, or if I'm doing something wrong. =20 =46rom the worms dataframe, which is at in a file called worms.txt at =20 http://www.imperial.ac.uk/bio/research/crawley/therbook <http://www.imperial.ac.uk/bio/research/mjcraw/therbook/index.htm>=20 =20 the idea is to extract a subset of the rows, sorted in declining order of worm density, with only the maximum

Would anybody be able to advise on which package would offer the best approach for producing a model able to predict the probability of species occupation based upon a range of variables, some of them catagorical (eg. ten soil types where the numbers assigned are not related to any qualitative/quantitative continuum or vegetation type) and others continuous such as field size or vegetation height.

Hi, forgive me if someone has already posted about this but I have had a look and cannot find the answer, also I am very new to R and been getting the grips with this. I have been trying to use Adonis to find out if there are significant difference between groups on data that I have analyses with NMDS, and have been struggling with getting this to work and understanding what is going on. I am

Dear R community, Below you may find the details of my model (lm11). I receive the error message "Error: cannot allocate vector of size 220979 Kb" after applying the autocorrelation function update(lm11, corr=corAR1()). lm11<-lme(Soil.temp ~ Veg*M+Veg*year, data=a, random = list(Site=pdDiag(~Veg), Plot=pdDiag(~Veg))

Dear friends of lme, After so many year with lme, I feel ashamed that I cannot get this to work. Maybe it's a syntax problem, but possibly a lack of understanding. We have growth curves of new dental bone that can well be modeled by a linear growth curve, for two different treatments and several subjects as random parameter. By definition, newbone is zero at t=0, so I tried to force the

Hi group, I'm trying to do a Tukey test to compare the means of a factor ("treatment") with three levels in an lme model that also contains the factors "site" and "time": model = response ~ treatment * (site + time) When I enter this model in csimtest, it takes all but the main factor "treatment" as covariables, not as factors (see below). Is it

Hi All, I am frustrated by mixed-effects model! I have searched the web for hours, and found lots on the nested anova, but nothing useful on my specific case, which is: a random factor (C) is nested within one of the fixed-factors (A), and a second fixed factor (B) is crossed with the first fixed factor: C/A A B A x B My question: I have a functioning model using the aov command (see

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

Hi all, I'm using R 2.0.1. for Windows to analyze the influence of following factors on response Y: A (four levels) B (three levels) C (two levels) D (29 levels) with E (four replicates) The dataset looks like this: A B C D E Y 0 1 1 1 1 491.9 0 1 1 1 2 618.7 0 1 1 1 3 448.2 0 1 1 1 4 632.9 250 1 1 1 1 92.4 250 1 1 1 2 117 250 1 1 1 3 35.5 250 1 1 1 4 102.7 500 1 1 1 1 47 500 1 1 1 2 57.4

Hi Helpers: I have a factor object that has 314k entries of 39 land cover types. (This object can be coerced to characters neatly should that be easier to work with.) > length(foo) [1] 314482 > foo[1:10] [1] Montane Chaparral Barren Red Fir Red Fir [5] Red Fir Red Fir Red Fir Red Fir [9] Red Fir Red Fir 39 Levels:

Hello group, For my PhD I'm working on a spatial sampling grid. I do have two data sets which I'd like to compare using cross-correlograms or cross-variograms. Is this an option in one of the R-packages? I've been searching the R-help archive and the available package-documentations, but I can't find how to do this. Thanks in advance, Ren?.

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

Hello I am trying to fit a REML to some soil mineral data which has been collected over the time period 1999 - 2004. I want to know if the 19 different treatments imposed, differ in terms of their soil mineral content. A tree model of the data has shown differences between the treatments can be attributed to the Magnesium, Potassium and organic matter content of the soil, with Magnesium being the

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is: