similar to: best predictive model for mixed catagorical/continuous variables

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

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

dear R-friends, i`ve got a large dataset of vegetation-samples with about 500 variables(=species) in the following format: 1 spec1 1 spec23 1 spec54 1 spec63 2 spec1 2 spec2 2 spec253 2 spec300 2 spec423 3 spec20 3 spec88 3 spec121 3 spec200 3 spec450 . . this means: sample 1 (grassland) with the species (=spec) 1, 23, 54, 63 is it possible to get a following data-structure for further

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

I've written a function that regularly throws the "cannot allocate vector of size X Kb" error, since it contains a loop that creates large numbers of big distance matrices. I'd be very grateful for any simple advice on how to reduce the memory demands of my function. Besides increasing memory.size to the maximum available, I've tried reducing my "dist" objects to 3

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 all R experts, how do I calculate the reliability between the two groups using the ICCs? I'll appreciate your reply, Thanks Sincerely, Supreet kaur, Biomedical research engineer, Nationwide Childrens Hospital, Columbus, OH (614)355-3509 [[alternative HTML version deleted]]

First I would like to say thank you for taking the time to read it.Here is my problem. I am running a lmer analysis for binary longitudinal (repeated measures) data. Basically, I manipulated fruits and vegetation to two levels each(present and absent) and I am trying to access how these factors affect mice foraging behavior. The design consist of 12 plots, divided in 3 blocks. So each block

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))

Hello all, I am going slightly mad trying to create a table for running co-correspondence analysis. What I have is seed bank and vegetation data, and my aim is to see if the vegetation found in a site (containing several seed bank samples) can predict the composition of a seed bank sample within that site. So for this I need two tables with matching rows. I have created an empty matrix,

Hi, This is a question regarding technique rather than an R specific issue. I have been asked to evaluate a 30+ year long term continuous survey of bird presence/absence data that has an associated ocular estimate of the vegetation community percent coverage. The data are organized by subpopulations (5), and by year ( 1991 - present). We are interested in gaining understanding on whether bird

Hi R listers, I am trying to group distances of nests to the vegetation into classes that are define by (0-5m, 6-10m, 11-15m, 16-20m, 21-25m, 26-30m, 31-35m, 36-40m, 41-45m, 46-50m, 51-55m, 56-60m). Each row is a nest and all the distances to the vegetation is in a column. In plyr, I have tried - below script but I think I am going about this the wrong way and am not successful. #Veg index

Hi, everyone, I haven't found anything similar in the forum, so here's my problem (I'm no expert in R nor statistics): I have a data set of 59.000 cases with 9 variables each (fractional coverage of 9 different plant types, such as deciduous broad-leaved temperate trees or evergreen tropical trees etc.), which was generated by a vegetation model. In order to evaluate the quality of

Hello all, I am currently working with rpart to classify vegetation types by spectral characteristics, and am comming up with poor classifications based on the fact that I have some vegetation types that have only 15 observations, while others have over 100. I have attempted to supply prior weights to the dataset, though this does not improve the classification greatly. Could anyone supply some

Hi everyone. I am trying to add a formula to my data using the groupedData function. My experiment consists of randomized block design using fruits, vegetation and time as factors. The idea is to see if fruits, vegetation and time explain the abundance of mice. I am using tree density as a covariate. So I tried to fit the following structure to my data. >

Hi, I hope someone knows the answer to this or has a real good reference about it (I am using Legendre & Legendre, Numerical Ecology, 1998).... My data is a data.frame with locations as rows and vegetation assemblages / species as columns. I've done a PCA, a correspondance analysis (CA) using ca in ca package and a detrended correspondance analysis (DCA) using decorana from vegan package.

Hi, For my master thesis I have 24 micro-plots on which I did measurements during 3 months. The measurements were: - Rainfall and runoff events throughout 3monts (runoff being dependant on the rainfall, a coefficient (%) has been made per rainfall event and per 3 months) - Soil texture (3 different textures were differentiated) - Slope (3 classes of slopes) - Stoniness (one time measurement)

Hi everybody, I know this might be very off topic and it took me quite a while to up my courage to post this?. But I remember a thread some time ago about how we can find the packages we need to do specific tasks in R if we don?t know before hand which ones actually do it. Now all the packages are listed alphabetically on the web site. Since I am not very advanced in writing my own functions I

Dear R users I am currently investigating time series analysis using an irregular time series. Our study is looking at vegetation change in areas of alien vegetation growth after clearing events. The irregular time series is sourced from Landsat ETM+ data, over a six year period I have 38 scenes. For certain periods I have monthly data while for others, images are up to three months apart. So far