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References: [ +from:msc0235@geo.ed.ac.uk: 8 ]

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1. Predict.lme "Number of columns of x should be the same as number of rows of y" (score: 1): Author: <msc0235@geo.ed.ac.uk>; Date: Tue, 3 Sep 2002 19:12:23 +0100; Dear all, I am trying to predict response values from a dataset with over 22,000 rows, using 4 explanatory variables.The command I am using is: predicting.estate.nymphs <- predict.lme(estateall.nymph; /archives/html/s-news/2002-09/msg00007.html (7,623 bytes)
2. glmmPQL() and categorical variables (score: 1): Author: msc0235@geo.ed.ac.uk; Date: Thu, 5 Sep 2002 11:06:10 +0100; Dear all, I am a little confused about Splus recoding categorical variables using contrasts - I have read the help files about it, but I do not know which default is used when analysing data with the; /archives/html/s-news/2002-09/msg00017.html (8,818 bytes)
3. Wilcoxon test (score: 1): Author: msc0235@geo.ed.ac.uk; Date: Fri, 2 Aug 2002 14:14:07 +0100; Dear all, I am trying to compare predicted and observed mean parasite data, using Wilcoxon test as the data is not normally distributed. I run tests from a script, and they are of the following forma; /archives/html/s-news/2002-08/msg00011.html (7,272 bytes)
4. glmmPQL() and explained deviance... ? (score: 1): Author: msc0235@geo.ed.ac.uk; Date: Fri, 16 Aug 2002 16:48:09 +0100; Dear all, I would like to calculate the percentage explained deviance for models obtained with the glmmPQL() procedure, but I do not get any Null deviance or residual deviance in the glmmPQL() output; /archives/html/s-news/2002-08/msg00110.html (6,806 bytes)
5. explained deviance (score: 1): Author: msc0235@geo.ed.ac.uk; Date: Thu, 22 Aug 2002 12:37:19 +0100; Dear all, I have models from glm.nb procedures from which I can extract the %explained deviance of the response studied from the null deviance and residual deviance given in the summary (model.object; /archives/html/s-news/2002-08/msg00132.html (6,631 bytes)
6. "Error in theta.ml: ..." ? ? ? (score: 1): Author: <msc0235@geo.ed.ac.uk>; Date: Sat, 24 Aug 2002 15:09:16 +0100; Dear all, does any one know what the error message below means - and how I could fix that ? the data is a bit sparse, but I thought the glm.nb should take care of that. The model I was trying is: adu; /archives/html/s-news/2002-08/msg00159.html (6,865 bytes)
7. glm.nb, t-values and p-values (score: 1): Author: <msc0235@geo.ed.ac.uk>; Date: Tue, 27 Aug 2002 11:50:14 +0100; Dear all, apologies if this is a very trivial question :- I have been using the glm.nb procedure, a typical output of which is shown below. the model returns t-values for each explanatory variable, b; /archives/html/s-news/2002-08/msg00171.html (8,388 bytes)
8. mixed effect models (score: 1): Author: msc0235@geo.ed.ac.uk; Date: Fri, 21 Jun 2002 16:53:52 +0100; Dear all, I would like to analyse a dataset of parasite abundance - this follows a negative binomial distribution, and comes in a hierarchical fomat (repeated parasite samples(10 samples) per habitat; /archives/html/s-news/2002-06/msg00189.html (6,674 bytes)

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