similar to: Reg. strings and numeric data in matrix.

Dear All, I have a list of models(1000) which have variable scores from 20 different method. I would like to rank models using consensus approach based on high scores from different methods.Is there any function available in R for this purpose? I will appreciate any pointers in this regard. Thank you very much in Advance, Mallika

Hi, I am using ext3 file system. Can I know the place where journal log is stored and more details about journaling. Quick response is appreciated. Thanks in advance, Mallika. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://listman.redhat.com/archives/ext3-users/attachments/20041227/574c90bf/attachment.htm>

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf:

I have a data file with indicates pretest scores for a linguistics experiment. The data are in long form so for each of 33 subjects there are 400 rows, one for each item on the test, and there is a column called ‘Correct’ that shows ‘C’ for a correct response and ‘E’ for an incorrect response. I am trying to write a formula that will create a vector that indicates the number of correct answers

Hello I've been investigating using multi-model inference, based on calculating AIC and AIC weights, using the techniques outlined in Burnham and Anderson's (2002) book. However I notice a couple of emails in the R-help archive stating that there are errors in the technique. Are these errors associated with the particular implementation that B & A propose in their text, or is the

Greetings, I have recently come into some confusion over weather or not AIC results for comparing among models requires that they be nested. Reading Burnham & Anderson (2002) they are explicit that nested models are not required, but other respected statisticians have suggested that nesting is a pre-requisite for comparison. Could anyone who feels strongly regarding either position

Do any R functions incorporate a sample sample size correction (e.g., Burnham and Anderson 1998). Thanks, Hank Stevens Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/botany/bot/henry.html http://www.muohio.edu/ecology

Hi All, I am wondering if their is a convenient way to export the results of the TukeyHSD function to Word or Excel. I have used capture.output(tukey.contrast, file="tukey.contrast.xls") and this works, but the data are not in a table form, and so it is sort of a pain to manipulate the output. I have be unable to find a more convenient way and any assistance would be greatly

I am trying to import a series of text files generated by stimulus presentation software. The problem that I am having is that the number of rows I need to skip is not fixed (depending on subject's pretest behavior) nor is the first row of the data I want always the same (the stimuli were presented in random order). So I need to bring in the .txt file (using readLines?), look for the row

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

In a generalized linear model with k covariates, there are 2(kth power) - 1 possible models (excluding interactions). Awhile ago a posting to R-help suggested Model Selection and Multimodel Inference, 2nd ed, by Burnham and Anderson as a good source for understanding model selection. They recommend (page 71) computing AIC differences over all candidate models in the set of possible models. After

Dear all, does anyone have r code to perform model-averaging of regression parameters by Akaike weights, and/or to do all-possible-subsets lm modelling that reports parameter estimates, AICc and number of parameters for each model? I have been looking for these in the archive but found none. (I am aware that many of you would warn me against these methods advocated by Burnham and Anderson

Hello, I am comparing 2 nonlinear models one with a correlation structure. I used the gnls command in the nlme package to fit model 1 and then the update(mod1,corr=corExp(form=~lat+lon,nugget=T) to fit the same model but with a correlation structure. I am comparing models using AIC, but I am getting results that don't seem to match. I get matching results for the model with no correlation

How can I get the outlier in this boxplot of "Score" to be represented by the corresponding value in "SubNo"? score=c(6,6,7,14,5,7,6,8) SubNo=1:8 mydata=data.frame(SubNo, score) boxplot(mydata$score) Thanks! Kevin [[alternative HTML version deleted]]

Hi I have two geostatistical models from geoR. An ordinary kriging model with AIC=-148.6 and a universal kriging model with AIC=-156.7, there are 345 data points. The improvement shown by the AIC by adding a trend component to the model seems quite small given the number of data points, is there a test to see if the improvement to the model fit is significant? Thanks David

HI All, I have a long data file generated from a minimal pair test that I gave to learners of Arabic before and after a phonetic training regime. For each of thirty some subjects there are 800 rows of data, from each of 400 items at pre and posttest. For each item the subject got correct, there is a 'C' in the column 'Correct'. The line: tapply(ALLDATA$Correct,

Hello R-list. I am a "long time listener - first time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the

We've encountered the following problem on our Redhat 7.2/XFS box running samba 2.2.3 with winbind and acl support. This week, some local accounts were created on the samba box for the installation of djb's dnscache. In addition, some NT domain accounts were also created on the NT4 PDC. The samba box is configured to use the NT4 domain for authentication; none of the local accounts

I have a slight conundrum. I'm attempting to write a scrip that will take a number of objects (lm, glm, and lmer) and return AIC scores and weights. I've run into 3 problems, and was wondering if anyone had any pointers. 1) is there any convenient way to extract the name of the objects? Simply, if I have a vector of objects c(my.lm, my.lmer) and I want to get a character