search for: burnham

...'s with characters (strings) data? How do I get this type of data from a flat file. Thanks very much, mallika ____________________________________________________________________________ Mallika Veeramalai, Ph.D., Postdoctoral Associate, Bioinformatics & Systems Biology, Burnham Institute for Medical Research, La Jolla, CA 92037, USA. phone : +1 858 646 3100 ext: 3627 Fax : +1 858 795 5249 Web : http://bioinformatics.burnham.org/~mallika/ Email : mallikav at burnham.org (or) kaaviyam at gmail.com

...vailable in R for this purpose? I will appreciate any pointers in this regard. Thank you very much in Advance, Mallika *~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~* Mallika Veeramalai, PhD, Postdoctoral Associate, Bioinformatics & Systems Biology, Prof. Adam Godzik Lab, Burnham Institute for Medical Research, La Jolla, San Diego, CA 92037, US. phone : +1 858 646 3100 ext: 3627 (work) Fax : +1 858 795 5249 Web : http://bioinformatics.burnham.org/~mallika/ Email : mallikav at burnham.org kaaviyam at gmail.com *~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...

...ng: nrow(pretestdata[(pretestdata$Subject=="1" & pretestdata$Correct=="C"),]) gives the number of correct responses for subject 1, but I would like a vector that indicates the number correct for each of 33 subjects. Thank you sincerely for your assistance. Kevin Burnham [[alternative HTML version deleted]]

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: http://www.springer-ny.com/supplements/0387953647/Preface_Contents_and_Chapter_8.pdf Lebreton, J-D et al. (1992) Modeling Survival and Testi...

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 whole approach flawed in some way? Many thanks...

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 post their arguments for or against nested models and AIC?...

...\tPressed\t(any response)\tC\t25860\t\t\t\t\t" I would also like it to ignore the row: [173] "Main Group\t1000\tBreak\tBreak\ tpause3\t\t \tPressed\t(any response)\tC\t47610\t\t\t\t\t" which will always be the same number of rows after the "Begin Main" row. Thanks, Kevin Burnham [[alternative HTML version deleted]]

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

...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 appreciated. Thanks, Kevin Burnham [[alternative HTML version deleted]]

...n. Robust standard errors are easily calculated by block-bootstrapping the data using animal as a cluster with the Design library, however I havent found a satisfactory solution for model selection. A couple options are: 1. Using QAIC where the deviance is divided by a variance inflation factor (Burnham & Anderson). However, this VIF can vary greatly depending on the data set and the set of covariates used in the global model. 2. Manual forward stepwise regression using both changes in deviance and robust p-values for the beta-coefficients. I have been looking for a solution to this problem...

...e between AIC and the lowest AIC value; w_i, the Akaike weights; AICc, the second order Akaike criterion; deltAICc, the difference between AICc and the lowest AICc value; w_ic, the AICc weights } \item{models}{the list of models} } \references{ Anderson, D.R., Link, W.A., Johnson, D.H. and Burnham, K.P. (2001). Suggestions for presenting the results of data analyses. Journal of Wildlife Management, 65, 373-378; Burnham, K.P. and Anderson, D.R. (2002) Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach, 2nd edn., Springer-Verlag, New York. 353 pp } \autho...

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

...rameters 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 2002, please do not.) Thank you, VB ------------------------------------------------------------------------------- [freemail] extra 1GB-os postafi??kkal, ??nnek m??r van? http://freemail.hu

...something wrong????? Information for model 1 rss=sum(resid(mod1)^2)=1318.645 var=rss/n=1.654511 n=797 number of parameters (K) = 10 (this does include the variance estimate) if I use logLik(mod1) I get -1331.541 with 10 d.f. if I use the formula: (-.5*n*log(var))-(797/2*log(2*pi))-(n/2) from Burnham and Anderson 2002 page 12, I get the same result as above: -1331.541 Information for model 2 with a correlation structure corr=corExp(form=~lat+lon,nugget=T) rss=1338.92 n=797 var=rss/n=1.679950 K=12 logLik(mod2)=-1313.65 with 12 d.f. If I use the equation from Burnham and Anderson I get:...

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 looking at the help page for stepAIC and checking MASS, I am not certain which AICs are estimated by stepAIC. Comments...

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 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,

...a simple solution: "we calculated QAICc adjusting for overdispersion by dividing the residual deviance (i.e. -2 loglikelihood) with the overdispersion parameter calculated from the most complex model as the sum of squares Pearson residuals divided by the number of degrees of freedom (Burnham & Anderson, 2002). " - Mystrud et al. 2007. Animal Conservation. 10:77-87. My question is: Will this calculation be valid with the residual deviance returned by the glm() function using the quasibinomial family as reported in R? I thought I should ask to be certain that there is...

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

Subject: Eliminate repeated components from a vector X-Mailer: VM 7.00 under 21.4 (patch 6) "Common Lisp" XEmacs Lucid Reply-To: fjmolina at lbl.gov FCC: /home/f/.xemacs/mail/sent Does anyone know how I can eliminate repeated elements from a vector?