similar to: quasibinomial glm

Hello list, (PC Windows 2000, R 1.2.3) i want to compile C source file for R on windows 2000. I'm working with Metrowerks CodeWarrior for creating and compiling C files. I've see that R.lib file must be obtain with R.ddl by Visual C++. I don't have Visual C++ (and i don't want to buy it !!) but i want to obtain R.lib file. Is there a solution to my problem ??? Any help would

Hello, i' m looking for a function to compute geary statistic for spatial correlation. Thanks. -- St?phane DRAY --------------------------------------------------------------- Biom?trie et Biologie ?volutive - Equipe "?cologie Statistique" Universite Lyon 1 - Bat 711 - 69622 Villeurbanne CEDEX - France Tel : 04 72 43 27 56 Fax : 04 78 89 27 19 04 72 43 27 57 E-mail

Hello List, i need help for eigen and svd functions. I have a non-symmetric square matrix. These matrix is not positive (some eigenvalues are negative). I want to diagonalise these matrix. So, I use svd and eigen and i compare the results. eigen give me the "good" eigenvalues (positive and negative). I compare with another software and the results are the same. BUT, when i use svd,

Hello, I am trying to play with RODBC library and Excel Files. In my file (doubs.xls) there are 2 spreadsheets: > library(RODBC) > connection<-odbcConnect("Excel Files") > sqlTables(connection) TABLE_CAT TABLE_SCHEM TABLE_NAME TABLE_TYPE REMARKS 1 F:\\Th?se\\R\\Doubs NA Faune$ SYSTEM TABLE NA 2 F:\\Th?se\\R\\Doubs NA Milieu$

A L E R T E V I R U S Notre antivirus a d?tect? le VIRUS "W32/Sobig.e at MM" dans votre courrier destin? ? : -> dray at biomserv.univ-lyon1.fr Nous refusons de le transmettre. Nous vous conseillons de d?contaminer votre poste de travail. Pour votre information, vous trouverez ci-dessous les entetes de votre message Pour des

Dear all, I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm(). 1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values for

Dear all, I try to consider overdispersion in a lmer model. But using family=quasibinomial rather than family=binomial seems to change the fit but not the result of an anova test. In addition if we specify test="F" as it is recomanded for glm using quasibinomial, the test remains a Chisq test. Are all tests scaled for dispersion, or none? Why is there a difference between glm and lmer

Full_Name: Henric Nilsson Version: 2.3.0 Patched (2006-05-09 r38014) OS: Windows 2000 SP4 Submission from: (NULL) (83.253.9.137) When supplying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit",

After looking at 48 glm binomial models I decided to try the quasibinomial with the top model 25 (lowest AIC). To try to account for overdispersion (residual deviance 2679.7/68 d.f.) After doing so the dispersion factor is the same for the quasibinomial and less sectors of the beach were significant by p-value. While the p-values in the binomial were more significant for each section of the

Hello, I would like to have some information about acf with missing values. Let us consider this example: x=rnorm(100) x2=x x2[sample(100,10)]=NA acf1=acf(x) acf2=acf(x2,na.action=na.pass) The computation of the acf is different for the two data sets. Looking at the the code reveals that with missing values, the computation of acf takes into account the number of pairs of non-NA values (i.e.

Hi all, I've been trying to figure out how to superimpose a histogram on a biplot that shows the relative contribution of each axis. I have been using the NIPALS function (http://biomserv.univ-lyon1.fr/~dray/ files/softwares/nipals.R) to run principal component analyses. Here is a toy example. source("http://biomserv.univ-lyon1.fr/~dray/files/softwares/nipals.R")

Hello, I have some questions concerning encoding and package distribution. We develop the ade4 package. For some data sets included in the package, there are accentued character (e.g. ?,?...). The data sets have been saved using latin1 encoding, but some of us use utf-8 and can not see some data sets which contains accented chracters. e.g: librarry(ade4) data(rankrock) rankrock in this case,

Dear all, I would like to compute power of a square non symmetric matrix. This is a part of a simulation study. Matrices are quite large (e.g., 900 by 900), and contains many 0 (more than 99 %). I have try the function mtx.exp of the Biodem package: library(Biodem) m <- matrix(0, 900, 900) i <- sample(1:900, 3000, replace = T) j <- sample(1:900, 3000, replace = T) for(x in 1:3000)

I've originally made 48 GLM binomial models and compare the AIC values. But dispersion was very large: Example: Residual deviance: 8811.6 on 118 degrees of freedom I was suggested to do a quasibinomial afterwards but found that it did not help the dispersion factor of models and received a warning: Residual deviance: 3005.7 on 67 degrees of freedom AIC: NA Number of Fisher Scoring

I am running a binomial glm with response variable the no of mites of two species y->cbind(mitea,miteb) against two continuous variables (temperature and predatory mites) - see below. My model shows overdispersion as the residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial to account for overdispersion the dispersion parameter estimate is 2501139, which seems

I am unable to calculate confidence intervals for the slope estimate in a quasibinomial glm using confint(). Below is the output and the package info for MASS. Thanks in advance! R 2.9.2 MASS 7.2-48 > confint(glm.palive.0.str) Waiting for profiling to be done... Error: dims [product 37] do not match the length of object [74] > glm.palive.0.str Call: glm(formula = cbind(alive, red) ~ str,

Dear All, I would like to plot text with a box around it. I used strwidth and strheight to compute the size of the box which is plotted with rect: z <- rnorm(10) # horizontal text works plot(rnorm(10)) x1 <- 5 y1 <- 0 label <- "Label" cha <- paste(" ", label, " ", sep = "") xh <- strwidth(cha, cex = par("cex")) yh <-

Hi, a while ago I posted a question regarding the use of alternative models, including a quasibinomial mixed-effects model (see Results 1). I rerun the exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results 2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3). While the coefficient estimates are basically the same in all three regressions, the estimated standard

Hello list, I would like to cross-compile R packages using R 2.4.0. I am working on Linux Debian and cross-compiled (windows binaries) without problems with older R version. I have used the doc of Yan and Rossini in the contributed section of the R documentation (same version of MinGW...). When I try to cross-compile R (make R), the procedure stopped and returns :

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