similar to: p-value for non-linear variable in overdispersed glm()

Thank you for the suggestion, Bill. The result is not quite what I would like. Here's sample code for you or anyone else who may be interested: Al1 = c('A','C','C','C') Al2 = c('G','G','G','T') Freq1 = c(0.0078,0.0567,0.9434,0.9908) MAF = c(0.0078,0.0567,0.0566,0.0092) m1 = data.frame(Al1=Al1,

Hello, I wonder if it is possible to change pxelinux.0's tftp prefix... I want to setup a repository like that: |-- dist1 | |-- pxelinux.cfg | | `-- default | `-- pxelinux.0 |-- dist2 | |-- pxelinux.cfg | | `-- default | `-- pxelinux.0 |-- pxelinux.cfg | |-- default | `-- menu.c32 `-- pxelinux.0 The file /pxelinux.cfg/default would contain something like that: >

I am new in R and i am having trouble here. I?ve already searched in the list but hasn?t helped When i run this script above i get the message "Error in gen[j, i] : incorrect number of dimensions". However gen is 1000x200 (ind x loc) and so is g could anybody help me for (i in 1 : loc) { #loc=200 for (j in 1 : ind) { #ind=1000 g1 <= function ( gen ) matrix ( if (gen[j, i]

Hey, I have a dataset and I want to identify the records by groups for further use in ggplot. Here is a sample data: ID Value AL1 1 AL2 2 CA1 3 CA4 4 I want to identify all the records that in the same state (AL1 AND A2), group them as "AL", and do the same for CA1 and CA4. How can I have an output like: ID Value State AL1 1 AL AL2 2 AL CA1 3 CA CA4 4

Hello, All! How might one accomplish this using the by() function? m1 is a data frame. # populate column "m1$major_allele" for ( i in 1:length(m1$major_allele)) { if ( m1$Freq1[i] == m1$MAF[i]){ m1$major_allele[i] = m1$Al1[i] } else{ m1$major_allele[i] = m1$Al2[i] } } Jim [[alternative HTML version deleted]]

Is there a way to combine two columns within a data frame? Example data: id snp AL1 AL2 1500 30 A B 1510 30 A A 1520 30 A B This is what I would like: indv snp AL1AL2 1500 30 AB 1510 30 AA 1520 30 AB Any help is greatly appreciated. Alysta

Dear R community, I am analyzing plant communities with the function mvpart, using a dissimilarit matrix as input. The matrix is calculated with the funtion gdist. fit <- mvpart(gdist (ba12[,18:29], meth="maximum", full=TRUE, sq=F) ~ beers + slope_dem + elev_dem+ plc_dem + pr_curv+ +curv+max_depth+doc_rocks+ abandon+land_use+ca_old, data=ba12, xv="p") This

Greetings: I would like comments on this example and after fixing it up, I need help from someone who has access to insert this in R's help page for plotmath. I uploaded a drawing http://pj.freefaculty.org/R/Normal-2009.pdf that is created by the following code http://pj.freefaculty.org/R/Normal1_2009_plotmathExample.R This will be a good addition to the plotmath help page/example.

Hi John, Thank-you very much for replying, - in answer to your questions: *"As I understand what you wrote, all factors but linelabel are crossed, with linelabel nested within all the other factors?" That's exactly it, *" What is in the highest order cells? Single observations, or multiple scores?" I would say "single observations" - but i have to

Hello everyone, I have programmed a function and, among other things, it has to compute the differential of a function. I have been using the function grad() and I have programmed some simulated studies. I have testes with 72,576 different combinations of the initial values and now I got an error in only 4 situations. The error comes from the function grad() and I don't understand why. This

I want to read data from a number of files into R. Reading individual files one by one requires writing enormous amount of code that will look something like the following. **************** maptools:::dbf.read("wb-01vc.dbf")->dist1 maptools:::dbf.read("wb-02vc.dbf")->dist2 maptools:::dbf.read("wb-03vc.dbf")->dist3

Hello All, I have a toy dataframe like this. It has 8 columns separated by tab. Name SampleID Al1 Al2 X Y R Th rs191191 A1 A B 0.999 0.09 0.78 0.090 abc928291 A1 B J 0.3838 0.3839 0.028 0.888 abcnab A1 H K 0.3939 0.939 0.3939 0.77 rx82922 B1 J K 0.3838 0.393

H ello R-experts, I want to do ordination plots using vegan metaMDS. I have a where many cells have zero values. Data structure: X[1:10,1:14] Height.1 Height.2 Height.3 Height.4 Height.5 Height.6 Height.7 Height.8 Height.9 Height.10 Height.11 Height.12 Height.13 D30I1A 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1B

Dear All, I would like to ask how to customize the graph corresponding to a procrustes analysis. I have to distance matrices, that I transform to two set of coordinates by means of muti dimensional scaling: library(mva) c1<-cmdscale(mat.dist1) c2<-cmdscale(mat.dist2) I vant to rotate c2 on c1, and I use the "procrustes" analysis from the {vegan} library. library(vegan)

I would like to try a simple parametric bootstrap, but unfortunately (stupidly?) my models are "overdispersed" gams & glms. I'm hoping for a function that generates overdispersed poisson or negative binomial data with a given mean, scale (& shape parameter). The loose definition I'm using is overdispersed poisson produces integer values with variance=const*mean &

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Hi all, First, a quick thank you for R; it's amazing. I am trying to fit models for a count dataset following the overdispersed logisitic regression approach outlined in Baggerly et al. (BMC Bioinformatics, 5:144; Annotated R code is given at the end of the paper) but R is returning an error with the data below. Any help in understanding or overcoming this obstacle is appreciated.

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

I am wondering if stepAIC in the MASS library may be used for model selection in an overdispersed Poisson situation. What I thought of doing was to get an estimate of the overdispersion parameter phi from fitting a model with all or most of the available predictors (we have a large number of observations so this should not be problematical) and then use stepAIC with scale = phi. Should this