similar to: CAPER pgls() anova not working with variable as factor

Hello, I'm doing a comparative analysis of mammal brain and body size data. I'm following Charlie Nunn and Natalie Cooper's instructions for "Running PGLS in R using caper". I run into the following error when I create my comparative dataset, combining my phylogenetic tree (mammaltree) and taxon measures (mammaldata): "Error in phy$node.label[which(newNb > 0) -

Dear R experts, I hope this is the right list for my question. As a newcomer in R, I am testing the R CAPER package, applying a simple regression on a phylogeny tree with three binary traits: (t1, t2, t3). My goal is to test the sensitivity to a correlation between t1 and t3. But if a correlation of 100% is considered (i.e. t3 = t1). The pgls method of CAPER seems to crash: You can find there

## I clicked the send-button too quickly, before changing the title of the message etc... Sorry.## I am running following phylogenetic analyses with the caper package: data=read.table(file="data.txt",header=T,sep="\t") tree = read.nexus("Tree.nex") primate = comparative.data(phy=tree, data=data,              names.col=Species, vcv=TRUE, na.omit=FALSE,

I can get btrfs to throw a kernel bug easily by running btrfs fi defrag on some files in 3.9.0: May 7 01:57:33 caper kernel: [ 0.000000] Linux version 3.9.0-030900-generic (apw@gomeisa) (gcc version 4.6.3 (Ubuntu/Linaro 4.6.3-1ubuntu5) ) #201304291257 SMP Mon Apr 29 16:58:15 UTC 2013 ... May 7 02:09:21 caper kernel: [ 726.745485] ------------[ cut here ]------------ May 7 02:09:21 caper

Hello! I have several variables. Each of them has a different distribution. I was thinking to use a Generalized Linear Model, glm(), but I need to introduce the family. Do you know if R has any tests for matching data to any distribution ( I am aware of shapiro.test). All the best, -- BAS [[alternative HTML version deleted]]

I have formulae with coefficents that I would like to update. However, I get some strange results. For example, see the following: For the formula y ~ d+ 3*r+t I want to add a variable p, so > update(y~d+0*r+t, .~.+p) produces y ~ d + t + p - 1 If the coefficient is not 0, but rather, something else - say, 3, I get the following: > update(y~d+3*r+t, .~.+p) Error in

Hi all, I need some solution in the following problem. The following error appears when i use "mgcv" package for implementing GAM. But the same formula works fine in "gam" package. > model.gam <- gam(formula = RES ~ > CAT01+s(NUM01,5)+CAT02+CAT03+s(NUM02,5)+CAT04+ + CAT05+s(NUM03,5)+CAT06+CAT07+s(NUM04,5)+CAT08+s(NUM05,5)+CAT09+ +

Hi list,Could you please help me to explain the following error messages with 't.test' in R Unix 2.1.1? I don't see it in R under Windows (R 2.3.0) or Unix (R2.3.1). Is it really due to the different R versions?Thanks,...TaoUnix session: (R.2.1.1)========================> R.version _ platform x86_64-unknown-linux-gnuarch x86_64 os

Dear moderator, If possible I would like to send in the following question for R-help: I am analyzing a small data set using PGLS with phylogenetic uncertainty taken into account and thereby including 100 potential phylogenetic tree scenarios. I've managed to run models on all of the different trees and performed model averaging to get parameter estimates for the intercept and most of the

Hello, I'm trying to calculate a chi-squared test to see if my data are different from the theoretical distribution or not: chisq.test(rbind(c(79, 52, 69, 71, 82, 87, 95, 74, 55, 78, 49, 60),c(80,80,80, 80, 80, 80, 80, 80, 80, 80, 80, 80))) Pearson's Chi-squared test data: rbind(c(79, 52, 69, 71, 82, 87, 95, 74, 55, 78, 49, 60), c(80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80,

Dear list, I have been searching for a week to fit a simple linear model to my data. I have looked into the previous posts but I haven't found anything relevant to my problem. I guess it is something simple...I just cannot see it. I have the following data frame, named "data", which is a subset of a microarray experiment. The columns are the samples and the rows are the probes. I

Hi, I try to calculate Kendall's W coefficient and I have a bizarre error. little.app.mat<-matrix(c(1,3,4,2,6,5,2,4,3,1,5,6,3,2,5,1,5,4),nrow=3,byrow=TRUE) print(kendall.w(little.app.mat[-1,])) >>> Kendall's W for ordinal data >>> W = 0.7753623Error in if (is.na(x$p.table)) { : argument is of length zero

Hi, I am a new user of R. I have 2 series of nominal data (2 series of answers for the same question) and I want to calculate the correlation between these 2 series. I've tried to use the correlation function (corr, ...) but all are for numeric data... Does anyone know what function should I use for the nominal data? Thanks a lot, Bianca

Hi all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

Full_Name: Massimo santini Version: 1.0.1 OS: Linux Submission from: (NULL) (159.149.147.89) I've just dl the .rpm of R 1.0.1, installed it and then launched insatll.packages("akima") after re-launching R [--vanilla], the command library(akima) has the only effect to coredump the program. Here is the output of gdb and strace... --- GDB (commands given can be seen from the

Hello, I have a big table with 3 columns and 103918 rows. This is the example,            time             species                 dbh 5 1 4.9377297 575 1 11.64127213 575 1 109.8182438 575 1 8.029809521 5 1 24.32501874 575 1 4.895992119 575 1 11.40567637 575 1 2.795090562 575 1 21.79281837 575 1 52.57476174 575 1 27.7290919 575 1 3.23262083 575 2 19.30612651 575 1

Dear R users, I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green). model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4) Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533

Hi all, there is probably something I'm still missing about how R manipulates multivariate data. I have a dataset in which, for various tuples of a set of 4 parameters, a value of interest is stored: p1 p2 p3 p4 v 0 0 0 0 0.2342 0 1 1 0 0.6124 .... 0 0 1 1 0.1234 What I would like to do is to "cut" the

Dear all, Can anyone help me, my R software can not run a nested linear regression by using the lme funcion. The message that appears is  Error: could not find function "lme" I already downloaded and loaded the package, please see below. Thank you in advance for any help! Nadia. > data<-read.csv("/Users/nadiasan1/Desktop/MOE and MOR.csv")> attach(data)>

Dear R users, I have to call fortran program from within R (R 2.8.1 on ubuntu 8.10 machine). Suppose I have a fortran code like this (this is only a toy model, my working model is far more complex, but input/output is similar) DOUBLE PRECISION FUNCTION model(times, alfa, beta) DOUBLE PRECISION alfa, beta, times model=beta*sin(times)+alfa*cos(times) END FUNCTION which