similar to: Random assignment

Hi, I have two matrices with a common field = species what i want to do is make a matrix that combines the data held in the other two based on the species name. I.e ( simple example) Matrix 1 - monocot SPECIES V1 A 2 B 3 C 4 D 5 Matrix 2 - PCAresults SPECIES V2 A 0.2 B 0.3 C 4.1 D 3.2 matrix 3 - What i want SPECIES V1 V2 A 2 0.2 B 3 0.3 C 4 4.1 D 5 3.2 my

Hi, I'm needing some help finding a function to read a large text file into an array in R. The data are essentially presence / absence / na data for many species and come as a grid with each species name (after two spaces) at the beginning of the matrix defining the map for that species. An excerpt could therefore be: SPECIES1 999001099 900110109 011101000 901100101 110100019 901110019

>> Dear All, >> >> I am having trouble getting my data into R as i need it! I am used to using read.delim("") to open .txt files to do work on. The function i am using requires a matrix like the one below. >> >> My data is from excel and then saved as a txt file. I have tried the usual read.delim("") approach but the function doesn't like it, i

Hi, I have reconstructed ancestral character states on a phylogeny using MuSSE in the diversitree package and plotted the character state probabilities as pie charts on the nodes. I would, however, like to colour the character states of my extant species, i.e. the tip labels, the same colours as my pie charts, such that all species in state 1 are e.g. blue, species in state 2 red and species in

Dear List, I have a table i have read into R: Name Yes/No John 0 Frank 1 Ann 0 James 1 Alex 1 etc - 800 different times. What i want to do is shuffle yes/no and randomly re-assign them to the name. I have used sample() and permute(), however there is no way to do this 1000 times. Furthermore, i want to copy the data into a excel spreadsheet in the same order as the data was input so i can

Dear all, We want to test if the invasiveStatus is predicted by the amount (quant) of animals arriving to a country of a certain species (taxonid). We are using lmer to perform the model. The model is: lmer(invasiveStatus~I(log(quant+1))+I(log(inDegree+1))+(1|taxonid)+(1|country), family=binomial,data=td), where invasiveStatus is a binary variable, quant and inDegree are integer variables, and

I teach biology, and would like to show the students how to use R for some statistical assignments. One of those is to make a kite diagram (for example as seen in http://www.medinavalleycentre.org.uk/images/Bembri1.jpg). Is there any way to create one using R? I did a help.search("kite") and looked on the r-project HP with no luck. Previously when the course was taugh the students have

Hi, I am using this loop par(mfrow=c(3,3)) annos<-c(2001:2007,2009) for (i in annos) { t<-subset(masia,YEAR==i) t$FAMILIA<-drop.levels(t$FAMILIA) pie(table(t$FAMILIA),main=i) } To make piecharts of species composition among years (my data frame is called "masia"). So I get 1 piechart of the families that we have found in our survey each year. We don't have always

Dear List, I have a data set stored in the following format: > head(dat, n = 10) id sppcode abundance 1 10307 10000000 1 2 10307 16220602 2 3 10307 20000000 5 4 10307 20110000 2 5 10307 24000000 1 6 10307 40210000 83 7 10307 40210102 45 8 10307 45140000 1 9 10307 45630000 1 10 10307 45630600 41 >

Hi Everyone,  Sorry to ask what I think is a basic question but I really haven't found my answer yet in the archives.  I am trying to run a mixed effects model in R using the lme package. My experiment is such that I am interested in the effects of Temperature (2 levels) and Species (3 levels) on Growth. I collected individuals from three populations within each species. Because individuals

To account for likely differences between families in naturalization rates, we fitted a generalized linear mixed model, using PROC NLMIXED in SAS10, with the naturalization rate per genus (that is, the number of naturalized species in a genus as a proportion of the total number of introduced species in a genus) as the response variable, a variable coding genera as containing at least one native

Hi, I have the following conceptual / interpretative question regarding random effects: A mixed effects model was fit on biological data, with observations coming from different species. There is a clear overall effect of certain predictors (entering the model as fixed effect), but as different species react slightly differently, the predictor also enters the model as random effect and with

Apologies for troubling the list, but it is a test that needs to be carried out. I am resending the message that I sent earlier on behalf of Paul Miller, but with a certain word used in the variables names of the SAS code changed to a different word. With thanks for your tolerance ... Ted. [*** PLEASE NOTE: I am sending this message on behalf of Paul Miller: Paul Miller <pjmiller_57 at

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

Hello: I am using R mainly on windows XP, version 2.5. I?m a biologist, with a medium level statistics background. I have a problem stating a two-way factorial design where both factors are random. I?m using the lmer() function implemented in the Matrix package version 0.99. My design is as follows: Two species were randomly selected from a total of 4 species. This species are present

Dear all, I make habitat suitability models for animal species. The purpose of my research is to investigate the accuracy of different models. I clearly have a nested design: - accuracy_measure -> response variable - 2 model types (model_type) -> fixed effect - 230 species (species) -> random effect - 10 replicates/species (replicate) -> random effect - 10 subreplicate/replicate

I have a host-parasite association matrix in which parasite species are rows and host species columns and cells contain the frequency of interactions. Some parasites are associated with many hosts, and some hosts harbor several parasites, and I want to repeatedly select only one single representative host per "generalized" (multi-host) parasite to create a new matrix in which no hosts

Hi there, I am an environmental studies masters student trying to get my thesis out the door. I am also newbie at trees in general, but I like what I see in the literature about the random forest algorithm. I think I get the general gist of things, but even after reading stuff I?m unclear about how I could be getting the results I?m seeing. I obviously am missing something about how the split

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

R Experts, I would like to create a series of variables without having to assign a separate line of code for each new variable. My dataframe (DF) contains two groups of linked variables (ESP1:ESP9) and (ECRL1:ECRL9). Within ESP1:ESP9 are abbreviated species codes (full dataframe contains 26 codes). ECRL1 represents the number of species x in variable ESP1 harvested, and so on through ESP9 and