search for: cooccured

...ing standardized data) >all2 Year standard 1 2001 0.034246575 2 2001 0.000000000 ... 141 2008 0.012820513 142 2008 0.230769231 I have plotted separate histograms for each year using hist(all2[Year==2001,]$standard,breaks=seq(0,.7,.005),ylim=c(0,10),main="2001",xlab="cooccured/total sites",ylab="frequency of cooccurance") hist(all2[Year==2002,]$standard, ... hist(all2[Year==2003,]$standard, ... etc. I would like to clean it up a bit by plotting the data using xyplot from library(lattice) of the standardized.data. My questions are: a. Is there a function...

Hey all, I feel like the solution to this problem should be relatively simple, but for some reason I can't find answers or come up with my own solution. Given the dataframe: (SpA and SpB not important, want to look at distribution of cooccurance for each year) Year SpA SpB Coocc 2000 0 2000 2 2000 1 2001 8 2001 2 2001 0 2001 0 2002 1 2002 2 How can I apply different functions to

Hi everyone, Have sample of items for each one, a set of 20 dichotomous (absent-present) variables are expressed. I'm trying to understand how to explore the co-occurence of each variable. Read some papers concerning smallest space analysis, but it does not seems implemented in any R package (and my protamming skills are =0). Non metric MDS gives error messages, probably because of the

Hi, I'm trying to build a thesaurus that will sensible values for rare words. I suspect the best algorithm to use is k-means although I'm not sure about that -- I would have preferred a k dimensional space with a binary cluster in each dimension so a word can belong to 0..k clusters, but I digress... I can measure the strength of correlation between words fairly easily by counting

Hello All! I am trying to plot the frequency of species coocurrance. If given a data set similar like this...(V1="species A", V2="species B", V3="frequency of cooccurance") > data V1 V2 V3 1 A B 0 2 A C 2 3 A D 5 4 B C 0 5 B D 1 6 C D 0 > data1<-as.data.frame(lapply(data,function(x)(rep(x,data$V3)))) > as.data.frame(data1[-1]) >

###Dear R users ###I have been using SensoMineR package from CRAN for most of my work in sensory data analysis and from my usage experience, I encountered some areas for improvement and considered ###modifying the function in SensoMineR package for my personal use. I felt that it could be useful to share this to the community for enabling adoption by other users where they might require a