search for: chrismcowen

Dear List, I have a data frame called trait with roughly 800 species in, each species have 15 columns of information: Species 1 2 3 etc.. a t y h b f j u c r y u etc.. I then have another data frame called com with the composition of species in each region, there are 506 different communities: community species NA1102 a NA1102 c NA0402 b NA0402 c AT1302 a AT1302 b etc.. What

Dear List, I am unsure if this is the correct list to post to, if it isn't I apologise. I am using SSH to access a Linux version of R on a remote computer as it offers more memory and processing power. The model will take 1-2 days to run, I am accessing R through Putty and when I close the connection and open R again, I am faced with a new session. As a Linux newbie, I was

Dear List, I am looking to calculate two things from my data frame and was after some advice. For the example below i want to know. 1. How many unique Orders/Families and Genera there are per eco-name 2. How many incidences are there for each Order/Family and Genus there are per eco-region I have 650 econame. I.e for Biak-Numfoor rain forests there are 2 orders, 2 families and two genera.

My objective is to look at differences in two species of fish from morphometric measurements. My morphometric measurements are head length, eye diameter, snout length, and measurements from tail to each fin. I want to use discrimanant function analyis to determine if there are differences between the two species. I am familiar with R but new to discrimannt function analysis. I want to learn

Dear List, i am trying to produce a 3d plot using wireframe using the code: wireframe(Residuals_FD ~ Elevation * Temperature, data = data2, scales = list(arrows = FALSE), drape = TRUE, colorkey = TRUE) As you can see when the code (using the data below) is run the plot area is set-up correctly but the actual surface is missing? Any help would be greatly appreciated. Chris #data Elevation

Dear List, I am familier with binary models, however i am now trying to get predictions from a ordinal model and have a question. I have a data set made up of 12 categorical predictors, the response variable is classed as 1,2,3,4,5,6, this relates to threat level of the species ( on the IUCN rating). Previously i have combined levels 1 and 2 to form = non threatened and then combined 3-6 to