search for: lm609

Hello, I have a distribution dataset for species consisting of xy coordinates at the 1km resolution, with only presence data. So a simplified example of a species distribution might be: y <- rbind(as.integer(rnorm(100,50,20)), as.integer(rnorm(200,100,30)), as.integer(rnorm(100,180,15))) x <- rbind(as.integer(rnorm(200,50,20)), as.integer(rnorm(200,100,20)), as.integer(rnorm(100,200,15)))

Hello, I have written a 'for' loop which on the first run makes nearest neighbour calculations for my dataset 'A' in relation to dataset 'B', then based on these results, some of the rows from A are moved into dataset B, and the calculation is repeated on the remaining rows in A. Therefore a smaller and smaller amount of data is analysed as the loop proceeds, since A

Hello, I am working with a dataset which essentially has only one column - a list of distances in metres, accurate to several decimal places. eg distance 1000 6403.124 1000 1414.214 1414.214 1000 I want to organise this into a frequency table, grouping into categories of 0 - 999, 1000 - 1999, 2000-2999 etc. I'd also like the rows where there are no data points in that category to

Hello, I am trying to carry out a phylogenetic autoregression to test whether my data show a phylogenetic signal, but I keep calculating bizzare R-squared values. My script is: > library(ape) > x <-

Hello, I am trying to fit gamma, negative exponential and inverse power functions to a dataset, and then test whether the fit of each curve is good. To do this I have been advised to calculate predicted values for bins of data (I have grouped a continuous range of distances into 1km bins), and then apply a chi-squared test. Example: > data <- data.frame(distance=c(1,2,3,4,5,6,7),