fgperm: Floating Grid Permutation Technique
In fgpt: Floating Grid Permutation Technique

Description Usage Arguments Details Value Author(s) References Examples

The fgperm function produces sets of permuted observations or indices using the Floating Grid Permutation Technique, which is a method for spatially restricted permutations. fgploc offers additional functionality to manipulate observations within grid cells, for instance observations could be scaled with grid cells.

fgperm(xy,z=1:dim(xy)[1], scale, group=1, iter=999, ratio=1, FUN=fyshuffle, ..., 
  add.obs=FALSE, as.matrix=FALSE)
fgploc(xy, scale, group=1, iter=999, ratio=1, FUN=fyshuffle, FUN.mani, ..., 
  marks, pass.rand=FALSE)

`xy`	Two-column matrix with the geographical locations of observations.
`z`	Vector with the observations. If left empty `z` will be a vector of indices from 1 to the total number of geographical locations. Those indices can be later be used for calculating statistics with `fgstat`.
`scale`	Value indicating the spatial scale of the permutations. `scale` should be positive.
`group`	Optional group membership of observations. `group` can be used when observations are for instance collected over multiple years.
`iter`	Number of iterations for every grid cell size. Default is 999. Note that in order to produce a probability an observation is assigned to any of the geographical locations is a negative function of the distance between its original and assigned location many iterations are needed.
`ratio`	The ratio between the sides of the grid cells. Default is 1.
`FUN`	Function to perform randomizations. Note that the function must be able to randomize one value, which is for instance a issue if using `sample`. For solutions see the Details and Examples of `sample`. The default is the Fisher-Yates shuffle `fyshuffle`.
`FUN.mani`	Function to perform manipulations of the observations within grid cells. This functionality should be used together with `cal.stat` to calculate specific statistics.
`...`	Optional arguments to `FUN` and `FUN.mani`.
`marks`	Should either be left empty, be a vector or a matrix. When `z` contains observations `marks` should be left empty. If the randomization procedure is for testing one variable, `marks` should be a vector for which the row numbers correspond to the index values in `z`. If the randomization procedure is for testing two variables (for instance the distance between them), `marks` should be a matrix for which the row numbers correspond to the index values in `z`.
`add.obs`	If `TRUE` the first set in the output will be the observed values.
`as.matrix`	If `TRUE` the output will be a matrix. If `FALSE` the output will be a list, which is needed when analyzing the data with `cal.stat`.
`pass.rand`	If `TRUE` The sets of randomized observations are also passed on to the output. Default is `FALSE`.

Before using those functions please read the reference or vignette. Alternatively use the more user-friendly function fgeasy. If there are missing values for the observations, leave z empty and enter the observations as marks in the fgstat function.

fgperm returns a list or a matrix, depending on the setting of argument as.matrix.

Reinder Radersma

Reinder Radersma & Ben C. Sheldon, 2015. A new permutation technique to explore and control for spatial autocorrelation. Submitted

## 200 random geographical locations
xy <- array(runif(400,0,2), dim=c(200,2))

## run fgperm to produce 99 randomizations for scale 1
test <- fgperm(xy, scale=1, iter=99, add.obs=TRUE) 

## run fgperm to produce 99 bootstraps for scale 1
test <- fgperm(xy, scale=1, iter=99, FUN=function(x){
          x[sample.int(length(x),replace=TRUE)]}, add.obs=TRUE) 

## 200 times 200 random distances (e.g. genetic relatedness between mated pairs)
trait <- array(rnorm(200*200,0.6,0.1), dim=c(200,200))
## make the observed pairs more alike
diag(trait) <- diag(trait)+0.02

## make two rows and two colums empty
trait[,3] <- NA
trait[,50] <- NA
trait[6,] <- NA
trait[12,] <- NA

## calculate means; will give NAs because there are missing values
calc <- fgstat(test,trait,mean)

## calculate means
calc <- fgstat(test,trait,mean, na.rm=TRUE)

## plot means
hist(calc)
abline(v=calc[1], col="red", lwd=2)