cal.stat: Calculate statistics for FGM

Description Usage Arguments Details Value Author(s) References Examples

View source: R/cal.stat.R

Description

Calculates a set of values for a particular statistic or sets of observations, typically for observed values and multiple sets of randomized observations.

Usage

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cal.stat(rand,marks,FUN=mean, ...)

Arguments

rand

A list for which the elements are either sets of randomized variables or randomized index values. rand can, but not necessarily is, the output of the function fgrand.

marks

Should either be left empty, be a vector or a matrix. When rand contains randomized variables 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 used in rand. 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 used in rand.

FUN

Any function used to calculate the statistic of interest (e.g. mean, median, var, sd). Default for FUN is mean.

...

Optional arguments to FUN. A particular useful one if dealing with missing values and using one of the functions from base is na.rm=TRUE.

Details

cal.stat is designed to calculate statistics for spatial explicit data for which randomized data sets are generated with fgrand.

Value

cal.stat returns a vector of statistics. If rand is the output of fgrand and add.obs=TRUE, the first value is the statistic for the observed data and the rest for randomizations.

Author(s)

Reinder Radersma

References

Reinder Radersma & Ben C. Sheldon, 2014. A new permutation test for dealing with and exploring spatial autocorrelation submitted to MEE

Examples

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#### Example for fgrand

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

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

## run fgrand to produce 99 bootstraps for scale 1
test <- fgrand(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 <- cal.stat(test,trait,mean)

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

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

  

FGM documentation built on May 31, 2017, 2:23 a.m.