# get.pi.permute: get the null distribution of the 'get.pi' function In IDSpatialStats: Estimate Global Clustering in Infectious Disease

## Description

Does permutations to calculate the null distribution of get pi if there were no spatial dependence. Randomly reassigns coordinates to each observation permutations times

## Usage

 ```1 2``` ```get.pi.permute(posmat, fun, r = 1, r.low = rep(0, length(r)), permutations, data.frame = TRUE) ```

## Arguments

 `posmat` a matrix with columns type, x and y `fun` the function to evaluate `r` the series of spatial distances we are interested in `r.low` the low end of each range....0 by default `permutations` the number of permute iterations `data.frame` logical indicating whether to return results as a data frame (default = TRUE)

## Value

pi values for all the distances we looked at

Other get.pi: `get.pi.bootstrap`, `get.pi.ci`, `get.pi.typed.bootstrap`, `get.pi.typed.permute`, `get.pi.typed`, `get.pi`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```#compare normally distributed with uniform points x<-cbind(1,runif(100,-100,100), runif(100,-100,100)) x<-rbind(x, cbind(2,rnorm(100,0,20), rnorm(100,0,20))) colnames(x) <- c("type","x","y") fun<-function(a,b) { if(a[1]!=2) return(3) if (b[1]==2) return(1) return(2) } r.max<-seq(10,100,10) r.min<-seq(0,90,10) r.mid <- (r.max+r.min)/2 pi<-get.pi(x,fun,r=r.max,r.low=r.min) pi.null<-get.pi.permute(x,fun,r=r.max,r.low=r.min,permutations=100) null.ci<-apply(pi.null[,-(1:2)],1,quantile,probs=c(0.25,0.75)) plot(r.mid, pi\$pi, type="l") lines(r.mid, null.ci[1,] , lty=2) lines(r.mid, null.ci[2,] , lty=2) ```