1d:T76c,# Bootstrap `get.pi` values. Runs `get.pi` on multiple bootstraps of the data. Is formulated such that the relationships between points and themselves will not be calculated. ```r get.pi.bootstrap( posmat, fun, r = 1, r.low = rep(0, length(r)), boot.iter = 500, data.frame = TRUE ) ``` ## Arguments - `posmat`: a matrix with columns type, x and y - `fun`: the function to decide relationships - `r`: the series of spatial distances we are interested in - `r.low`: the low end of each range. 0 by default - `boot.iter`: the number of bootstrap iterations - `data.frame`: logical indicating whether to return results as a data frame (default = TRUE) ## Returns pi values for all the distances we looked at ## Note In each bootstrap iteration N observations are drawn from the existing data with replacement. To avoid errors in inference resulting from the same observatin being compared with itself in the bootstrapped data set, original indices are perserved, and pairs of points in the bootstrapped dataset with the same original index are ignored. ## Examples ```r #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.boot<-get.pi.bootstrap(x,fun,r=r.max,r.low=r.min,boot.iter=100) pi.ci<-apply(pi.boot[,-(1:2)],1,quantile,probs=c(0.25,0.75)) plot(r.mid, pi$pi , type="l") lines(r.mid, pi.ci[1,] , lty=2) lines(r.mid, pi.ci[2,] , lty=2) ``` ## See Also Other get.pi: `get.pi()`, `get.pi.ci()`, `get.pi.permute()`, `get.pi.typed()`, `get.pi.typed.bootstrap()`, `get.pi.typed.permute()` ## Author(s) Justin Lessler and Henrik Salje

get.pi.bootstrap function