search for: confpoints

...i in 1:cnt) { s = sample(x,10,replace=F) # sample population tresults = t.test(s) attach(tresults) C[i] = CIr(conf.int[1],conf.int[2]) if (CImiss(m,conf.int[1],conf.int[2])) Ccov = Ccov + 1 detach(tresults) bcaresults <- bcanon(s,5000,mean,alpha=c(.025,.975)) attach(bcaresults) B[i] = CIr(confpoints[1,2],confpoints[2,2]) if (CImiss(m,confpoints[1,2],confpoints[2,2])) Bcov = Bcov + 1 detach(bcaresults) } print(summary (C)) print(Ccov/cnt) print(summary (B)) print(Bcov/cnt) [[alternative HTML version deleted]]

...$obland [1] 4717.0 3605.6 10049.9 9513.1 1802.8 1118.0 500.0 500.0 2549.5 [10] 500.0 3000.0 6000.0 2000.0 2549.5 2061.6 4924.4 2692.6 In using bcanon I have tried the following: > diff.means<-function(x) {mean(distland$scland)-mean(distland$obland)} > bcanon(distland,100,diff.means) $confpoints alpha bca point [1,] 0.025 NA [2,] 0.050 NA [3,] 0.100 NA [4,] 0.160 NA [5,] 0.840 NA [6,] 0.900 NA [7,] 0.950 NA [8,] 0.975 NA $z0 [1] -Inf $acc [1] NaN $u [1] 773.7227 773.7227 $call bcanon(x = distland, nboot = 100, theta = diff.means) I am a new user of R and can't understand what is h...