R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(9769,9321,9939,9336,10195,9464,10010,10213,9563,9890,9305,9391,9928,8686,9843,9627,10074,9503,10119,10000,9313,9866,9172,9241,9659,8904,9755,9080,9435,8971,10063,9793,9454,9759,8820,9403,9676,8642,9402,9610,9294,9448,10319,9548,9801,9596,8923,9746,9829,9125,9782,9441,9162,9915,10444,10209,9985,9842,9429,10132,9849,9172,10313,9819,9955,10048,10082,10541,10208,10233,9439,9963,10158,9225,10474,9757,10490,10281,10444,10640,10695,10786,9832,9747,10411,9511,10402,9701,10540,10112,10915,11183,10384,10834,9886,10216)
> par2 = '12'
> par1 = '500'
> par1 <- as.numeric(par1)
> par2 <- as.numeric(par2)
> if (par1 < 10) par1 = 10
> if (par1 > 5000) par1 = 5000
> if (par2 < 3) par2 = 3
> if (par2 > length(x)) par2 = length(x)
> library(lattice)
> library(boot)

Attaching package: 'boot'


	The following object(s) are masked from package:lattice :

	 melanoma 

> boot.stat <- function(s)
+ {
+ s.mean <- mean(s)
+ s.median <- median(s)
+ c(s.mean, s.median)
+ }
> (r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))

BLOCK BOOTSTRAP FOR TIME SERIES

Fixed Block Length of 12 

Call:
tsboot(tseries = x, statistic = boot.stat, R = par1, l = 12, 
    sim = "fixed")


Bootstrap Statistics :
    original    bias    std. error
t1*  9816.77  1.143625   105.33834
t2*  9824.00 -1.947000    95.09894
> z <- data.frame(cbind(r$t[,1],r$t[,2]))
Warning message:
In data.row.names(row.names, rowsi, i) :
  some row.names duplicated: 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271 [... truncated]
> colnames(z) <- list('mean','median')
> postscript(file="/var/www/html/rcomp/tmp/1e4m51290950323.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> b <- boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
> grid()
> dev.off()
null device 
          1 
> b
$stats
          [,1]   [,2]
[1,]  9571.417 9643.0
[2,]  9752.302 9764.0
[3,]  9813.094 9821.5
[4,]  9883.818 9857.5
[5,] 10066.906 9992.5

$n
[1] 500 500

$conf
         [,1]     [,2]
[1,] 9803.801 9814.893
[2,] 9822.387 9828.107

$out
 [1] 10081.583  9497.375 10088.990 10107.865 10113.031  9516.802 10081.323
 [8] 10165.458  9529.635 10024.000 10158.000  9525.500 10005.000  9610.000
[15] 10061.000 10005.000 10082.000 10055.500  9610.000  9618.500 10005.000
[22] 10097.000 10005.000  9610.000  9610.000 10046.000 10065.000 10097.000
[29] 10158.000  9618.500 10097.000  9603.000  9555.500  9610.000  9610.000
[36]  9610.000 10158.000  9503.000 10112.000 10195.000  9548.000  9610.000
[43] 10135.000 10000.000

$group
 [1] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[39] 2 2 2 2 2 2

$names
[1] "mean"   "median"

> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'statistic',header=TRUE)
> a<-table.element(a,'Q1',header=TRUE)
> a<-table.element(a,'Estimate',header=TRUE)
> a<-table.element(a,'Q3',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'IQR',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'mean',header=TRUE)
> q1 <- quantile(r$t[,1],0.25)[[1]]
> q3 <- quantile(r$t[,1],0.75)[[1]]
> a<-table.element(a,q1)
> a<-table.element(a,r$t0[1])
> a<-table.element(a,q3)
> a<-table.element(a,sqrt(var(r$t[,1])))
> a<-table.element(a,q3-q1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'median',header=TRUE)
> q1 <- quantile(r$t[,2],0.25)[[1]]
> q3 <- quantile(r$t[,2],0.75)[[1]]
> a<-table.element(a,q1)
> a<-table.element(a,r$t0[2])
> a<-table.element(a,q3)
> a<-table.element(a,sqrt(var(r$t[,2])))
> a<-table.element(a,q3-q1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/2sd2w1290950323.tab") 
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'95% Confidence Intervals',3,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'',1,TRUE)
> a<-table.element(a,'Mean',1,TRUE)
> a<-table.element(a,'Median',1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Lower Bound',1,TRUE)
> a<-table.element(a,b$conf[1,1])
> a<-table.element(a,b$conf[1,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Upper Bound',1,TRUE)
> a<-table.element(a,b$conf[2,1])
> a<-table.element(a,b$conf[2,2])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/3ewik1290950323.tab") 
> try(system("convert tmp/1e4m51290950323.ps tmp/1e4m51290950323.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.763   0.187   1.520