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Data:
255 280.2 299.9 339.2 374.2 393.5 389.2 381.7 375.2 369 357.4 352.1 346.5 342.9 340.3 328.3 322.9 314.3 308.9 294 285.6 281.2 280.3 278.8 274.5 270.4 263.4 259.9 258 262.7 284.7 311.3 322.1 327 331.3 333.3 321.4 327 320 314.7 316.7 314.4 321.3 318.2 307.2 301.3 287.5 277.7 274.4 258.8 253.3 251 248.4 249.5 246.1 244.5 243.6 244 240.8 249.8 248 259.4 260.5 260.8 261.3 259.5 256.6 257.9 256.5 254.2 253.3 253.8 255.5 257.1 257.3 253.2 252.8 252 250.7 252.2 250 251 253.4 251.2 255.6 261.1 258.9 259.9 261.2 264.7 267.1 266.4 267.7 268.6 267.5 268.5 268.5 270.5 270.9 270.1 269.3 269.8 270.1 264.9 263.7 264.8 263.7 255.9 276.2 360.1 380.5 373.7 369.8 366.6 359.3 345.8 326.2 324.5 328.1 327.5 324.4 316.5 310.9 301.5 291.7 290.4 287.4 277.7 281.6 288 276 272.9 283 283.3 276.8 284.5 282.7 281.2 287.4 283.1 284 285.5 289.2 292.5 296.4 305.2 303.9 311.5 316.3 316.7 322.5 317.1 309.8 303.8 290.3 293.7 291.7 296.5 289.1 288.5 293.8 297.7 305.4 302.7 302.5 303 294.5 294.1 294.5 297.1 289.4 292.4 287.9 286.6 280.5 272.4 269.2 270.6 267.3 262.5 266.8 268.8 263.1 261.2 266 262.5 265.2 261.3 253.7 249.2 239.1 236.4 235.2 245.2 246.2 247.7 251.4 253.3 254.8 250 249.3 241.5 243.3 248 253 252.9 251.5 251.6 253.5 259.8 334.1 448 445.8 445 448.2 438.2 439.8 423.4 410.8 408.4 406.7 405.9 402.7 405.1 399.6 386.5 381.4 375.2 357.7 359 355 352.7 344.4 343.8 338 339 333.3 334.4 328.3 330.7 330 331.6 351.2 389.4 410.9 442.8 462.8 466.9 461.7 439.2 430.3 416.1 402.5 397.3 403.3 395.9 387.8 378.6 377.1 370.4 362 350.3 348.2 344.6 343.5 342.8 347.6 346.6 349.5 342.1 342 342.8 339.3 348.2 333.7 334.7 354 367.7 363.3 358.4 353.1 343.1 344.6 344.4 333.9 331.7 324.3 321.2 322.4 321.7 320.5 312.8 309.7 315.6 309.7 304.6 302.5 301.5 298.8 291.3 293.6 294.6 285.9 297.6 301.1 293.8 297.7 292.9 292.1 287.2 288.2 283.8 299.9 292.4 293.3 300.8 293.7 293.1 294.4 292.1 291.9 282.5 277.9 287.5 289.2 285.6 293.2 290.8 283.1 275 287.8 287.8 287.4 284 277.8 277.6 304.9 294 300.9 324 332.9 341.6 333.4 348.2 344.7 344.7 329.3 323.5 323.2 317.4 330.1 329.2 334.9 315.8 315.4 319.6 317.3 313.8 315.8 311.3
# simulations
blockwidth of bootstrap
Significant digits
Quantiles
P1 P5 Q1 Q3 P95 P99
P0.5 P2.5 Q1 Q3 P97.5 P99.5
P10 P20 Q1 Q3 P80 P90
bandwidth
Chart options
R Code
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) 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')) z <- data.frame(cbind(r$t[,1],r$t[,2])) colnames(z) <- list('mean','median') bitmap(file='plot7.png') b <- boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() b load(file='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='mytable.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='mytable1.tab')
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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