## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationWed, 26 Oct 2016 14:47:11 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Oct/26/t1477486319tbp64ytluj0mb6u.htm/, Retrieved Thu, 07 Jul 2022 14:28:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296688, Retrieved Thu, 07 Jul 2022 14:28:59 +0000
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Original text written by user:
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Estimated Impact102
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Bootstrap] [2016-10-26 12:47:11] [325a18647724c80085378f2a448a1737] [Current]
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Dataseries X:
21
22
22
18
23
12
20
22
21
19
22
15
20
19
18
15
20
21
21
15
16
23
21
18
25
9
30
20
23
16
16
19
25
18
23
21
10
14
22
26
23
23
24
24
18
23
15
19
16
25
23
17
19
21
18
27
21
13
8
29
28
23
21
19
19
20
18
19
17
19
25
19
22
23
14
16
24
20
12
24
22
12
22
20
10
23
17
22
24
18
21
20
20
22
19
20
26
23
24
21
21
19
8
17
20
11
8
15
18
18
19
19
23
22
21
25
30
17
27
23
23
18
18
23
19
15
20
16
24
25
25
19
19
16
19
19
23
21
22
19
20
20
3
23
23
20
15
16
7
24
17
24
24
19
25
20
28
23
27
18
28
21
19
23
27
22
28
25
21
22
28
20
29
25
25
20
20
16
20
20
23
18
25
18
19
25
25
25
24
19
26
10
17
13
17
30
25
4
16
21
23
22
17
20
20
22
16
23
0
18
25
23
12
18
24
11
18
23
24
29
18
15
29
16
19
22
16
23
23
19
4
20
24
20
4
24
22
16
3
15
24
17
20
27
26
23
17
20
22
19
24
19
23
15
27
26
22
22
18
15
22
27
10
20
17
23
19
13
27
23
16
25
2
26
20
23
22
24


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296688&T=0

[TABLE]
[ROW]
 Summary of computational transaction[/C][/ROW] [ROW] Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW] Raw Output[/C] view raw output of R engine [/C][/ROW] [ROW] Computing time[/C] 5 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=296688&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296688&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server Big Analytics Cloud Computing Center

 Estimation Results of Bootstrap statistic P0.5 P2.5 Q1 Estimate Q3 P97.5 P99.5 S.D. IQR mean 19.269 19.481 19.804 19.996 20.174 20.554 20.724 0.28461 0.3696 median 20 20 20 20 21 21 21 0.44606 1 midrange 14.5 14.5 15 15 16 16.513 17 0.61472 1 mode 19 19 20 23 23 23 23.005 1.723 3 mode k.dens 18.464 18.916 19.363 19.574 22.36 23.1 23.405 1.5622 2.9967

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P0.5 & P2.5 & Q1 & Estimate & Q3 & P97.5 & P99.5 & S.D. & IQR \tabularnewline
mean & 19.269 & 19.481 & 19.804 & 19.996 & 20.174 & 20.554 & 20.724 & 0.28461 & 0.3696 \tabularnewline
median & 20 & 20 & 20 & 20 & 21 & 21 & 21 & 0.44606 & 1 \tabularnewline
midrange & 14.5 & 14.5 & 15 & 15 & 16 & 16.513 & 17 & 0.61472 & 1 \tabularnewline
mode & 19 & 19 & 20 & 23 & 23 & 23 & 23.005 & 1.723 & 3 \tabularnewline
mode k.dens & 18.464 & 18.916 & 19.363 & 19.574 & 22.36 & 23.1 & 23.405 & 1.5622 & 2.9967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296688&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P0.5[/C][C]P2.5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P97.5[/C][C]P99.5[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]19.269[/C][C]19.481[/C][C]19.804[/C][C]19.996[/C][C]20.174[/C][C]20.554[/C][C]20.724[/C][C]0.28461[/C][C]0.3696[/C][/ROW]
[ROW][C]median[/C][C]20[/C][C]20[/C][C]20[/C][C]20[/C][C]21[/C][C]21[/C][C]21[/C][C]0.44606[/C][C]1[/C][/ROW]
[ROW][C]midrange[/C][C]14.5[/C][C]14.5[/C][C]15[/C][C]15[/C][C]16[/C][C]16.513[/C][C]17[/C][C]0.61472[/C][C]1[/C][/ROW]
[ROW][C]mode[/C][C]19[/C][C]19[/C][C]20[/C][C]23[/C][C]23[/C][C]23[/C][C]23.005[/C][C]1.723[/C][C]3[/C][/ROW]
[ROW][C]mode k.dens[/C][C]18.464[/C][C]18.916[/C][C]19.363[/C][C]19.574[/C][C]22.36[/C][C]23.1[/C][C]23.405[/C][C]1.5622[/C][C]2.9967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296688&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296688&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Estimation Results of Bootstrap statistic P0.5 P2.5 Q1 Estimate Q3 P97.5 P99.5 S.D. IQR mean 19.269 19.481 19.804 19.996 20.174 20.554 20.724 0.28461 0.3696 median 20 20 20 20 21 21 21 0.44606 1 midrange 14.5 14.5 15 15 16 16.513 17 0.61472 1 mode 19 19 20 23 23 23 23.005 1.723 3 mode k.dens 18.464 18.916 19.363 19.574 22.36 23.1 23.405 1.5622 2.9967

par4 <- 'P1 P5 Q1 Q3 P95 P99'par3 <- '0'par2 <- '5'par1 <- '200'par1 <- as.numeric(par1)par2 <- as.numeric(par2)if (par3 == '0') bw <- NULLif (par3 != '0') bw <- as.numeric(par3)if (par1 < 10) par1 = 10if (par1 > 5000) par1 = 5000library(modeest)library(lattice)library(boot)boot.stat <- function(s,i){s.mean <- mean(s[i])s.median <- median(s[i])s.midrange <- (max(s[i]) + min(s[i])) / 2s.mode <- mlv(s[i], method='mfv')$Ms.kernelmode <- mlv(s[i], method='kernel', bw=bw)$Mc(s.mean, s.median, s.midrange, s.mode, s.kernelmode)}x<-na.omit(x)(r <- boot(x,boot.stat, R=par1, stype='i'))bitmap(file='plot1.png')plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean')grid()dev.off()bitmap(file='plot2.png')plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median')grid()dev.off()bitmap(file='plot3.png')plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')grid()dev.off()bitmap(file='plot7.png')plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')grid()dev.off()bitmap(file='plot8.png')plot(r$t[,5],type='p',ylab='simulated values',main='Simulation of Mode of Kernel Density')grid()dev.off()bitmap(file='plot4.png')densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean')dev.off()bitmap(file='plot5.png')densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median')dev.off()bitmap(file='plot6.png')densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange')dev.off()bitmap(file='plot9.png')densityplot(~r$t[,4],col='black',main='Density Plot',xlab='mode')dev.off()bitmap(file='plot10.png')densityplot(~r$t[,5],col='black',main='Density Plot',xlab='mode of kernel dens.')dev.off()z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]))colnames(z) <- list('mean','median','midrange','mode','mode k.dens')bitmap(file='plot11.png')boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')grid()dev.off()load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)a<-table.row.end(a)if (par4 == 'P1 P5 Q1 Q3 P95 P99') {myq.1 <- 0.01myq.2 <- 0.05myq.3 <- 0.95myq.4 <- 0.99myl.1 <- 'P1'myl.2 <- 'P5'myl.3 <- 'P95'myl.4 <- 'P99'}if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {myq.1 <- 0.005myq.2 <- 0.025myq.3 <- 0.975myq.4 <- 0.995myl.1 <- 'P0.5'myl.2 <- 'P2.5'myl.3 <- 'P97.5'myl.4 <- 'P99.5'}if (par4 == 'P10 P20 Q1 Q3 P80 P90') {myq.1 <- 0.10myq.2 <- 0.20myq.3 <- 0.80myq.4 <- 0.90myl.1 <- 'P10'myl.2 <- 'P20'myl.3 <- 'P80'myl.4 <- 'P90'}a<-table.row.start(a)a<-table.element(a,'statistic',header=TRUE)a<-table.element(a,myl.1,header=TRUE)a<-table.element(a,myl.2,header=TRUE)a<-table.element(a,'Q1',header=TRUE)a<-table.element(a,'Estimate',header=TRUE)a<-table.element(a,'Q3',header=TRUE)a<-table.element(a,myl.3,header=TRUE)a<-table.element(a,myl.4,header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'IQR',header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mean',header=TRUE)q1 <- quantile(r$t[,1],0.25)[[1]]q3 <- quantile(r$t[,1],0.75)[[1]]p01 <- quantile(r$t[,1],myq.1)[[1]]p05 <- quantile(r$t[,1],myq.2)[[1]]p95 <- quantile(r$t[,1],myq.3)[[1]]p99 <- quantile(r$t[,1],myq.4)[[1]]a<-table.element(a,signif(p01,par2))a<-table.element(a,signif(p05,par2))a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[1],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(p95,par2))a<-table.element(a,signif(p99,par2))a<-table.element( a,signif( sqrt(var(r$t[,1])),par2 ) )a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'median',header=TRUE)q1 <- quantile(r$t[,2],0.25)[[1]]q3 <- quantile(r$t[,2],0.75)[[1]]p01 <- quantile(r$t[,2],myq.1)[[1]]p05 <- quantile(r$t[,2],myq.2)[[1]]p95 <- quantile(r$t[,2],myq.3)[[1]]p99 <- quantile(r$t[,2],myq.4)[[1]]a<-table.element(a,signif(p01,par2))a<-table.element(a,signif(p05,par2))a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[2],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(p95,par2))a<-table.element(a,signif(p99,par2))a<-table.element(a,signif(sqrt(var(r$t[,2])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'midrange',header=TRUE)q1 <- quantile(r$t[,3],0.25)[[1]]q3 <- quantile(r$t[,3],0.75)[[1]]p01 <- quantile(r$t[,3],myq.1)[[1]]p05 <- quantile(r$t[,3],myq.2)[[1]]p95 <- quantile(r$t[,3],myq.3)[[1]]p99 <- quantile(r$t[,3],myq.4)[[1]]a<-table.element(a,signif(p01,par2))a<-table.element(a,signif(p05,par2))a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[3],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(p95,par2))a<-table.element(a,signif(p99,par2))a<-table.element(a,signif(sqrt(var(r$t[,3])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mode',header=TRUE)q1 <- quantile(r$t[,4],0.25)[[1]]q3 <- quantile(r$t[,4],0.75)[[1]]p01 <- quantile(r$t[,4],myq.1)[[1]]p05 <- quantile(r$t[,4],myq.2)[[1]]p95 <- quantile(r$t[,4],myq.3)[[1]]p99 <- quantile(r$t[,4],myq.4)[[1]]a<-table.element(a,signif(p01,par2))a<-table.element(a,signif(p05,par2))a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[4],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(p95,par2))a<-table.element(a,signif(p99,par2))a<-table.element(a,signif(sqrt(var(r$t[,4])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mode k.dens',header=TRUE)q1 <- quantile(r$t[,5],0.25)[[1]]q3 <- quantile(r$t[,5],0.75)[[1]]p01 <- quantile(r$t[,5],myq.1)[[1]]p05 <- quantile(r$t[,5],myq.2)[[1]]p95 <- quantile(r$t[,5],myq.3)[[1]]p99 <- quantile(r$t[,5],myq.4)[[1]]a<-table.element(a,signif(p01,par2))a<-table.element(a,signif(p05,par2))a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[5],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(p95,par2))a<-table.element(a,signif(p99,par2))a<-table.element(a,signif(sqrt(var(r\$t[,5])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable.tab')