## 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 14:45:37 +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/t1477486055x1rk7cbo33o43fc.htm/, Retrieved Thu, 07 Jul 2022 14:00:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296687, Retrieved Thu, 07 Jul 2022 14:00:01 +0000
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Original text written by user:
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Estimated Impact96
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Bootstrap plot Task ] [2016-10-26 12:45:37] [71d167f7de04005af677e6526bf8917e] [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 16 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 time16 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296687&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] 16 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=296687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296687&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 16 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 19.133 19.265 19.772 19.996 20.149 20.628 20.914 0.32066 0.3768 median 20 20 20 20 21 21 21 0.43802 1 midrange 14.5 14.5 15 15 16 16.5 18.002 0.68502 1 mode 19 19 20 23 23 23 23 1.7347 3 mode k.dens 18.773 18.968 19.363 19.574 22.508 23.121 23.333 1.5726 3.1448

\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 & 19.133 & 19.265 & 19.772 & 19.996 & 20.149 & 20.628 & 20.914 & 0.32066 & 0.3768 \tabularnewline
median & 20 & 20 & 20 & 20 & 21 & 21 & 21 & 0.43802 & 1 \tabularnewline
midrange & 14.5 & 14.5 & 15 & 15 & 16 & 16.5 & 18.002 & 0.68502 & 1 \tabularnewline
mode & 19 & 19 & 20 & 23 & 23 & 23 & 23 & 1.7347 & 3 \tabularnewline
mode k.dens & 18.773 & 18.968 & 19.363 & 19.574 & 22.508 & 23.121 & 23.333 & 1.5726 & 3.1448 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296687&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]19.133[/C][C]19.265[/C][C]19.772[/C][C]19.996[/C][C]20.149[/C][C]20.628[/C][C]20.914[/C][C]0.32066[/C][C]0.3768[/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]0.43802[/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]16.5[/C][C]18.002[/C][C]0.68502[/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[/C][C]1.7347[/C][C]3[/C][/ROW]
[ROW][C]mode k.dens[/C][C]18.773[/C][C]18.968[/C][C]19.363[/C][C]19.574[/C][C]22.508[/C][C]23.121[/C][C]23.333[/C][C]1.5726[/C][C]3.1448[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296687&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 19.133 19.265 19.772 19.996 20.149 20.628 20.914 0.32066 0.3768 median 20 20 20 20 21 21 21 0.43802 1 midrange 14.5 14.5 15 15 16 16.5 18.002 0.68502 1 mode 19 19 20 23 23 23 23 1.7347 3 mode k.dens 18.773 18.968 19.363 19.574 22.508 23.121 23.333 1.5726 3.1448

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')