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Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationSun, 01 Dec 2013 07:59:11 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/01/t1385902873dc1wz94q2w7oefk.htm/, Retrieved Thu, 28 Mar 2024 21:52:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=229773, Retrieved Thu, 28 Mar 2024 21:52:44 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
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Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2013-12-01 12:59:11] [267d6dd6d9f18d2f1c3bf0912f69c4a3] [Current]
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Dataseries X:
8
10
10
13
14
12
11
8
8
10
10
12
12
12
11
12
12
12
12
12
12
10
12
10
11
10
10
12
10
12
7
12
18
12
11
13
10
10
10
8
12
10
10
8
14
9
8
12
15
14
1
9
7
8
12
57
12
10
10
8
8
16
14
13
10
12
9
12
11
10
8
8
9
12
8
12
10
12
9
8
12
8
12
10
12
9
28
10
12
9
14
12
12
99
13
13
14
12
12
10
11
12
14
10
12
12
6
12
10
12
12
12
9
12
12
13
8
12
10
10
10
9
12
9
10
8
12
10
8
8
9
12
12
10
10
9
11
10
9
15
10
8
10
8
9
9
6
16
12
12
12
12
10
12
8
9
12
12
8
14
10
12
8
11
10
12
12
12
12
8
10
7
10
10
12
11
9
10
12
14
13
10
11
10
10
8
10
10
10
8
8
4
14
8
12
12
10
8
12
12
10
10
12
12
9
11
14
10
8
12
8
10
11
12
10
10
12
8
9
12
8
8
10
10
10
14
10
12
12
13
9
12
12
10
12
6
8
12
10
9
11
11
9
10
15
12
7
7
10
9
10
10
9
12
10
9
12
10
7
12
10
10
12
8
12
10
10
9
8
8
12
12
10
12
10
9
10
10
8
10
12
12
16
10
9
12
12
10
7
12
10
10
6
9
6
18
13
10
12
15
12
12
9
7
12
13
14
13
12
8
8
10
10
8
12
10
12
12
12
9
12
7
12
8
8
12
14
10
5
9
8
13
10
10
14
10
99
10
12
17
14
8
14
12
12
10
10
8
12
12
12
10
12
10
10
12
12
12
12
13
12
8
10
12
8
10
10
12
12
12
12
12
12
14
10
12
14
12
14
12
13
8
12
14
10
10
11
16
12
10
10
99
8
11
12
12
11
10
20
9
14
12
10
12
10
12
12
8
12
12
10
99
12
2
10
10
10
12
12
12
12
88
9
12
14
8
12
10
10
10
7
8
10
1
10
10
9
15
10
12
12
12
11
12
12
14
8
12
12
10
14
8
10
12
10
10
10
12
9
12
11
8
14
12
10
12
10
8
14
12
12
12
8
12
12
10
12
12
12
9
11
10
15
10
9
9
10
7
10
9
10
10
10
15
12
12
10
12
8
12
11
8
14
8
12
10
15
9
13
12
14
12
12
17
10
13
12
12
10
12
10
12
10
10
10
1
8
12
10
10
10
12
12
11
12
8
8
12
12
10
12
9
10
12
12
12
12
10
10
9
12
10
9
12
7
14
10
10
9
10
8
10
12
12
10
9
10
9
12
10
12
10
9
7
12
11
12
9
13
12
12
7
8
12
12
12
11
12
13
10
12
10
12
12
15
12
12
13
10
10
8
11
12
12
12
12
10
10
12
15
12
10
10
7
12
10
11
10
10
10
10
11
7
15
8
10
6
8
9
8
7
10
12
14
11
8
10
8
8
14
12
15
12
12
9
12
12
9
11
15
11
12
7
15
9
10
15
15
8
11
12
10
10
12
7
12
10
11
12
10
10
8
9
8
10
10
14
10
10
12
12
7
12
10
12
9
9
13
14
10
12
12
12
12
12
10
10
8
12
8
14
10
70
12
10
8
8
11
10
8
7
8
50
9
12
12
7
10
8
10
10
10
8
12
7
13
13
8
8
11
6
12
9
12
13
13
12
12
10
8
12
10
10
15
12
10
12
8
8
12
12
10
12
9
12
12
10
9
10
10
10
12
12
12
12
8
10
12
15
10
8
15
10
9
12
99
10
10
11
11
12
12
14
12
14
9
10
12
13
10
11
10
12
12
12
13
14
9
10
10
12
12
10
99
8
99
12
99
12
10
12
10
12
12
12
10
12
12
10
10
12
99
12
12
9
99
12
12
9
12
12
12
15
12
12
12
8
8
12
8
12
12
10
10
12
99
8
8
99
10
10
5
9
9
99
9
10
12




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time48 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 48 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=229773&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]48 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=229773&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=229773&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time48 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean11.47511.70912.05912.32112.60313.00513.210.399280.544
median101010111111120.500221
midrange50505050505051.50.252550
mode121212121212120.109410
mode k.dens8111111.14911.18311.23911.2890.600140.18288

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 11.475 & 11.709 & 12.059 & 12.321 & 12.603 & 13.005 & 13.21 & 0.39928 & 0.544 \tabularnewline
median & 10 & 10 & 10 & 11 & 11 & 11 & 12 & 0.50022 & 1 \tabularnewline
midrange & 50 & 50 & 50 & 50 & 50 & 50 & 51.5 & 0.25255 & 0 \tabularnewline
mode & 12 & 12 & 12 & 12 & 12 & 12 & 12 & 0.10941 & 0 \tabularnewline
mode k.dens & 8 & 11 & 11 & 11.149 & 11.183 & 11.239 & 11.289 & 0.60014 & 0.18288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=229773&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P1[/C][C]P5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P95[/C][C]P99[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]11.475[/C][C]11.709[/C][C]12.059[/C][C]12.321[/C][C]12.603[/C][C]13.005[/C][C]13.21[/C][C]0.39928[/C][C]0.544[/C][/ROW]
[ROW][C]median[/C][C]10[/C][C]10[/C][C]10[/C][C]11[/C][C]11[/C][C]11[/C][C]12[/C][C]0.50022[/C][C]1[/C][/ROW]
[ROW][C]midrange[/C][C]50[/C][C]50[/C][C]50[/C][C]50[/C][C]50[/C][C]50[/C][C]51.5[/C][C]0.25255[/C][C]0[/C][/ROW]
[ROW][C]mode[/C][C]12[/C][C]12[/C][C]12[/C][C]12[/C][C]12[/C][C]12[/C][C]12[/C][C]0.10941[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]8[/C][C]11[/C][C]11[/C][C]11.149[/C][C]11.183[/C][C]11.239[/C][C]11.289[/C][C]0.60014[/C][C]0.18288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=229773&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=229773&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
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean11.47511.70912.05912.32112.60313.00513.210.399280.544
median101010111111120.500221
midrange50505050505051.50.252550
mode121212121212120.109410
mode k.dens8111111.14911.18311.23911.2890.600140.18288



Parameters (Session):
par1 = 750 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 750 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par3 == '0') bw <- NULL
if (par3 != '0') bw <- as.numeric(par3)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
library(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])) / 2
s.mode <- mlv(s[i], method='mfv')$M
s.kernelmode <- mlv(s[i], method='kernel', bw=bw)$M
c(s.mean, s.median, s.midrange, s.mode, s.kernelmode)
}
(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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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')