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Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationWed, 17 Dec 2014 16:10:44 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418832676gkm1sudhqyz8895.htm/, Retrieved Thu, 16 May 2024 21:52:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270485, Retrieved Thu, 16 May 2024 21:52:14 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact64
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
- R PD  [Bootstrap Plot - Central Tendency] [] [2014-12-15 18:30:21] [6656361aa4da5489a6a45e803df0211c]
-   PD      [Bootstrap Plot - Central Tendency] [] [2014-12-17 16:10:44] [457d039f1491608548baeb848eb0333c] [Current]
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Dataseries X:
-210.474
-358.671
-160.138
-419.181
-0.287994
-434.614
-439.097
-786.624
-200.419
0.404877
-0.649392
-266.468
396.908
332.335
561.236
-0.39219
-201.699
306.183
-183.113
-163.003
254.664
101.801
339.287
-0.0492666
-128.135
384.466
-281.841
-0.809796
0.969593
137.996
31.944
0.126517
561.287
10.213
149.873
-436.245
0.709693
-166.996
0.281807
414.557
218.327
273.253
-0.556159
-0.650341
213.572
-294.076
277.612
288.435
12.155
334.778
335.814
-309.134
-330.645
17.265
-148.888
-664.536
-419.244
-19.244
-250.885
-110.488
-370.346
-376.905
-363.254
-0.896259
-309.049
-110.779
-44.627
-565.743
-356.552
0.304758
-0.222915
-148.491
412.497
-0.707829
137.128
185.254
529.851
239.675
0.420814
-0.73879
-0.749578
-187.517
204.715
243.286
114.751
507.858
-661.884
140.849
-0.674143
-143.258
3.36
556.938
645.432
176.743
517.354
458.216
316.513
-108.839
218.179
186.589
-126.842
-394.909
-0.816292




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 17 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270485&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270485&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270485&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 time17 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-71.633-46.189-13.6470.8865423.57154.40869.40330.57437.217
median-45.286-0.74958-0.64939-0.287990.281810.7096917.41213.7430.9312
midrange-112.69-112.69-70.596-70.596-9.552-2.228103.1742.64661.044
mode-566.7-370.67-110.780.88654206.37422.22556.98242.42317.15
mode k.dens-137.62-110.78-67.101107.7497.768131.76154.1786.413164.87

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -71.633 & -46.189 & -13.647 & 0.88654 & 23.571 & 54.408 & 69.403 & 30.574 & 37.217 \tabularnewline
median & -45.286 & -0.74958 & -0.64939 & -0.28799 & 0.28181 & 0.70969 & 17.412 & 13.743 & 0.9312 \tabularnewline
midrange & -112.69 & -112.69 & -70.596 & -70.596 & -9.552 & -2.228 & 103.17 & 42.646 & 61.044 \tabularnewline
mode & -566.7 & -370.67 & -110.78 & 0.88654 & 206.37 & 422.22 & 556.98 & 242.42 & 317.15 \tabularnewline
mode k.dens & -137.62 & -110.78 & -67.101 & 107.74 & 97.768 & 131.76 & 154.17 & 86.413 & 164.87 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270485&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]-71.633[/C][C]-46.189[/C][C]-13.647[/C][C]0.88654[/C][C]23.571[/C][C]54.408[/C][C]69.403[/C][C]30.574[/C][C]37.217[/C][/ROW]
[ROW][C]median[/C][C]-45.286[/C][C]-0.74958[/C][C]-0.64939[/C][C]-0.28799[/C][C]0.28181[/C][C]0.70969[/C][C]17.412[/C][C]13.743[/C][C]0.9312[/C][/ROW]
[ROW][C]midrange[/C][C]-112.69[/C][C]-112.69[/C][C]-70.596[/C][C]-70.596[/C][C]-9.552[/C][C]-2.228[/C][C]103.17[/C][C]42.646[/C][C]61.044[/C][/ROW]
[ROW][C]mode[/C][C]-566.7[/C][C]-370.67[/C][C]-110.78[/C][C]0.88654[/C][C]206.37[/C][C]422.22[/C][C]556.98[/C][C]242.42[/C][C]317.15[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-137.62[/C][C]-110.78[/C][C]-67.101[/C][C]107.74[/C][C]97.768[/C][C]131.76[/C][C]154.17[/C][C]86.413[/C][C]164.87[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270485&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270485&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
mean-71.633-46.189-13.6470.8865423.57154.40869.40330.57437.217
median-45.286-0.74958-0.64939-0.287990.281810.7096917.41213.7430.9312
midrange-112.69-112.69-70.596-70.596-9.552-2.228103.1742.64661.044
mode-566.7-370.67-110.780.88654206.37422.22556.98242.42317.15
mode k.dens-137.62-110.78-67.101107.7497.768131.76154.1786.413164.87



Parameters (Session):
par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 200 ; 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')