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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 computationThu, 18 Dec 2014 17:06:07 +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/18/t1418924353i86zbw5j5t9fk8e.htm/, Retrieved Sun, 19 May 2024 17:03:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271170, Retrieved Sun, 19 May 2024 17:03:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2014-12-18 17:06:07] [feba87b71495ef91d18b6c2716812399] [Current]
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Dataseries X:
-20.0103
9.7238
-11.7416
9.75259
-5.55083
-0.320366
-5.7244
-3.24147
17.2411
21.337
-6.14217
15.9418
-4.75499
-1.46151
14.9843
6.16021
-12.5253
0.0708488
5.38295
15.236
5.82309
5.87092
-1.82017
3.55206
6.71267
-2.13024
0.58214
-0.974793
8.51374
-6.77129
-24.6045
1.43582
0.278207
-13.4254
-6.18907
5.11068
5.26177
2.30334
10.1572
-9.13784
-8.34781
-1.46907
-7.14648
-25.6822
-22.7239
17.4212
-1.46815
6.53869
15.359
-4.22856
-1.55343
1.07467
-9.41178
5.95449
6.49263
0.883404
-17.2033
2.68814
0.0590176
-6.64258
5.61443
2.17344
-11.8402
-10.8801
3.07135
-4.71201
9.00069
5.49482
-7.02547
-15.5301
-3.46804
-1.14747
8.39787
21.1313
-14.9856
-12.383
-10.9545
11.3527
-17.5833
-4.47459
-4.02592
1.22045
-8.09547
4.93943
-9.85972
3.86895
6.62671
8.32527
3.41316
-0.443259
2.94532
-16.9452
10.6284
-20.5673
-1.05396
10.0769
3.27931
4.47008
-5.69767
10.588
2.7374
7.43125
-21.8438
2.43317
0.518565
-0.332044
10.8458
-4.733
10.1769
7.86067
8.00527
14.1001
4.68844
-33.3573
-9.90749
-1.62393
2.76403
7.0529
-1.00883
5.11966
3.54052
1.52897
2.32267
8.0686
-0.453911
-1.066
-17.1699
-2.35055
14.1253
-3.77852
-4.90567
4.54947
-4.63375
7.46809
-18.4607
15.406
-8.33941
-4.62488
2.74744
12.3333
-8.3592
-10.3217
14.6054
-7.78704
-1.70855
-4.6664
25.3994
0.902002
0.615837
0.101931
0.820176
3.2846
2.48447
-22.6277
13.0915
14.9615
16.4707
-1.53971
12.0135
-13.5255
-8.87142
1.61178
-7.40536
9.4446
7.68667
-2.9323
-5.73991
-0.838065
16.0878
5.75811
16.9895
-0.249015
1.06971
3.80367
2.92451
-4.2305
-2.83262
-23.9018
-6.88269
-1.45641
2.82006
-6.21565
-5.40163
1.34174
-16.6684
5.09313
7.41601
7.56244
10.8351
-3.02935
-5.34284
10.4499
10.9755
11.599
-5.67017
2.63862
-9.28199
-1.31292
-2.05871
12.1733
10.4783
16.7962
4.18899
-12.4073
-5.44996
-11.5178
-1.29862
-5.66373
-1.97151
-0.26441
-0.973273
-2.68797
4.3488
2.63272
-1.66586
4.45
-13.4291
-0.686557
-9.58575
-3.22303
-1.23696
3.91756
-0.15096
-5.58504
-6.16955
-1.53986
6.04984
5.97465
0.782091
-3.70614
13.0273
0.274719
5.18653
-8.69762
-0.81313
6.04521
4.82026
7.46733
-3.69529
8.18797
14.4236
-11.7636
3.51596
-0.709141
-1.11413
-1.83407
-10.4226
-8.64429
8.5234
8.17814
-2.16161
-2.52619
-5.22119
-29.1761
-12.2677
4.14454
-2.33823
5.63576
1.15231
-6.63362
6.54058
-0.936218
-2.61261
8.51596
-0.295666
1.36872
-9.48038
16.6812
9.57957
4.56142
3.14183
-14.9254
3.31462
1.06213
6.01922
-6.38438
2.94154
-1.91801




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271170&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271170&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271170&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 time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-1.2162-0.99233-0.45183-1.6532e-060.345880.792661.15160.556130.7977
median-1.1333-0.90884-0.32620.086390.550351.06611.26130.630380.87656
midrange-7.9681-6.113-3.979-3.979-1.88840.397450.400961.87872.0906
mode-23.909-12.041-4.0779-1.6532e-063.204910.19114.6267.227.2828
mode k.dens-2.1484-1.5781-0.903950.0732180.511323.10794.47231.41881.4153

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -1.2162 & -0.99233 & -0.45183 & -1.6532e-06 & 0.34588 & 0.79266 & 1.1516 & 0.55613 & 0.7977 \tabularnewline
median & -1.1333 & -0.90884 & -0.3262 & 0.08639 & 0.55035 & 1.0661 & 1.2613 & 0.63038 & 0.87656 \tabularnewline
midrange & -7.9681 & -6.113 & -3.979 & -3.979 & -1.8884 & 0.39745 & 0.40096 & 1.8787 & 2.0906 \tabularnewline
mode & -23.909 & -12.041 & -4.0779 & -1.6532e-06 & 3.2049 & 10.191 & 14.626 & 7.22 & 7.2828 \tabularnewline
mode k.dens & -2.1484 & -1.5781 & -0.90395 & 0.073218 & 0.51132 & 3.1079 & 4.4723 & 1.4188 & 1.4153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271170&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]-1.2162[/C][C]-0.99233[/C][C]-0.45183[/C][C]-1.6532e-06[/C][C]0.34588[/C][C]0.79266[/C][C]1.1516[/C][C]0.55613[/C][C]0.7977[/C][/ROW]
[ROW][C]median[/C][C]-1.1333[/C][C]-0.90884[/C][C]-0.3262[/C][C]0.08639[/C][C]0.55035[/C][C]1.0661[/C][C]1.2613[/C][C]0.63038[/C][C]0.87656[/C][/ROW]
[ROW][C]midrange[/C][C]-7.9681[/C][C]-6.113[/C][C]-3.979[/C][C]-3.979[/C][C]-1.8884[/C][C]0.39745[/C][C]0.40096[/C][C]1.8787[/C][C]2.0906[/C][/ROW]
[ROW][C]mode[/C][C]-23.909[/C][C]-12.041[/C][C]-4.0779[/C][C]-1.6532e-06[/C][C]3.2049[/C][C]10.191[/C][C]14.626[/C][C]7.22[/C][C]7.2828[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-2.1484[/C][C]-1.5781[/C][C]-0.90395[/C][C]0.073218[/C][C]0.51132[/C][C]3.1079[/C][C]4.4723[/C][C]1.4188[/C][C]1.4153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271170&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271170&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-1.2162-0.99233-0.45183-1.6532e-060.345880.792661.15160.556130.7977
median-1.1333-0.90884-0.32620.086390.550351.06611.26130.630380.87656
midrange-7.9681-6.113-3.979-3.979-1.88840.397450.400961.87872.0906
mode-23.909-12.041-4.0779-1.6532e-063.204910.19114.6267.227.2828
mode k.dens-2.1484-1.5781-0.903950.0732180.511323.10794.47231.41881.4153



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')