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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 computationThu, 11 Dec 2014 13:27:21 +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/11/t14183045198zoxs87gcfjyd8x.htm/, Retrieved Thu, 16 May 2024 12:28:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265975, Retrieved Thu, 16 May 2024 12:28:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
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Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2014-12-11 13:27:21] [26b3f07cb5f54f7efd4618e9d9764016] [Current]
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Dataseries X:
-0.203371
1.83954
-3.99842
-3.54979
1.99535
2.47494
0.491011
-1.06717
-2.40548
0.497605
-4.96832
-1.19681
-3.32339
1.45495
0.0324611
-1.52205
-0.895006
4.27043
-0.532763
0.344667
-0.766884
3.30913
-1.76993
0.667426
-2.96608
0.611989
-1.13864
0.268181
-0.0694413
4.24212
-0.2916
1.66636
-0.169951
2.75267
-0.286787
1.80494
1.12226
2.94223
2.78156
-0.234387
4.30637
-3.39222
2.62824
-3.48627
-1.77293
5.03519
-0.131355
1.33328
0.961863
3.08047
0.87483
-3.40977
-0.0697041
-0.353665
-4.67409
-0.267995
1.90704
-3.57494
-1.11979
-0.527631
-0.134618
-3.1628
1.73693
-8.62592
-2.1841
3.96131
3.54727
-2.74729
0.516192
0.665353
1.96648
-4.34254
0.198455
2.28418
1.80142
-3.45617
-0.59196
2.05281
-1.51061
3.38987
0.645193
1.11523
-1.05139
0.913353
0.3663
-1.1055
-1.76182
3.44819
-4.76006
1.60361
-0.164952
-1.46406
5.11849
0.807546
3.18599
0.507788
0.726992
-0.290257
-0.817927
-0.0100989
4.00474
-4.57783
-0.426452
3.76331
1.50891
1.31086
-1.03203
1.89323
0.444468
-2.24862
-2.51039
1.39191
1.90086
0.722152
2.60669
2.59014
-1.63463
1.89309
2.78846
0.164052
-4.39915
1.35566
2.70636
-0.481726
1.4205
1.44723
2.94503
-1.89198
3.95074
0.768292
-6.26729
-3.85708
-0.76368
3.36079
-0.120031
-0.436506
0.219161
3.45397
-1.56933
-0.495751
-1.02855
-3.1601
1.27308
-3.32889
3.97453
-0.699685
-0.00667291
-1.51653
0.0312629
1.38456
1.2778
3.36525
0.128356
-1.02979
1.05131
0.0723306
0.818199
-4.1199
-0.729021
3.42905
1.92781
-2.77076
1.67204
-3.84477
-0.451414
-0.508117
-1.20322
1.72267
4.15216
-2.86415
-0.188474
3.31753
-6.05261
-0.898591
3.272
3.69014
1.28913
3.45901
2.23509
1.70036
3.92907
5.43171
1.33328
-0.0097872
-0.439357
-2.82121
-8.79276
-1.31713
-0.6039
-2.44398
-0.435604
-2.49312
-2.95265
1.53202
2.71388
-1.87747
0.0818738
0.611181
0.623405
-0.256333
0.197445
-0.993439
-3.84957
0.0675354
-0.719349
-1.2423
0.736092
-5.0831
-6.41785
-2.72363
-5.87623
-1.85687
-2.3713
-3.05836
1.5444
1.99207
-0.710742
-0.0456103
2.16158
2.75577
-2.48705
2.3506
-0.786099
0.557902
1.49737
0.677204
-0.208789
1.40129




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265975&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265975&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265975&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.42345-0.2652-0.091567-3.0307e-080.121120.286440.356710.16530.21268
median-0.22205-0.16758-0.0385840.0499980.180750.446050.645190.183540.21933
midrange-2.2434-1.8788-1.8371-1.6805-1.5971-0.49307-0.417790.449490.24003
mode-4.5797-3.4894-0.934391.33331.33333.19843.76721.8752.2677
mode k.dens-0.57395-0.39385-0.23928-0.0950180.0256361.03161.53530.417620.26491

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.42345 & -0.2652 & -0.091567 & -3.0307e-08 & 0.12112 & 0.28644 & 0.35671 & 0.1653 & 0.21268 \tabularnewline
median & -0.22205 & -0.16758 & -0.038584 & 0.049998 & 0.18075 & 0.44605 & 0.64519 & 0.18354 & 0.21933 \tabularnewline
midrange & -2.2434 & -1.8788 & -1.8371 & -1.6805 & -1.5971 & -0.49307 & -0.41779 & 0.44949 & 0.24003 \tabularnewline
mode & -4.5797 & -3.4894 & -0.93439 & 1.3333 & 1.3333 & 3.1984 & 3.7672 & 1.875 & 2.2677 \tabularnewline
mode k.dens & -0.57395 & -0.39385 & -0.23928 & -0.095018 & 0.025636 & 1.0316 & 1.5353 & 0.41762 & 0.26491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265975&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]-0.42345[/C][C]-0.2652[/C][C]-0.091567[/C][C]-3.0307e-08[/C][C]0.12112[/C][C]0.28644[/C][C]0.35671[/C][C]0.1653[/C][C]0.21268[/C][/ROW]
[ROW][C]median[/C][C]-0.22205[/C][C]-0.16758[/C][C]-0.038584[/C][C]0.049998[/C][C]0.18075[/C][C]0.44605[/C][C]0.64519[/C][C]0.18354[/C][C]0.21933[/C][/ROW]
[ROW][C]midrange[/C][C]-2.2434[/C][C]-1.8788[/C][C]-1.8371[/C][C]-1.6805[/C][C]-1.5971[/C][C]-0.49307[/C][C]-0.41779[/C][C]0.44949[/C][C]0.24003[/C][/ROW]
[ROW][C]mode[/C][C]-4.5797[/C][C]-3.4894[/C][C]-0.93439[/C][C]1.3333[/C][C]1.3333[/C][C]3.1984[/C][C]3.7672[/C][C]1.875[/C][C]2.2677[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.57395[/C][C]-0.39385[/C][C]-0.23928[/C][C]-0.095018[/C][C]0.025636[/C][C]1.0316[/C][C]1.5353[/C][C]0.41762[/C][C]0.26491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265975&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265975&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-0.42345-0.2652-0.091567-3.0307e-080.121120.286440.356710.16530.21268
median-0.22205-0.16758-0.0385840.0499980.180750.446050.645190.183540.21933
midrange-2.2434-1.8788-1.8371-1.6805-1.5971-0.49307-0.417790.449490.24003
mode-4.5797-3.4894-0.934391.33331.33333.19843.76721.8752.2677
mode k.dens-0.57395-0.39385-0.23928-0.0950180.0256361.03161.53530.417620.26491



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')