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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, 01 Feb 2018 07:48:32 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Feb/01/t1517467733krxyo5hz9n7vncr.htm/, Retrieved Mon, 29 Apr 2024 01:41:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=313164, Retrieved Mon, 29 Apr 2024 01:41:35 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
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Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2018-02-01 06:48:32] [4bbd12ea3a6c2ab532848261ff0d9984] [Current]
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Dataseries X:
-7.34658641160771
-3.83393270509369
0.601079165691683
10.1708123315412
-19.3839869794366
76.9922425740188
5.53070349660019
-40.6642925799496
7.8923727519013
17.2902637570394
12.6760697817719
19.6464948843585
-22.8607017650403
24.2145566602915
-102.787405880591
-22.146815361688
19.5158080760987
-27.5133483228973
32.8225102202477
-23.6235769736771
9.83643314161895
4.60698806707138
70.5427891086114
50.22081902765
-0.610172080927745
109.512600045334
38.3844237614283
-17.1329178076636
9.8914651620462
8.93506884166259
-50.6089193933982
6.24830624992741
-15.4577854355838
17.4048085530475
-39.2380533193169
-41.6349321334386
5.69033922999012
31.4039155628394
20.6034212187875
4.54632743294802
-32.8317960857245
5.29772239563854
18.8811727936388
21.0417879652906
-34.8082213857578
32.3312855186524
-31.5422133188891
-6.96524384146907
-51.1887131351437
-19.6210349753855
16.3293278006868
-54.3249050913562
11.0791037716048
51.4231162089253
-17.4375959401962
-28.8303943807569
23.0112582539784
-6.31347225533266
-53.84077421835
-12.249523516243
13.1904099411244
8.39642203599641
-42.7811420681512
59.8084957547755
36.2483062499274
-29.8633361331903
38.3824605416578
76.0113792797905
45.9664338814935
-1.17612597762016
21.4146723322159
-26.7205970597838
-36.2752826830959
-1.78601535890883
-12.2530318706035
2.56265100567706
1.16142391932059
9.31058249798772
-27.9481685557842
-35.9069347340566
-16.4696314381812
7.28921972470831
-19.3587966655329
1.19328162042654
22.2468389243388
0.588096592448664
-19.1512627690298
-32.8384189427771
-19.9199397354674
28.5582080595397
-27.2895444904127
20.3171435437495
39.2813121694082
-9.73130430948931
-0.942375677488173
9.60548926754502
18.1714456892322
-4.93840200425281
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12.8399739909504
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30.4073651037041
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3.41546646113856
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25.0086309727327
29.2440128930041
10.2707157618025
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60.4465269458381
27.8033040934051
28.783673988851
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18.4628713819454
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9.82813335318713
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40.6664595968392
-28.5945915143623
-10.6826220633439
26.0439497578208
-9.82613598785401
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-1.70087235956341
-99.8017389500321
14.1375276385751
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-11.5851921361629
43.8639647275284
32.0648275157806
1.17526571711362
15.5921920420822
5.30726906493567
0.309518089321935
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0.855347084597148
45.82596028482
34.6050928277554
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25.724966525478
38.2553520465631
-20.4191182276973
5.13598732690237
36.9150764336932
-22.4415575475537
17.9049156953637
-14.7084067380213
20.6994781446868
4.38462512248504
35.8516667812385
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64.3234036524464
-31.3808056049636
22.9818734549562
9.22512141838141
7.16267812199563
-6.14015849546619
10.5281938816587
19.0310230212635
40.8664924012808
38.6731382905774
38.6731382905774
28.9974598982497
-6.40376507741122
23.1365593461027
56.3239790494825
-81.4622605682183
-6.58429493929693
26.0776190049686
19.861114006955
5.20928321135139
57.0212696008941
9.45682352090344
7.45682352090344
12.4568235209034
16.306500717659
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-27.969678691805
33.4925597898478
-10.2838683863397
22.2460928279007
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9.35706193361561
3.31777871462932
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31.3960625611379
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12.0411114253833
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36.1205139843701
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8.14127045873426
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22.111100502846
24.4276514774178
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9.01713310814046
12.6078628048154
19.1899681873745
-22.1745442232816
21.1920012076042
-67.4087619189757




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R ServerBig 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313164&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]11 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=313164&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313164&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 Outputview raw output of R engine
Computing time11 seconds
R ServerBig Analytics Cloud Computing Center







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-3.9555-2.7561-1.56251.753e-151.41583.14234.78531.94092.9783
median-1.3371-0.0110381.45554.46555.4197.3357.8272.34473.9635
midrange-27.217-23.992-13.388-7.73233.36264.855410.0079.057216.751
mode-66.091-41.997-15.21821.71421.04445.87464.32328.96236.262
mode k.dens-4.37694.63158.788510.48511.62413.78217.2214.17042.8351

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -3.9555 & -2.7561 & -1.5625 & 1.753e-15 & 1.4158 & 3.1423 & 4.7853 & 1.9409 & 2.9783 \tabularnewline
median & -1.3371 & -0.011038 & 1.4555 & 4.4655 & 5.419 & 7.335 & 7.827 & 2.3447 & 3.9635 \tabularnewline
midrange & -27.217 & -23.992 & -13.388 & -7.7323 & 3.3626 & 4.8554 & 10.007 & 9.0572 & 16.751 \tabularnewline
mode & -66.091 & -41.997 & -15.218 & 21.714 & 21.044 & 45.874 & 64.323 & 28.962 & 36.262 \tabularnewline
mode k.dens & -4.3769 & 4.6315 & 8.7885 & 10.485 & 11.624 & 13.782 & 17.221 & 4.1704 & 2.8351 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313164&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]-3.9555[/C][C]-2.7561[/C][C]-1.5625[/C][C]1.753e-15[/C][C]1.4158[/C][C]3.1423[/C][C]4.7853[/C][C]1.9409[/C][C]2.9783[/C][/ROW]
[ROW][C]median[/C][C]-1.3371[/C][C]-0.011038[/C][C]1.4555[/C][C]4.4655[/C][C]5.419[/C][C]7.335[/C][C]7.827[/C][C]2.3447[/C][C]3.9635[/C][/ROW]
[ROW][C]midrange[/C][C]-27.217[/C][C]-23.992[/C][C]-13.388[/C][C]-7.7323[/C][C]3.3626[/C][C]4.8554[/C][C]10.007[/C][C]9.0572[/C][C]16.751[/C][/ROW]
[ROW][C]mode[/C][C]-66.091[/C][C]-41.997[/C][C]-15.218[/C][C]21.714[/C][C]21.044[/C][C]45.874[/C][C]64.323[/C][C]28.962[/C][C]36.262[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-4.3769[/C][C]4.6315[/C][C]8.7885[/C][C]10.485[/C][C]11.624[/C][C]13.782[/C][C]17.221[/C][C]4.1704[/C][C]2.8351[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=313164&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313164&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-3.9555-2.7561-1.56251.753e-151.41583.14234.78531.94092.9783
median-1.3371-0.0110381.45554.46555.4197.3357.8272.34473.9635
midrange-27.217-23.992-13.388-7.73233.36264.855410.0079.057216.751
mode-66.091-41.997-15.21821.71421.04445.87464.32328.96236.262
mode k.dens-4.37694.63158.788510.48511.62413.78217.2214.17042.8351



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;
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)
}
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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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')