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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 computationSat, 13 Dec 2014 17:03:20 +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/13/t1418490262pw6joym9m0lp02t.htm/, Retrieved Thu, 16 May 2024 14:49:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267239, Retrieved Thu, 16 May 2024 14:49:59 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Bootstrap residuals] [2014-12-13 17:03:20] [d0ee3c98d5e00815b38c7c808f1992f4] [Current]
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Dataseries X:
177.134
275.473
140.104
169.678
-77.275
891.108
250.174
-297.809
13.762
208.327
302.016
176.525
-12.416
391.393
-818.973
-458.155
303.575
-110.911
160.706
-167.435
137.753
103.955
854.714
558.286
128.952
112.167
141.272
-420.117
130.918
210.509
-293.421
217.182
-138.866
314.742
-262.607
-332.765
236.806
-277.338
30.49
316.753
-186.579
150.646
323.699
389.869
-647.869
549.751
-217.736
-424.914
-243.735
-451.439
-129.457
-856.943
198.799
204.312
-976.519
-139.128
541.967
980.583
-749.859
-326.996
320.638
350.556
-201.917
757.495
590.233
-58.423
531.191
845.579
641.083
-184.908
482.356
-0.316403
-507.078
-314.234
245.152
400.014
-138.615
288.476
-441.214
-154.647
-279.658
-722.181
-364.142
-280.051
565.309
-212.271
-472.508
-488.874
-328.597
109.981
-48.453
-220.086
940.623
-211.858
-24.147
-636.286
0.692157
-248.617
-22.238
-12.477
-539.797
-231.031
-246.312
504.397
-238.122
-19.33
-204.764
-243.259
-699.893
-231.561
-58.006
-213.486
-75.592
-611.994
315.324
324.898
-11.454
-540.928
727.103
353.949
371.073
-520.572
291.813
-842.642
-188.576
-955.033
241.059
-204.009
667.667
-184.724
-423.932
452.052
-0.357521
-892.989
-380.235
-413.534
412.896
-870.326
324.802
-602.511
-361.014
503.819
349.267
11.473
319.892
299.282
-51.957
-776.226
16.372
558.085
55.29
-32.663
382.525
635.435
-522.208
-311.668
437.115
-106.245
-53.654
-320.529
505.378
-282.126
618.721
-116.524
485.937
-626.507
112.338
-171.696
-166.287
-327.282
-221.452
392.627
299.149
505.787
511.224
0.221179
592.677
574.955
357.948
-552.271
-192.701
169.562
453.324
-679.272
434.323
-256.987
-358.327
194.262
354.889
-469.548
-472.594
-246.451
-440.305
-0.617302
-267.016
-389.551
460.229
-175.834
-26.627
-276.029
104.865
-487.819
332.228
131.642
193.422
-404.628
-637.423
-520.303
339.595
0.912762
-657.278
442.913
115.977
-24.414
495.244
-239.238
-499.999
669.819
-454.119
721.129
391.058
-58.183
325.058
313.475
638.505
-442.521
298.102
-199.447
138.923
463.432
628.653
371.696
-180.676
667.982
-104.313
-546.962
-509.893
42.526
-347.282
-21.911
0.883971
-350.482
-174.501
638.584
-136.735
145.339
629.914
217.804
228.558
28.797
415.738
-156.146
-546.979
-977.775
714.867
827.442
-360.353
-78.239
861.913
732.424
-527.477
-784.179
-266.735
0.00778122
0.222608
-35.317
-191.624
642.386
145.397
-440.801
23.695
-221.131
-464.626
915.522
554.278
204.138
237.446




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267239&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267239&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267239&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-51.196-37.481-9.72475.037622.31548.08655.89424.832.04
median-75.609-58.006-22.238-12.4160.2211813.89243.20125.07722.459
midrange-57.967-42.737-18.5761.4041.40412.77543.9118.21619.98
mode-820.56-509.34-204.285.0376221.22593.98758.19341.15425.5
mode k.dens-254.46-221.8-166.5415.13215.60187.348157.19108.61182.14

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -51.196 & -37.481 & -9.7247 & 5.0376 & 22.315 & 48.086 & 55.894 & 24.8 & 32.04 \tabularnewline
median & -75.609 & -58.006 & -22.238 & -12.416 & 0.22118 & 13.892 & 43.201 & 25.077 & 22.459 \tabularnewline
midrange & -57.967 & -42.737 & -18.576 & 1.404 & 1.404 & 12.775 & 43.91 & 18.216 & 19.98 \tabularnewline
mode & -820.56 & -509.34 & -204.28 & 5.0376 & 221.22 & 593.98 & 758.19 & 341.15 & 425.5 \tabularnewline
mode k.dens & -254.46 & -221.8 & -166.54 & 15.132 & 15.601 & 87.348 & 157.19 & 108.61 & 182.14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267239&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]-51.196[/C][C]-37.481[/C][C]-9.7247[/C][C]5.0376[/C][C]22.315[/C][C]48.086[/C][C]55.894[/C][C]24.8[/C][C]32.04[/C][/ROW]
[ROW][C]median[/C][C]-75.609[/C][C]-58.006[/C][C]-22.238[/C][C]-12.416[/C][C]0.22118[/C][C]13.892[/C][C]43.201[/C][C]25.077[/C][C]22.459[/C][/ROW]
[ROW][C]midrange[/C][C]-57.967[/C][C]-42.737[/C][C]-18.576[/C][C]1.404[/C][C]1.404[/C][C]12.775[/C][C]43.91[/C][C]18.216[/C][C]19.98[/C][/ROW]
[ROW][C]mode[/C][C]-820.56[/C][C]-509.34[/C][C]-204.28[/C][C]5.0376[/C][C]221.22[/C][C]593.98[/C][C]758.19[/C][C]341.15[/C][C]425.5[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-254.46[/C][C]-221.8[/C][C]-166.54[/C][C]15.132[/C][C]15.601[/C][C]87.348[/C][C]157.19[/C][C]108.61[/C][C]182.14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267239&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267239&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-51.196-37.481-9.72475.037622.31548.08655.89424.832.04
median-75.609-58.006-22.238-12.4160.2211813.89243.20125.07722.459
midrange-57.967-42.737-18.5761.4041.40412.77543.9118.21619.98
mode-820.56-509.34-204.285.0376221.22593.98758.19341.15425.5
mode k.dens-254.46-221.8-166.5415.13215.60187.348157.19108.61182.14



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')