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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 computationMon, 15 Dec 2014 18:43:09 +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/15/t1418669006dx5h0yuman64ka8.htm/, Retrieved Sun, 19 May 2024 14:44:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268883, Retrieved Sun, 19 May 2024 14:44:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-12-10 11:43:21] [fcb217fc07c8c85d04ee916c1b1c9b9b]
-   PD    [Bootstrap Plot - Central Tendency] [Paper Kaat Van de...] [2014-12-15 18:43:09] [f89ab3f0b580871a9b630460155ef2b6] [Current]
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Dataseries X:
134.415
0.993813
120.513
-433.172
-416.345
-139.044
290.748
337.113
-0.737296
-413.434
-0.874246
-539.936
0.203388
-0.426899
-152.672
212.347
-0.161866
-138.366
-152.412
-0.253019
-509.045
227.076
-348.498
102.098
-0.0471752
-370.274
423.582
399.878
-172.114
-0.0448377
-106.305
-0.184697
-222.549
-215.913
0.715443
0.594442
-0.763758
204.887
-284.322
-0.438035
-426.876
-0.21342
0.899454
-152.445
423.927
-17.724
194.215
-127.262
311.419
299.859
11.081
-0.920907
-0.852172
0.102687
267.286
-0.836934
-515.842
349.473
0.662026
325.512
-314.785
0.415432
-209.131
243.001
-354.291
0.672637
-157.467
-0.87682
171.688
244.883
-0.447797
0.786381
-0.184602
491.438
16.526
113.094
243.515
110.534
153.111
-253.067
0.943287
3.549
154.044
-523.081
0.177445
178.513
249.032
26.376
-0.828565
240.561
380.557
-471.689
-132.783
-106.144
-205.479
-146.217
-380.244
154.985
362.531
-207.854
197.873
0.475598
-0.256584
-0.967893
457.457
-0.705737
443.822
-437.208
-156.216
0.150375
13.188
151.214
-775.197
0.303348
198.842
135.526
175.522
-0.021928
-117.767
285.437
-0.212648
0.497234
234.111
255.938
-355.656
-196.635
0.426012
-0.238748
361.634
-0.224126
-0.338173
-0.253981
0.216842
0.539828
-0.791206
-0.850598
204.496
-354.351
213.775
126.795
-149.195
140.379
0.167323
207.507
0.253032
185.538
-0.895694
0.129129
-0.620308
104.253
-685.098
129.424
17.279
-0.336383
119.315
-0.0576225
201.017
0.748739
-0.328027
-129.779
345.906
120.278
0.434555
486.262
334.622
-102.028
307.818
116.424
0.69539
-226.354
133.271
354.633
-1.258
-189.665
-189.665
316.299
-137.097
26.983
-0.334691
-465.246
-0.340843
-0.889836
253.668
104.133
-285.775
14.343
274.718
-152.956
-222.443
-0.140347
-250.017
148.947
-3.222
405.514
0.491281
113.264
-16.995
10.854
205.109
185.267
0.552589
0.598918
-190.916
0.562509
-210.537
201.183
-210.176
0.170832
472.372
260.122
-0.734551
-0.920121
-207.816
-0.876575
-0.0978597
-139.236
208.129
0.861911
-144.137
0.616767
201.561
-465.144
-252.483
0.369517
254.988
261.872
111.855
205.368
333.199
293.702
283.479
344.273
-0.671102
-369.538
-373.402
-800.245
-0.457782
-197.441
-0.42167
-0.718342
-151.987
-141.771
0.961016
334.622
-248.423
-0.317802
0.98969
-0.578122
0.620783
-203.603
-390.639
-159.295
0.546625
20.231
-376.697
205.755
-471.957
-661.332
-12.526
-594.018
-102.028
-15.879
-326.708
0.961016
260.304
0.274983
0.480188
188.163
186.564
-148.835
0.839936
-0.953047
214.369
288.849
100.618
-0.256903
163.349
-375.046





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=268883&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=268883&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268883&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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-34.142-27.31-11.956-1.7068.203621.54528.71814.84120.16
median-0.32816-0.21303-0.0460060.169080.303350.518530.580960.23330.34935
midrange-178.21-163.94-154.4-154.4-141.88-96.83-51.2923.50612.524
mode-515.84-262.2-93.70810.973101.03334.62406.03178.13194.73
mode k.dens-52.069-39.4730.03575629.65444.97455.87563.77331.27644.938

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -34.142 & -27.31 & -11.956 & -1.706 & 8.2036 & 21.545 & 28.718 & 14.841 & 20.16 \tabularnewline
median & -0.32816 & -0.21303 & -0.046006 & 0.16908 & 0.30335 & 0.51853 & 0.58096 & 0.2333 & 0.34935 \tabularnewline
midrange & -178.21 & -163.94 & -154.4 & -154.4 & -141.88 & -96.83 & -51.29 & 23.506 & 12.524 \tabularnewline
mode & -515.84 & -262.2 & -93.708 & 10.973 & 101.03 & 334.62 & 406.03 & 178.13 & 194.73 \tabularnewline
mode k.dens & -52.069 & -39.473 & 0.035756 & 29.654 & 44.974 & 55.875 & 63.773 & 31.276 & 44.938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268883&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]-34.142[/C][C]-27.31[/C][C]-11.956[/C][C]-1.706[/C][C]8.2036[/C][C]21.545[/C][C]28.718[/C][C]14.841[/C][C]20.16[/C][/ROW]
[ROW][C]median[/C][C]-0.32816[/C][C]-0.21303[/C][C]-0.046006[/C][C]0.16908[/C][C]0.30335[/C][C]0.51853[/C][C]0.58096[/C][C]0.2333[/C][C]0.34935[/C][/ROW]
[ROW][C]midrange[/C][C]-178.21[/C][C]-163.94[/C][C]-154.4[/C][C]-154.4[/C][C]-141.88[/C][C]-96.83[/C][C]-51.29[/C][C]23.506[/C][C]12.524[/C][/ROW]
[ROW][C]mode[/C][C]-515.84[/C][C]-262.2[/C][C]-93.708[/C][C]10.973[/C][C]101.03[/C][C]334.62[/C][C]406.03[/C][C]178.13[/C][C]194.73[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-52.069[/C][C]-39.473[/C][C]0.035756[/C][C]29.654[/C][C]44.974[/C][C]55.875[/C][C]63.773[/C][C]31.276[/C][C]44.938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268883&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268883&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-34.142-27.31-11.956-1.7068.203621.54528.71814.84120.16
median-0.32816-0.21303-0.0460060.169080.303350.518530.580960.23330.34935
midrange-178.21-163.94-154.4-154.4-141.88-96.83-51.2923.50612.524
mode-515.84-262.2-93.70810.973101.03334.62406.03178.13194.73
mode k.dens-52.069-39.4730.03575629.65444.97455.87563.77331.27644.938



Parameters (Session):
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')