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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 computationWed, 10 Dec 2014 11:43:21 +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/10/t141821182138v3ixttf1bnn24.htm/, Retrieved Tue, 28 May 2024 17:35:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264955, Retrieved Tue, 28 May 2024 17:35:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
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] [42cc6d0d468769986f2f8c7c7fdc2d20] [Current]
-   PD    [Bootstrap Plot - Central Tendency] [Paper Kaat Van de...] [2014-12-15 18:43:09] [ab0a3961221f2428d6e871705e34c9c4]
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Dataseries X:
336.509
112.749
157.853
-399.294
-303.349
0.103554
317.475
0.2152
-0.25657
-320.646
-0.314507
-480.913
0.874367
-0.317033
0.812466
-0.319714
0.0298427
-0.951898
-241.071
-113.177
-372.583
195.225
0.571043
206.577
0.631319
-173.563
0.968946
105.607
-0.475612
0.595944
-277.189
0.128433
-0.603156
-341.227
-144.201
-0.617269
-0.178923
0.956469
-204.311
-145.027
-489.562
-0.533301
162.756
0.444857
0.18766
-0.728989
-0.403019
-465.782
310.031
105.963
-0.742378
-505.572
-0.0899311
2.88
127.934
-0.809542
-447.373
427.008
0.0634832
260.356
-0.856597
160.095
-409.077
322.196
0.425749
-134.878
-183.169
0.916656
2.879
148.149
-0.412647
109.711
-128.758
131.179
130.861
0.76464
20.924
0.952533
-26.911
-393.585
-0.958842
281.979
-122.394
-460.005
-178.836
-0.300462
0.126285
-114.066
-237.944
340.774
186.373
-421.099
-0.037355
-1.923
-417.077
-0.196341
-301.901
0.694852
271.477
-438.818
-0.0279733
-0.102165
-151.586
-164.481
198.139
-187.186
179.255
-492.715
-392.746
-202.106
103.959
133.365
-427.413
0.423465
206.151
172.862
0.503139
210.103
-153.314
276.484
-254.112
219.522
0.063779
53.377
-395.448
-148.779
0.184177
-101.952
121.412
0.714348
0.956522
-185.987
0.934641
-0.712778
0.899788
-102.574
3.303
-18.885
-0.142488
-0.0425349
0.444209
343.144
-0.831591
-0.282044
0.42324
0.0774149
0.0258045
0.202742
-386.637
159.361
-694.452
0.902417
155.049
-0.123485
246.989
0.846468
0.334151
113.912
-121.596
-0.829885
251.714
0.905641
0.787197
563.578
300.926
211.358
-0.443579
250.877
-0.301955
-16.746
-0.607228
40.537
-0.950149
0.677328
0.677328
319.467
-0.965234
225.938
-0.337468
-232.381
-0.377868
-12.733
180.271
0.164693
-302.769
166.848
401.756
-114.228
-0.296227
217.477
0.634733
-0.0961583
-200.419
264.571
167.188
188.405
-290.179
111.929
256.308
205.062
276.608
161.042
-207.637
0.068599
-311.963
335.081
-0.986145
415.454
-0.273932
150.819
197.657
-0.81575
-390.475
-0.178014
-113.315
-240.725
179.102
145.124
0.381966
-0.448472
0.913696
-365.159
-109.658
0.744808
338.676
37.601
142.098
368.882
218.918
140.831
260.023
0.846805
161.304
-206.723
-232.883
-554.058
245.024
-274.363
-0.659227
0.28564
-164.425
-235.293
0.947772
300.926
0.0695294
-0.392082
0.273176
-0.631917
-119.645
-175.497
-248.773
-0.69899
135.263
155.885
-193.508
139.992
-32.501
-484.989
-0.35819
-538.652
211.358
0.567627
-0.932286
0.947772
0.733743
-0.0833782
162.305
115.831
326.771
190.088
251.084
135.665
226.554
215.735
-0.514156
0.92588
16.229
0.373985




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264955&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264955&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264955&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-32.288-25.336-16.942-6.57241.970613.25920.83512.41418.912
median-0.11321-0.0899310.0466630.127360.202740.423350.474950.148180.15608
midrange-146.35-139.5-65.437-65.437-61.59912.46329.00348.5763.838
mode-485.19-235.1-73.664128.48105.83261.76401.76163.21179.5
mode k.dens-236.56-0.146920.068380.09503127.619140.38250.267.43427.55

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -32.288 & -25.336 & -16.942 & -6.5724 & 1.9706 & 13.259 & 20.835 & 12.414 & 18.912 \tabularnewline
median & -0.11321 & -0.089931 & 0.046663 & 0.12736 & 0.20274 & 0.42335 & 0.47495 & 0.14818 & 0.15608 \tabularnewline
midrange & -146.35 & -139.5 & -65.437 & -65.437 & -61.599 & 12.463 & 29.003 & 48.576 & 3.838 \tabularnewline
mode & -485.19 & -235.1 & -73.664 & 128.48 & 105.83 & 261.76 & 401.76 & 163.21 & 179.5 \tabularnewline
mode k.dens & -236.56 & -0.14692 & 0.06838 & 0.095031 & 27.619 & 140.38 & 250.2 & 67.434 & 27.55 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264955&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]-32.288[/C][C]-25.336[/C][C]-16.942[/C][C]-6.5724[/C][C]1.9706[/C][C]13.259[/C][C]20.835[/C][C]12.414[/C][C]18.912[/C][/ROW]
[ROW][C]median[/C][C]-0.11321[/C][C]-0.089931[/C][C]0.046663[/C][C]0.12736[/C][C]0.20274[/C][C]0.42335[/C][C]0.47495[/C][C]0.14818[/C][C]0.15608[/C][/ROW]
[ROW][C]midrange[/C][C]-146.35[/C][C]-139.5[/C][C]-65.437[/C][C]-65.437[/C][C]-61.599[/C][C]12.463[/C][C]29.003[/C][C]48.576[/C][C]3.838[/C][/ROW]
[ROW][C]mode[/C][C]-485.19[/C][C]-235.1[/C][C]-73.664[/C][C]128.48[/C][C]105.83[/C][C]261.76[/C][C]401.76[/C][C]163.21[/C][C]179.5[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-236.56[/C][C]-0.14692[/C][C]0.06838[/C][C]0.095031[/C][C]27.619[/C][C]140.38[/C][C]250.2[/C][C]67.434[/C][C]27.55[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264955&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264955&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-32.288-25.336-16.942-6.57241.970613.25920.83512.41418.912
median-0.11321-0.0899310.0466630.127360.202740.423350.474950.148180.15608
midrange-146.35-139.5-65.437-65.437-61.59912.46329.00348.5763.838
mode-485.19-235.1-73.664128.48105.83261.76401.76163.21179.5
mode k.dens-236.56-0.146920.068380.09503127.619140.38250.267.43427.55



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')