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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, 13 Nov 2014 18:27:03 +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/Nov/13/t1415903239zgx5e5frkfiz65u.htm/, Retrieved Sun, 19 May 2024 10:08:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=254546, Retrieved Sun, 19 May 2024 10:08:29 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
- R  D    [Bootstrap Plot - Central Tendency] [ws7] [2014-11-13 18:27:03] [e9c088684c75c49d5852bf7a76ea4083] [Current]
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Dataseries X:
-211.377
162.659
-124.809
-302.809
948.726
19.426
840.414
-158.916
-228.244
0.851045
-0.360797
-23.704
-0.986595
153.982
0.812351
0.082788
111.946
150.256
-314.541
0.545672
-106.176
-142.175
-156.273
-126.661
119.856
-673.545
-0.333925
175.566
203.471
-262.398
-228.299
-0.382925
-101.934
-0.624919
-0.0244947
-434.509
21.962
-0.436158
-0.825006
-387.832
-310.309
243.302
-398.882
-171.861
-149.985
-269.772
-372.161
-125.563
42.709
-256.223
-160.474
0.465273
196.112
-0.969953
-477.282
220.375
171.293
-371.927
-438.384
0.459231
110.493
-128.626
-262.707
-0.415303
-0.260549
-425.689
1.834
-228.382
-0.0121782
156.911
-0.788538
209.814
142.428
-212.916
-197.728
524.104
214.003
312.924
0.56089
-55.563
-0.93046
-331.904
0.509069
-0.45635
0.228635
0.731929
-118.701
0.145049
380.745
165.512
0.543997
0.988396
246.021
-115.405
-0.018439
154.144
-1.676
0.749892
-236.223
-0.458006
171.229
164.389
-45.951
402.201
217.768
-0.126093
386.451
-180.596
646.585
232.036
-0.047381
-185.524
-0.240378
177.236
-0.463133
-0.21987
-0.592045
-448.776
313.808
-0.76342
121.427
-0.494876
-392.769
-119.471
-231.862
-180.878
-0.38355
185.838
0.382216
-286.527
237.688
-192.453
174.833
-0.270601
304.365
641.317
0.387799
-0.764125
-215.154
-249.602
-345.325
278.629
-0.173696
-0.0432014
-0.0344116
0.848073
-300.692
-328.598
221.334
0.593313
387.359
-272.697
-256.981
213.703
39.858
0.543997
0.333075
185.838
-403.888
370.604
17.475
733.833
10.124
92.764
199.121
593.146
-0.980061
-0.942786
-0.279144
126.126
350.741
0.481168
-439.316
17.181
118.809
-455.051
-112.299
288.395
-215.201
-133.349
-294.118
-552.027
-11.531
-248.216
-0.678616
544.613
125.409
0.261203
-143.738
455.404
-427.998
-272.896
190.415
0.305804
13.531
-253.925
634.699
279.858
0.902978
-0.667428
0.185587
-0.975019
-0.188145
-104.664
-150.271
0.707313
-0.273833
233.671
-0.234915
0.972595
-0.339769
-0.0886144
350.203
-275.269
162.594
-0.0318217
0.897482
-0.437463
381.627
222.594
-312.392
-0.459963
106.037
-379.536
219.481
-217.932
0.247934
-320.275
489.235
-267.488
-143.997
-147.114
-428.241
3.771
-286.969
-164.354
171.396
-208.022
-497.526
-366.011
-419.274
0.326123
-0.119846
0.869543
226.227
407.807
0.748261
791.382
240.876
-0.236992
0.936757
295.233
-320.266
-0.795812
206.391
0.113795
463.661
0.529048
225.051
-846.241
-175.167
-0.972249
308.095
-13.944




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254546&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]10 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=254546&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254546&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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-39.115-28.454-14.696-4.82883.011117.58929.73914.29517.707
median-0.43682-0.37186-0.24877-0.14989-0.0344120.13020.316630.158840.21436
midrange-56.7-27.4345.96851.243137.59198.35246.8471.08991.623
mode-434.72-370.34-104.293.191103.27322.39733.83208.22207.47
mode k.dens-301.7-150.09-0.106050.1817347.9566.129172.1681.1548.056

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -39.115 & -28.454 & -14.696 & -4.8288 & 3.0111 & 17.589 & 29.739 & 14.295 & 17.707 \tabularnewline
median & -0.43682 & -0.37186 & -0.24877 & -0.14989 & -0.034412 & 0.1302 & 0.31663 & 0.15884 & 0.21436 \tabularnewline
midrange & -56.7 & -27.43 & 45.968 & 51.243 & 137.59 & 198.35 & 246.84 & 71.089 & 91.623 \tabularnewline
mode & -434.72 & -370.34 & -104.2 & 93.191 & 103.27 & 322.39 & 733.83 & 208.22 & 207.47 \tabularnewline
mode k.dens & -301.7 & -150.09 & -0.10605 & 0.18173 & 47.95 & 66.129 & 172.16 & 81.15 & 48.056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254546&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]-39.115[/C][C]-28.454[/C][C]-14.696[/C][C]-4.8288[/C][C]3.0111[/C][C]17.589[/C][C]29.739[/C][C]14.295[/C][C]17.707[/C][/ROW]
[ROW][C]median[/C][C]-0.43682[/C][C]-0.37186[/C][C]-0.24877[/C][C]-0.14989[/C][C]-0.034412[/C][C]0.1302[/C][C]0.31663[/C][C]0.15884[/C][C]0.21436[/C][/ROW]
[ROW][C]midrange[/C][C]-56.7[/C][C]-27.43[/C][C]45.968[/C][C]51.243[/C][C]137.59[/C][C]198.35[/C][C]246.84[/C][C]71.089[/C][C]91.623[/C][/ROW]
[ROW][C]mode[/C][C]-434.72[/C][C]-370.34[/C][C]-104.2[/C][C]93.191[/C][C]103.27[/C][C]322.39[/C][C]733.83[/C][C]208.22[/C][C]207.47[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-301.7[/C][C]-150.09[/C][C]-0.10605[/C][C]0.18173[/C][C]47.95[/C][C]66.129[/C][C]172.16[/C][C]81.15[/C][C]48.056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254546&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254546&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-39.115-28.454-14.696-4.82883.011117.58929.73914.29517.707
median-0.43682-0.37186-0.24877-0.14989-0.0344120.13020.316630.158840.21436
midrange-56.7-27.4345.96851.243137.59198.35246.8471.08991.623
mode-434.72-370.34-104.293.191103.27322.39733.83208.22207.47
mode k.dens-301.7-150.09-0.106050.1817347.9566.129172.1681.1548.056



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')