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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 computationTue, 11 Nov 2014 08:58:17 +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/11/t141569632529k3522sklrznwx.htm/, Retrieved Sun, 19 May 2024 12:02:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253517, Retrieved Sun, 19 May 2024 12:02:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [WS7: bootstrap] [2014-11-11 08:58:17] [21b927ddce509724d48ffb8407994bd0] [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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253517&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253517&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253517&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'Herman Ole Andreas Wold' @ wold.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-38.917-29.985-14.781-4.82885.506122.46432.81915.87820.287
median-0.43701-0.34015-0.23492-0.14989-0.0318220.207930.327590.166310.20309
midrange-56.64-27.4321.8851.243124.16198.35225.669.634102.28
mode-419.63-277.02-81.67293.191114.4343593.63194.14196.07
mode k.dens-317.19-218.39-24.6730.1817347.58765.099167.9393.48672.26

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -38.917 & -29.985 & -14.781 & -4.8288 & 5.5061 & 22.464 & 32.819 & 15.878 & 20.287 \tabularnewline
median & -0.43701 & -0.34015 & -0.23492 & -0.14989 & -0.031822 & 0.20793 & 0.32759 & 0.16631 & 0.20309 \tabularnewline
midrange & -56.64 & -27.43 & 21.88 & 51.243 & 124.16 & 198.35 & 225.6 & 69.634 & 102.28 \tabularnewline
mode & -419.63 & -277.02 & -81.672 & 93.191 & 114.4 & 343 & 593.63 & 194.14 & 196.07 \tabularnewline
mode k.dens & -317.19 & -218.39 & -24.673 & 0.18173 & 47.587 & 65.099 & 167.93 & 93.486 & 72.26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253517&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]-38.917[/C][C]-29.985[/C][C]-14.781[/C][C]-4.8288[/C][C]5.5061[/C][C]22.464[/C][C]32.819[/C][C]15.878[/C][C]20.287[/C][/ROW]
[ROW][C]median[/C][C]-0.43701[/C][C]-0.34015[/C][C]-0.23492[/C][C]-0.14989[/C][C]-0.031822[/C][C]0.20793[/C][C]0.32759[/C][C]0.16631[/C][C]0.20309[/C][/ROW]
[ROW][C]midrange[/C][C]-56.64[/C][C]-27.43[/C][C]21.88[/C][C]51.243[/C][C]124.16[/C][C]198.35[/C][C]225.6[/C][C]69.634[/C][C]102.28[/C][/ROW]
[ROW][C]mode[/C][C]-419.63[/C][C]-277.02[/C][C]-81.672[/C][C]93.191[/C][C]114.4[/C][C]343[/C][C]593.63[/C][C]194.14[/C][C]196.07[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-317.19[/C][C]-218.39[/C][C]-24.673[/C][C]0.18173[/C][C]47.587[/C][C]65.099[/C][C]167.93[/C][C]93.486[/C][C]72.26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253517&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253517&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-38.917-29.985-14.781-4.82885.506122.46432.81915.87820.287
median-0.43701-0.34015-0.23492-0.14989-0.0318220.207930.327590.166310.20309
midrange-56.64-27.4321.8851.243124.16198.35225.669.634102.28
mode-419.63-277.02-81.67293.191114.4343593.63194.14196.07
mode k.dens-317.19-218.39-24.6730.1817347.58765.099167.9393.48672.26



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')