Free Statistics

of Irreproducible Research!

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 computationSun, 18 Jan 2015 13:49:41 +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/2015/Jan/18/t1421588989etjosfdik97eh7i.htm/, Retrieved Tue, 14 May 2024 05:24:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=274200, Retrieved Tue, 14 May 2024 05:24:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact57
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2015-01-18 13:49:41] [c7f962214140f976f2c4b1bb2571d9df] [Current]
Feedback Forum

Post a new message
Dataseries X:
0.294153
297.156
-271.955
-210.608
515.598
388.145
348.012
-0.793748
0.0474647
0.949459
170.415
353.959
-316.424
270.588
245.478
0.885167
0.432262
136.526
-1.255
242.932
288.737
-242.299
-0.36997
-132.201
179.733
-683.795
113.673
0.968342
129.799
-262.899
0.532419
0.742768
212.812
-0.0241935
0.256096
0.854528
-136.766
0.889595
190.098
-204.375
-0.553176
257.853
0.103225
-0.921825
0.567044
-236.129
-0.183641
0.333117
366.465
-159.683
0.909003
0.806532
-0.386175
-139.188
-172.474
163.465
191.825
-0.283804
-307.451
-119.565
-236.197
-151.284
-352.005
112.206
148.021
-505.159
-157.071
-246.861
156.108
143.552
0.719528
342.149
0.566379
-0.363746
-19.214
-0.056789
301.548
0.632589
137.487
-202.638
0.115316
-0.491874
171.652
0.792612
-0.0500028
106.084
-0.257791
0.293489
-339.489
346.282
0.104144
0.834079
0.760705
-0.886211
0.991821
-0.812128
-0.861605
203.306
0.0326958
175.233
-0.919433
0.872158
-338.395
195.822
-234.112
101.075
20.363
-28.444
0.975645
116.927
-219.003
-224.943
193.964
396.052
0.344235
101.153
0.283773
-106.596
0.296354
-0.483775
0.441724
0.121994
-100.658
0.382647
-180.252
0.786913
154.651
415.924
147.515
-167.088
-141.748
-0.355202
253.319
0.754977
230.513
173.636
0.713535
-0.826256
0.873435
-0.698291
0.318378
225.138
-0.706501
0.692002
148.385
144.925
-239.905
-272.706
-240.806
189.067
0.355387
0.489124
-245.246
-24.997
147.025
0.104144
0.770858
415.924
-273.299
0.0487413
0.520571
0.817289
0.750211
429.504
-203.717
204.186
-0.19388
-0.864039
-37.685
-303.779
0.446489
159.975
-515.257
175.479
251.998
-248.453
-335.533
0.512109
130.535
-229.359
-0.352145
-180.944
0.0351127
-120.099
202.659
120.875
0.216629
0.686974
0.498628
0.817199
-194.897
-128.106
227.819
-165.853
17.838
-226.586
21.398
0.426097
-324.974
-0.945592
-329.269
112.158
278.591
0.297647
0.379211
107.392
-0.692765
321.317
-0.0115483
149.078
-281.386
125.588
-134.621
-396.611
-136.908
158.447
186.706
-0.45464
-205.585
121.455
-309.837
211.414
-231.201
0.0218416
-0.877228
163.328
497.042
-191.696
-151.227
-250.048
0.0158494
-308.591
-0.165203
0.240808
0.874019
-199.099
0.755342
-0.0764699
-461.544
-270.354
-292.081
-285.065
0.241454
-0.409056
12.673
0.219823
0.100763
508.859
-0.195174
0.358255
202.625
107.606
-131.709
-0.952895
0.0362316
-0.868792
-206.858
-259.497
219.119
-478.671
0.0887831
12.742
-292.786
-0.0466467




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=274200&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=274200&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274200&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-29.913-23.581-11.445-4.66813.148915.60825.68511.60414.594
median0.0156490.041760.108330.248780.308010.426250.472720.119580.19968
midrange-127.15-93.376-84.098-84.0980.17055.219527.31843.72284.269
mode-293.02-184.68-17.971208.0182.923356.06429.5149.27100.89
mode k.dens-23.8650.0599320.131140.177770.2553227.60635.71923.9160.12417

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -29.913 & -23.581 & -11.445 & -4.6681 & 3.1489 & 15.608 & 25.685 & 11.604 & 14.594 \tabularnewline
median & 0.015649 & 0.04176 & 0.10833 & 0.24878 & 0.30801 & 0.42625 & 0.47272 & 0.11958 & 0.19968 \tabularnewline
midrange & -127.15 & -93.376 & -84.098 & -84.098 & 0.1705 & 5.2195 & 27.318 & 43.722 & 84.269 \tabularnewline
mode & -293.02 & -184.68 & -17.971 & 208.01 & 82.923 & 356.06 & 429.5 & 149.27 & 100.89 \tabularnewline
mode k.dens & -23.865 & 0.059932 & 0.13114 & 0.17777 & 0.25532 & 27.606 & 35.719 & 23.916 & 0.12417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=274200&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]-29.913[/C][C]-23.581[/C][C]-11.445[/C][C]-4.6681[/C][C]3.1489[/C][C]15.608[/C][C]25.685[/C][C]11.604[/C][C]14.594[/C][/ROW]
[ROW][C]median[/C][C]0.015649[/C][C]0.04176[/C][C]0.10833[/C][C]0.24878[/C][C]0.30801[/C][C]0.42625[/C][C]0.47272[/C][C]0.11958[/C][C]0.19968[/C][/ROW]
[ROW][C]midrange[/C][C]-127.15[/C][C]-93.376[/C][C]-84.098[/C][C]-84.098[/C][C]0.1705[/C][C]5.2195[/C][C]27.318[/C][C]43.722[/C][C]84.269[/C][/ROW]
[ROW][C]mode[/C][C]-293.02[/C][C]-184.68[/C][C]-17.971[/C][C]208.01[/C][C]82.923[/C][C]356.06[/C][C]429.5[/C][C]149.27[/C][C]100.89[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-23.865[/C][C]0.059932[/C][C]0.13114[/C][C]0.17777[/C][C]0.25532[/C][C]27.606[/C][C]35.719[/C][C]23.916[/C][C]0.12417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=274200&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=274200&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-29.913-23.581-11.445-4.66813.148915.60825.68511.60414.594
median0.0156490.041760.108330.248780.308010.426250.472720.119580.19968
midrange-127.15-93.376-84.098-84.0980.17055.219527.31843.72284.269
mode-293.02-184.68-17.971208.0182.923356.06429.5149.27100.89
mode k.dens-23.8650.0599320.131140.177770.2553227.60635.71923.9160.12417



Parameters (Session):
par1 = 0.95 ; par2 = 20 ;
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')