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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, 20 Jan 2015 18:17:15 +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/20/t1421777845k5iafsyy3i8ntu9.htm/, Retrieved Wed, 15 May 2024 08:41:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=275377, Retrieved Wed, 15 May 2024 08:41:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact37
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Pexx8] [2015-01-20 18:17:15] [4d754bf6e19324217bcb21f53e4ec277] [Current]
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Dataseries X:
-7.34659
-3.83393
0.601079
10.1708
-19.384
76.9922
5.5307
-40.6643
7.89237
17.2903
12.6761
19.6465
-22.8607
24.2146
-102.787
-22.1468
19.5158
-27.5133
32.8225
-23.6236
9.83643
4.60699
70.5428
50.2208
-0.610172
109.513
38.3844
-17.1329
9.89147
8.93507
-50.6089
6.24831
-15.4578
17.4048
-39.2381
-41.6349
5.69034
31.4039
20.6034
4.54633
-32.8318
5.29772
18.8812
21.0418
-34.8082
32.3313
-31.5422
-6.96524
-51.1887
-19.621
16.3293
-54.3249
11.0791
51.4231
-17.4376
-28.8304
23.0113
-6.31347
-53.8408
-12.2495
13.1904
8.39642
-42.7811
59.8085
36.2483
-29.8633
38.3825
76.0114
45.9664
-1.17613
21.4147
-26.7206
-36.2753
-1.78602
-12.253
2.56265
1.16142
9.31058
-27.9482
-35.9069
-16.4696
7.28922
-19.3588
1.19328
22.2468
0.588097
-19.1513
-32.8384
-19.9199
28.5582
-27.2895
20.3171
39.2813
-9.7313
-0.942376
9.60549
18.1714
-4.9384
-7.56233
12.84
-30.4868
-3.639
-17.8985
30.4074
-5.72721
-9.3317
-5.54115
-14.7961
-55.8251
-7.45031
7.46339
3.41547
-77.9853
-70.9246
25.0086
29.244
10.2707
-13.374
60.4465
27.8033
28.7837
-65.8301
18.4629
-89.4987
-19.6233
-11.2502
9.82813
-20.7817
40.6665
-28.5946
-10.6826
26.0439
-9.82614
-7.37667
-50.715
-40.8176
-1.70087
-99.8017
14.1375
-11.3701
-11.5852
43.864
32.0648
1.17527
15.5922
5.30727
0.309518
-25.6243
0.855347
45.826
34.6051
-22.2974
25.725
38.2554
-20.4191
5.13599
36.9151
-22.4416
17.9049
-14.7084
20.6995
4.38463
35.8517
-124.977
64.3234
-31.3808
22.9819
9.22512
7.16268
-6.14016
10.5282
19.031
40.8665
38.6731
38.6731
28.9975
-6.40377
23.1366
56.324
-81.4623
-6.58429
26.0776
19.8611
5.20928
57.0213
9.45682
7.45682
12.4568
16.3065
-34.724
-27.9697
33.4926
-10.2839
22.2461
-13.1051
-18.4536
9.35706
3.31778
-9.62075
18.7282
89.7837
-23.6847
13.0052
21.7361
-3.67581
-28.9609
-76.9933
-32.6912
49.3754
17.3415
-49.6868
25.4617
-21.8287
-12.7843
14.6583
-21.7871
9.07381
48.8847
-33.9381
10.7993
13.2289
-33.8785
-23.4321
7.38082
39.5195
-41.808
9.4401
1.73577
-11.3687
14.4103
36.7927
11.4415
3.58295
42.2508
-29.7848
-79.3484
-47.2454
22.0011
-55.9352
-1.49485
-25.0235
-28.1147
15.2404
64.3234
-1.89631
29.7924
31.3961
-13.8095
-7.75536
10.6631
12.0411
-54.9164
5.60625
-11.4533
36.1205
14.2016
-21.5212
-47.5903
11.8037
25.0071
-31.3808
-9.64807
7.74461
15.2404
8.14127
-17.6636
-23.8619
22.1111
24.4277
-35.2637
25.4359
-11.1429
9.01713
12.6079
19.19
-22.1745
21.192
-67.4088




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=275377&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=275377&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=275377&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 time7 seconds
R Server'George Udny Yule' @ yule.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-4.8841-3.0382-1.03814.2194e-061.50033.13844.80861.972.5383
median-1.6183-0.626781.86724.46555.30737.29157.90282.38263.44
midrange-27.248-23.992-7.732-7.732-0.847954.855710.0078.92526.884
mode-65.986-41.178-15.1221.71417.75951.51686.94730.132.879
mode k.dens-13.5875.37048.722610.48511.76914.30616.0194.38823.0466

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -4.8841 & -3.0382 & -1.0381 & 4.2194e-06 & 1.5003 & 3.1384 & 4.8086 & 1.97 & 2.5383 \tabularnewline
median & -1.6183 & -0.62678 & 1.8672 & 4.4655 & 5.3073 & 7.2915 & 7.9028 & 2.3826 & 3.44 \tabularnewline
midrange & -27.248 & -23.992 & -7.732 & -7.732 & -0.84795 & 4.8557 & 10.007 & 8.9252 & 6.884 \tabularnewline
mode & -65.986 & -41.178 & -15.12 & 21.714 & 17.759 & 51.516 & 86.947 & 30.1 & 32.879 \tabularnewline
mode k.dens & -13.587 & 5.3704 & 8.7226 & 10.485 & 11.769 & 14.306 & 16.019 & 4.3882 & 3.0466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=275377&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]-4.8841[/C][C]-3.0382[/C][C]-1.0381[/C][C]4.2194e-06[/C][C]1.5003[/C][C]3.1384[/C][C]4.8086[/C][C]1.97[/C][C]2.5383[/C][/ROW]
[ROW][C]median[/C][C]-1.6183[/C][C]-0.62678[/C][C]1.8672[/C][C]4.4655[/C][C]5.3073[/C][C]7.2915[/C][C]7.9028[/C][C]2.3826[/C][C]3.44[/C][/ROW]
[ROW][C]midrange[/C][C]-27.248[/C][C]-23.992[/C][C]-7.732[/C][C]-7.732[/C][C]-0.84795[/C][C]4.8557[/C][C]10.007[/C][C]8.9252[/C][C]6.884[/C][/ROW]
[ROW][C]mode[/C][C]-65.986[/C][C]-41.178[/C][C]-15.12[/C][C]21.714[/C][C]17.759[/C][C]51.516[/C][C]86.947[/C][C]30.1[/C][C]32.879[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-13.587[/C][C]5.3704[/C][C]8.7226[/C][C]10.485[/C][C]11.769[/C][C]14.306[/C][C]16.019[/C][C]4.3882[/C][C]3.0466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=275377&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=275377&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-4.8841-3.0382-1.03814.2194e-061.50033.13844.80861.972.5383
median-1.6183-0.626781.86724.46555.30737.29157.90282.38263.44
midrange-27.248-23.992-7.732-7.732-0.847954.855710.0078.92526.884
mode-65.986-41.178-15.1221.71417.75951.51686.94730.132.879
mode k.dens-13.5875.37048.722610.48511.76914.30616.0194.38823.0466



Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')