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Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationSat, 08 Nov 2014 09:49:52 +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/08/t14154403696j6kc9c3unwbg4g.htm/, Retrieved Sun, 19 May 2024 16:09:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253118, Retrieved Sun, 19 May 2024 16:09:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact206
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] [2014-11-08 09:49:52] [c15d474939d69eac0efd26ce7542850f] [Current]
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Dataseries X:
-1.12915
1.19814
-0.350526
-2.66877
9.32888
1.6214
9.93459
-1.84868
-2.33115
0.49835
-0.561255
-1.16802
-0.47892
2.66376
0.295939
1.06278
1.08969
1.03851
-2.42223
1.65476
-3.00596
-0.874179
-0.16186
-0.228289
0.556501
-6.83157
-0.401632
2.10431
1.9554
-2.25937
-2.44985
0.565526
-0.91088
-1.10172
0.0158956
-5.06817
2.39776
-0.289498
-0.796891
-3.50031
-3.01769
2.36285
-2.3933
-0.528472
-3.34581
-2.60899
-3.31276
-1.27647
4.28993
-2.62574
-1.37154
0.864888
2.46347
-0.187366
-4.64141
-0.538426
1.79638
-3.17015
-4.32754
0.774147
2.15447
-1.34395
-2.78029
0.783906
1.02323
-4.38807
2.59552
-1.83823
0.319833
1.92447
-0.848499
3.13086
1.27872
-1.6582
-2.3029
5.07457
2.43558
3.56448
0.380975
-4.22436
-1.10707
-0.692393
-3.07553
0.603121
-0.30265
0.193688
0.489952
-0.77014
1.46207
4.15595
1.45514
1.95503
0.841168
2.1669
-3.98145
-0.343911
1.95365
-1.80327
0.448734
-2.55534
-0.468088
1.85501
1.45022
-3.99657
3.78113
2.44604
-0.0889025
4.03991
-0.580606
7.61272
1.85603
0.182817
-2.53441
1.69002
1.40792
-0.841567
0.830792
-0.389637
-4.32545
2.31912
0.223467
1.37259
-1.5238
-3.94535
-1.11535
-2.35843
-2.71762
-0.359716
1.4021
0.311555
-2.57886
2.59737
-2.14191
2.16457
-0.367438
2.55551
7.60156
1.02417
0.109089
-1.85724
-2.98978
-2.2889
2.25106
-0.559822
0.061958
-1.28258
1.13165
-2.33256
-3.14328
2.16716
-0.550863
3.58591
-2.56579
-2.52726
2.23791
3.42
1.95503
0.360106
1.4021
-4.45635
2.92925
1.82722




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253118&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253118&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253118&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.47803-0.36598-0.129866.8519e-080.147820.33410.439210.214130.27768
median-0.52847-0.40163-0.29607-0.125380.188250.339970.415190.263670.48433
midrange-0.456690.390581.27231.55152.13042.4382.64790.598870.85808
mode-4.2254-3.3535-1.23841.67861.45212.79294.51312.08122.6905
mode k.dens-2.5698-0.54338-0.212110.187630.912121.79752.06090.839671.1242

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.47803 & -0.36598 & -0.12986 & 6.8519e-08 & 0.14782 & 0.3341 & 0.43921 & 0.21413 & 0.27768 \tabularnewline
median & -0.52847 & -0.40163 & -0.29607 & -0.12538 & 0.18825 & 0.33997 & 0.41519 & 0.26367 & 0.48433 \tabularnewline
midrange & -0.45669 & 0.39058 & 1.2723 & 1.5515 & 2.1304 & 2.438 & 2.6479 & 0.59887 & 0.85808 \tabularnewline
mode & -4.2254 & -3.3535 & -1.2384 & 1.6786 & 1.4521 & 2.7929 & 4.5131 & 2.0812 & 2.6905 \tabularnewline
mode k.dens & -2.5698 & -0.54338 & -0.21211 & 0.18763 & 0.91212 & 1.7975 & 2.0609 & 0.83967 & 1.1242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253118&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]-0.47803[/C][C]-0.36598[/C][C]-0.12986[/C][C]6.8519e-08[/C][C]0.14782[/C][C]0.3341[/C][C]0.43921[/C][C]0.21413[/C][C]0.27768[/C][/ROW]
[ROW][C]median[/C][C]-0.52847[/C][C]-0.40163[/C][C]-0.29607[/C][C]-0.12538[/C][C]0.18825[/C][C]0.33997[/C][C]0.41519[/C][C]0.26367[/C][C]0.48433[/C][/ROW]
[ROW][C]midrange[/C][C]-0.45669[/C][C]0.39058[/C][C]1.2723[/C][C]1.5515[/C][C]2.1304[/C][C]2.438[/C][C]2.6479[/C][C]0.59887[/C][C]0.85808[/C][/ROW]
[ROW][C]mode[/C][C]-4.2254[/C][C]-3.3535[/C][C]-1.2384[/C][C]1.6786[/C][C]1.4521[/C][C]2.7929[/C][C]4.5131[/C][C]2.0812[/C][C]2.6905[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-2.5698[/C][C]-0.54338[/C][C]-0.21211[/C][C]0.18763[/C][C]0.91212[/C][C]1.7975[/C][C]2.0609[/C][C]0.83967[/C][C]1.1242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253118&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253118&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-0.47803-0.36598-0.129866.8519e-080.147820.33410.439210.214130.27768
median-0.52847-0.40163-0.29607-0.125380.188250.339970.415190.263670.48433
midrange-0.456690.390581.27231.55152.13042.4382.64790.598870.85808
mode-4.2254-3.3535-1.23841.67861.45212.79294.51312.08122.6905
mode k.dens-2.5698-0.54338-0.212110.187630.912121.79752.06090.839671.1242



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')