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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 computationFri, 12 Dec 2014 11:58:13 +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/Dec/12/t1418385510hhs115u95wjr5v3.htm/, Retrieved Wed, 29 May 2024 04:52:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266568, Retrieved Wed, 29 May 2024 04:52:24 +0000
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Original text written by user:
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User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2014-12-12 11:58:13] [003c997d057e54927bd887526d955d96] [Current]
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Dataseries X:
0.441223
-0.104706
0.625587
-5.86138
-4.90731
-1.03239
1.63862
-0.854869
-2.82197
-4.1705
-1.4008
-6.39428
-0.808615
-2.38777
-2.94673
-0.841514
-1.49298
-2.47311
-3.2034
-2.3021
-5.01252
0.85588
-1.7692
0.408324
-1.09819
-3.25617
0.0464344
0.796597
-2.82848
-0.670504
-3.835
-0.604706
-2.44021
-3.99949
-1.7034
-1.34151
-1.60471
0.520375
-3.9337
-1.56529
-5.84151
-1.10471
-0.0981913
-1.4337
0.059789
-1.835
-1.13109
-5.2034
1.0334
0.750668
-1.03891
-4.64412
-0.808615
0.757183
0.6979
-1.47311
-5.74282
2.3295
-1.51904
1.1979
-3.72327
0.619072
-4.63761
2.07282
-2.53891
-2.14412
-2.96008
-1.61155
1.75718
0.02689
-1.66399
-0.275716
-2.7363
0.737314
0.0334048
-0.262686
0.790082
0.171516
-2.24282
-4.99949
-1.97311
1.72428
-1.9008
-5.64412
-2.7692
-1.42718
-0.874413
-0.979625
-3.24282
1.55979
1.15849
-5.67702
-2.22979
-2.57832
-4.01252
-2.01252
-3.40731
0.151971
2.52038
-5.03891
-0.97311
-1.40731
-2.70992
-3.1705
1.86239
-3.47311
1.25718
-5.87441
-4.54542
-2.7692
-0.209918
0.119072
-4.71643
1.1979
2.96109
3.46109
0.125262
1.84937
0.941223
2.80963
-2.5262
3.38226
2.36891
4.11907
-2.03891
0.290082
1.96109
-1.22979
2.50702
1.98748
1.55327
0.112232
2.02689
0.599203
1.29008
-0.236302
4.19139
-3.14412
1.90832
0.809626
0.421678
3.25067
-0.828484
2.7637
1.50051
1.97412
-0.506009
0.730799
-0.8021
3.05979
-4.00601
1.30963
2.89529
2.2637
3.35588
0.836011
2.54643
2.86239
0.276727
0.059789
3.30963
2.2966
1.17152
4.42168
3.24383
1.15197
2.50702
3.38226
1.3295
-1.69689
1.5992
3.50051
0.434708
1.72428
1.72428
2.86891
-0.598191
3.7637
0.500506
-2.47962
-1.42067
0.895294
3.17152
0.36891
-2.30861
1.36891
4.45458
-0.538908
1.11256
0.428193
0.790082
1.83601
-1.64412
3.39529
2.09269
2.2637
-1.19689
0.763698
3.42168
1.7308
3.40181
2.25718
-0.00600906
1.89529
-0.348029
3.42168
-1.63761
2.88878
2.11223
1.80311
1.46109
1.79008
-1.82848
1.29008
0.908324
-0.440211
2.2308
3.55979
0.895294
1.50051
2.55979
-2.34151
1.25067
1.28357
4.00051
2.88878
2.55327
4.19139
2.51354
3.2966
3.45458
2.38194
1.79008
-1.14412
-1.04542
-4.94673
1.21777
-1.8021
-0.8021
-0.742817
-0.499494
-2.1705
2.15849
3.24383
1.05327
1.50051
1.27673
-0.111221
0.171516
-0.578322
-1.78223
-1.59819
2.50051
1.63862
-1.00601
2.73731
-2.61122
-4.17702
-0.598191
-5.60471
1.15197
1.49399
-1.34151
2.15849
2.7966
1.39529
2.85588
1.15849
3.42819
0.658486
4.11907
0.454577
1.36891
2.63862
1.50051
2.19139
2.5663




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266568&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266568&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266568&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'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.35963-0.26643-0.0847683.4599e-070.123570.265350.381270.160590.20834
median-0.242630.0321020.171520.421680.520380.750670.790080.236710.34886
midrange-1.1376-1.1014-0.96985-0.96985-0.70992-0.7034-0.693470.139540.25994
mode-3.822-1.84290.444641.50051.72433.42174.11911.49611.2796
mode k.dens-1.17170.606871.1131.3181.48891.8442.0990.557090.37585

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.35963 & -0.26643 & -0.084768 & 3.4599e-07 & 0.12357 & 0.26535 & 0.38127 & 0.16059 & 0.20834 \tabularnewline
median & -0.24263 & 0.032102 & 0.17152 & 0.42168 & 0.52038 & 0.75067 & 0.79008 & 0.23671 & 0.34886 \tabularnewline
midrange & -1.1376 & -1.1014 & -0.96985 & -0.96985 & -0.70992 & -0.7034 & -0.69347 & 0.13954 & 0.25994 \tabularnewline
mode & -3.822 & -1.8429 & 0.44464 & 1.5005 & 1.7243 & 3.4217 & 4.1191 & 1.4961 & 1.2796 \tabularnewline
mode k.dens & -1.1717 & 0.60687 & 1.113 & 1.318 & 1.4889 & 1.844 & 2.099 & 0.55709 & 0.37585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266568&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]-0.35963[/C][C]-0.26643[/C][C]-0.084768[/C][C]3.4599e-07[/C][C]0.12357[/C][C]0.26535[/C][C]0.38127[/C][C]0.16059[/C][C]0.20834[/C][/ROW]
[ROW][C]median[/C][C]-0.24263[/C][C]0.032102[/C][C]0.17152[/C][C]0.42168[/C][C]0.52038[/C][C]0.75067[/C][C]0.79008[/C][C]0.23671[/C][C]0.34886[/C][/ROW]
[ROW][C]midrange[/C][C]-1.1376[/C][C]-1.1014[/C][C]-0.96985[/C][C]-0.96985[/C][C]-0.70992[/C][C]-0.7034[/C][C]-0.69347[/C][C]0.13954[/C][C]0.25994[/C][/ROW]
[ROW][C]mode[/C][C]-3.822[/C][C]-1.8429[/C][C]0.44464[/C][C]1.5005[/C][C]1.7243[/C][C]3.4217[/C][C]4.1191[/C][C]1.4961[/C][C]1.2796[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.1717[/C][C]0.60687[/C][C]1.113[/C][C]1.318[/C][C]1.4889[/C][C]1.844[/C][C]2.099[/C][C]0.55709[/C][C]0.37585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266568&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266568&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-0.35963-0.26643-0.0847683.4599e-070.123570.265350.381270.160590.20834
median-0.242630.0321020.171520.421680.520380.750670.790080.236710.34886
midrange-1.1376-1.1014-0.96985-0.96985-0.70992-0.7034-0.693470.139540.25994
mode-3.822-1.84290.444641.50051.72433.42174.11911.49611.2796
mode k.dens-1.17170.606871.1131.3181.48891.8442.0990.557090.37585



Parameters (Session):
par1 = 277 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 277 ; 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')