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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 computationSun, 23 Aug 2015 02:26:33 +0100
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/Aug/23/t1440293215r1kl9brzbwnttuu.htm/, Retrieved Wed, 15 May 2024 18:07:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280315, Retrieved Wed, 15 May 2024 18:07:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
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] [] [2015-08-23 01:26:33] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataseries X:
-1.24419
-4.88718
-0.490945
-1.06096
-5.36602
-7.0784
0.0109581
2.76842
-1.32718
-0.165241
-6.22745
-1.18352
-6.42614
-1.20752
-0.947302
-6.02698
-1.16283
-3.28201
-1.9326
-6.60769
0.718001
-1.86342
1.27168
-1.12953
-2.01122
-2.84157
-1.06147
-4.28968
-2.04517
-6.62058
-3.08226
-3.48847
-1.75628
-2.96096
-2.8161
-3.99483
-0.866192
-8.11894
-2.4253
-0.0973222
-1.61189
-1.36344
-2.01086
0.422831
-2.56833
-0.20309
-3.2608
1.27284
-4.29449
-0.177927
-4.79323
2.3006
-3.9465
-0.827154
-0.152985
1.33331
-0.939047
-1.55621
-5.55347
-0.105781
0.0578376
0.0849968
-4.87649
-9.48533
-0.696666
4.78925
5.54093
3.19797
6.11705
3.33417
-0.0518329
4.08728
2.54109
-2.10681
-0.086689
2.95521
0.629497
0.701727
1.34989
3.66394
1.6906
2.34252
-1.59601
-0.02425
4.15463
-4.67904
5.60757
1.54829
4.41975
2.9667
4.9664
2.25106
3.01281
3.34552
-0.495234
-1.68141
0.399273
1.16896
4.71519
-2.74168
2.53148
4.79416
3.30913
5.76483
2.19268
3.42699
2.47067
1.70197
1.9171
-0.701076
5.67777
-5.11897
5.11584
1.81735
4.0105
0.574013
0.987892
-3.313
1.80108
-0.455804
3.67362
-1.29397
-0.926348
5.39551
5.82035
-1.29497
2.67097
6.90898
4.03103
5.88712
5.9298
5.36353
5.17222
0.174608
-4.18691
-8.74799
0.935582
-3.41513
0.194321
4.05321
4.54415
0.769981
3.5866
0.122622
-0.77787
-1.53279
0.100308
-4.29024
0.868474




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280315&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280315&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280315&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'Sir Maurice George Kendall' @ kendall.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.55447-0.42692-0.201051.7718e-070.132330.424480.510640.255670.33338
median-0.82815-0.50531-0.16524-0.0866890.0109580.194320.574010.253980.1762
midrange-1.8325-1.7778-1.4091-1.2882-1.0009-0.240780.14420.429280.40815
mode-6.4327-4.2945-1.66021.7718e-071.92434.78955.88752.70593.5845
mode k.dens-1.2999-1.0848-0.77532-0.54901-0.330610.0702320.66620.457340.44471

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.55447 & -0.42692 & -0.20105 & 1.7718e-07 & 0.13233 & 0.42448 & 0.51064 & 0.25567 & 0.33338 \tabularnewline
median & -0.82815 & -0.50531 & -0.16524 & -0.086689 & 0.010958 & 0.19432 & 0.57401 & 0.25398 & 0.1762 \tabularnewline
midrange & -1.8325 & -1.7778 & -1.4091 & -1.2882 & -1.0009 & -0.24078 & 0.1442 & 0.42928 & 0.40815 \tabularnewline
mode & -6.4327 & -4.2945 & -1.6602 & 1.7718e-07 & 1.9243 & 4.7895 & 5.8875 & 2.7059 & 3.5845 \tabularnewline
mode k.dens & -1.2999 & -1.0848 & -0.77532 & -0.54901 & -0.33061 & 0.070232 & 0.6662 & 0.45734 & 0.44471 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280315&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]-0.55447[/C][C]-0.42692[/C][C]-0.20105[/C][C]1.7718e-07[/C][C]0.13233[/C][C]0.42448[/C][C]0.51064[/C][C]0.25567[/C][C]0.33338[/C][/ROW]
[ROW][C]median[/C][C]-0.82815[/C][C]-0.50531[/C][C]-0.16524[/C][C]-0.086689[/C][C]0.010958[/C][C]0.19432[/C][C]0.57401[/C][C]0.25398[/C][C]0.1762[/C][/ROW]
[ROW][C]midrange[/C][C]-1.8325[/C][C]-1.7778[/C][C]-1.4091[/C][C]-1.2882[/C][C]-1.0009[/C][C]-0.24078[/C][C]0.1442[/C][C]0.42928[/C][C]0.40815[/C][/ROW]
[ROW][C]mode[/C][C]-6.4327[/C][C]-4.2945[/C][C]-1.6602[/C][C]1.7718e-07[/C][C]1.9243[/C][C]4.7895[/C][C]5.8875[/C][C]2.7059[/C][C]3.5845[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.2999[/C][C]-1.0848[/C][C]-0.77532[/C][C]-0.54901[/C][C]-0.33061[/C][C]0.070232[/C][C]0.6662[/C][C]0.45734[/C][C]0.44471[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280315&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280315&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-0.55447-0.42692-0.201051.7718e-070.132330.424480.510640.255670.33338
median-0.82815-0.50531-0.16524-0.0866890.0109580.194320.574010.253980.1762
midrange-1.8325-1.7778-1.4091-1.2882-1.0009-0.240780.14420.429280.40815
mode-6.4327-4.2945-1.66021.7718e-071.92434.78955.88752.70593.5845
mode k.dens-1.2999-1.0848-0.77532-0.54901-0.330610.0702320.66620.457340.44471



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')