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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 computationThu, 18 Dec 2014 14:49:03 +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/18/t1418914157x0ofq3rw25ptrxq.htm/, Retrieved Fri, 17 May 2024 14:04:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271023, Retrieved Fri, 17 May 2024 14:04:42 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD    [Bootstrap Plot - Central Tendency] [hubu] [2014-12-18 14:49:03] [a9ee49ff8435be51911716bad99dd485] [Current]
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Dataseries X:
-6.39953
1.95736
2.47865
1.58972
0.184681
0.918505
-1.99525
1.58639
-0.140506
1.35685
1.72748
5.06728
-2.55853
-0.947617
1.13426
0.264652
2.59324
0.74381
0.743969
-1.11232
1.2196
1.30622
0.553112
0.291992
2.65669
-0.855357
1.92359
1.74414
0.735418
1.04816
0.426579
3.41835
-0.513488
0.833442
-1.06471
-0.237221
-0.759509
1.16511
-7.10937
1.72666
1.58909
-1.34436
1.48135
-0.236383
1.38838
1.0188
-1.946
-2.25532
1.80812
2.12565
-1.15933
7.90836
0.926642
0.590183
2.05887
0.760719
-0.455908
-2.37174
1.11229
2.44094
-2.10897
-1.54551
-1.47152
3.05933
-0.878558
2.1848
-1.85434
-2.48434
-1.07292
-2.31574
2.72206
-0.0293457
-3.1509
0.404606
1.88743
-2.62756
-3.5747
0.754203
-2.19211
1.4091
-3.19355
2.84859
-0.385986
1.7434
-2.0231
0.413897
1.12249
0.18236
-0.302073
0.411861
-2.43077
-0.489293
-0.852392
1.41397
0.0561077
1.00138
3.78336
3.09187
-0.146159
-0.833078
-1.82779
0.067725
-0.429261
-0.310928
1.8096
0.62417
-0.700127
-0.72391
1.86827
-3.61635
-1.97036
1.67556
1.87606
3.63278
1.01241
2.04096
3.99554
2.48477
1.58861
2.97283
-0.499202
-3.91109
-2.82911
-6.12053
0.339289
-0.944523
0.0352121
-1.04288
-1.32319
-1.13312
0.183183
1.96753
-2.51184
-0.287708
-0.369008
0.68567
0.758509
-1.40043
-3.49701
-0.776372
0.760543
2.3108
-2.67336
0.700963
-4.50339
-5.18602
-1.8807
-7.006
-0.421422
-2.08002
-3.16565
0.737049
1.53266
0.811355
-0.429889
0.602124
1.06958
-1.16829
-0.541346
-0.852089
1.34924
1.76348
0.356035
-0.821847
0.901744
-1.23025




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271023&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271023&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271023&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'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.41865-0.27167-0.10225-3.5542e-080.116210.267460.314370.162390.21846
median-0.29534-0.191290.125040.278320.409250.613150.693570.230050.28421
midrange-1.8471-1.663-1.0210.399490.399490.466341.36120.866471.4205
mode-6.4066-3.6164-1.0249-3.5542e-080.87512.04192.9741.8261.9
mode k.dens-0.73695-0.394880.65130.865351.09131.43631.56820.501830.44002

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.41865 & -0.27167 & -0.10225 & -3.5542e-08 & 0.11621 & 0.26746 & 0.31437 & 0.16239 & 0.21846 \tabularnewline
median & -0.29534 & -0.19129 & 0.12504 & 0.27832 & 0.40925 & 0.61315 & 0.69357 & 0.23005 & 0.28421 \tabularnewline
midrange & -1.8471 & -1.663 & -1.021 & 0.39949 & 0.39949 & 0.46634 & 1.3612 & 0.86647 & 1.4205 \tabularnewline
mode & -6.4066 & -3.6164 & -1.0249 & -3.5542e-08 & 0.8751 & 2.0419 & 2.974 & 1.826 & 1.9 \tabularnewline
mode k.dens & -0.73695 & -0.39488 & 0.6513 & 0.86535 & 1.0913 & 1.4363 & 1.5682 & 0.50183 & 0.44002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271023&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]-0.41865[/C][C]-0.27167[/C][C]-0.10225[/C][C]-3.5542e-08[/C][C]0.11621[/C][C]0.26746[/C][C]0.31437[/C][C]0.16239[/C][C]0.21846[/C][/ROW]
[ROW][C]median[/C][C]-0.29534[/C][C]-0.19129[/C][C]0.12504[/C][C]0.27832[/C][C]0.40925[/C][C]0.61315[/C][C]0.69357[/C][C]0.23005[/C][C]0.28421[/C][/ROW]
[ROW][C]midrange[/C][C]-1.8471[/C][C]-1.663[/C][C]-1.021[/C][C]0.39949[/C][C]0.39949[/C][C]0.46634[/C][C]1.3612[/C][C]0.86647[/C][C]1.4205[/C][/ROW]
[ROW][C]mode[/C][C]-6.4066[/C][C]-3.6164[/C][C]-1.0249[/C][C]-3.5542e-08[/C][C]0.8751[/C][C]2.0419[/C][C]2.974[/C][C]1.826[/C][C]1.9[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.73695[/C][C]-0.39488[/C][C]0.6513[/C][C]0.86535[/C][C]1.0913[/C][C]1.4363[/C][C]1.5682[/C][C]0.50183[/C][C]0.44002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271023&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271023&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-0.41865-0.27167-0.10225-3.5542e-080.116210.267460.314370.162390.21846
median-0.29534-0.191290.125040.278320.409250.613150.693570.230050.28421
midrange-1.8471-1.663-1.0210.399490.399490.466341.36120.866471.4205
mode-6.4066-3.6164-1.0249-3.5542e-080.87512.04192.9741.8261.9
mode k.dens-0.73695-0.394880.65130.865351.09131.43631.56820.501830.44002



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):
par4 <- 'P1 P5 Q1 Q3 P95 P99'
par3 <- '0'
par2 <- '5'
par1 <- '200'
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')