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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, 09 Dec 2014 22:18:34 +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/09/t1418163542pvwm1g48wdpxwrx.htm/, Retrieved Thu, 16 May 2024 13:41:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264857, Retrieved Thu, 16 May 2024 13:41:23 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsBootstrap Plot Ruwe examenscore
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Paper data] [2014-12-09 22:18:34] [99d5c1073827aabbadf7ab1e7da1d584] [Current]
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Dataseries X:
-4.84302
0.820939
1.33844
1.77346
-0.326794
1.40122
-1.02342
1.6782
-3.09512
2.50665
0.840295
3.63482
-3.00999
-1.01596
0.414801
-1.1507
1.34041
0.899733
0.219287
-1.05924
1.33001
-0.341461
0.458842
-0.962173
2.7787
-3.55751
0.471548
-0.107123
0.00104232
1.74952
-2.07602
1.37873
0.819432
0.452797
0.625949
-1.67195
-2.16192
-0.29961
-2.78199
1.12554
-5.9401
0.510312
1.21739
0.279732
2.56375
-0.0361669
1.15064
1.71005
-0.680955
-0.982169
2.42078
1.21271
-0.906117
4.33813
2.05159
0.455674
1.46546
2.2648
0.368933
-2.37198
0.667098
2.50427
-0.803694
0.139298
0.139298
1.8914
-1.64528
1.99727
-0.915574
-2.96832
-1.83123
-0.758928
1.64456
-0.596943
-4.00669
0.428682
3.25621
-1.62812
-0.0969513
-0.168432
0.199653
0.393225
-2.44996
2.30082
1.30877
1.63134
-2.72792
-0.502047
2.32831
0.738283
1.23248
1.54884
-1.55378
0.580965
-2.16839
2.91921
-1.95988
2.32053
0.191538
0.924366
0.601413
1.16247
-0.348431
-2.65836
-0.131416
-0.543977
-1.47805
1.39553
0.253221
1.51969
0.439461
0.105977
1.07147
-2.89622
-0.469864
-0.391425
2.21297
2.31513
0.786931
3.10603
1.52842
1.63731
1.64279
0.828085
0.865019
-3.01084
-2.18931
-5.52259
0.611861
-3.14387
-1.37719
-1.55203
-1.61733
-2.77582
1.23764
2.05159
1.4376
-0.189906
0.401466
-0.436383
-1.70738
-1.14817
-1.7106
-2.99273
-2.31609
1.60421
1.03434
-2.88302
1.42136
-3.21075
-5.67987
-0.873268
-6.51859
0.455674
0.610017
-2.06222
1.23764
1.41035
0.739195
1.55008
0.202712
2.22172
0.138837
2.76471
-0.257934
0.585694
2.11124
0.569993
0.979733
1.8075
-0.736541





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
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=264857&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]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=264857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264857&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
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.33702-0.24642-0.107194.7485e-080.0934970.237550.330730.147590.20068
median-0.0361670.138840.253220.41480.453520.570540.611860.144080.2003
midrange-1.7072-1.6312-1.2306-1.0902-0.94389-0.67087-0.251780.303120.28672
mode-3.5704-2.7357-0.545090.971051.09521.89812.50921.4181.6402
mode k.dens0.140330.356990.599260.820871.07141.32661.38310.306090.47218

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.33702 & -0.24642 & -0.10719 & 4.7485e-08 & 0.093497 & 0.23755 & 0.33073 & 0.14759 & 0.20068 \tabularnewline
median & -0.036167 & 0.13884 & 0.25322 & 0.4148 & 0.45352 & 0.57054 & 0.61186 & 0.14408 & 0.2003 \tabularnewline
midrange & -1.7072 & -1.6312 & -1.2306 & -1.0902 & -0.94389 & -0.67087 & -0.25178 & 0.30312 & 0.28672 \tabularnewline
mode & -3.5704 & -2.7357 & -0.54509 & 0.97105 & 1.0952 & 1.8981 & 2.5092 & 1.418 & 1.6402 \tabularnewline
mode k.dens & 0.14033 & 0.35699 & 0.59926 & 0.82087 & 1.0714 & 1.3266 & 1.3831 & 0.30609 & 0.47218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264857&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]-0.33702[/C][C]-0.24642[/C][C]-0.10719[/C][C]4.7485e-08[/C][C]0.093497[/C][C]0.23755[/C][C]0.33073[/C][C]0.14759[/C][C]0.20068[/C][/ROW]
[ROW][C]median[/C][C]-0.036167[/C][C]0.13884[/C][C]0.25322[/C][C]0.4148[/C][C]0.45352[/C][C]0.57054[/C][C]0.61186[/C][C]0.14408[/C][C]0.2003[/C][/ROW]
[ROW][C]midrange[/C][C]-1.7072[/C][C]-1.6312[/C][C]-1.2306[/C][C]-1.0902[/C][C]-0.94389[/C][C]-0.67087[/C][C]-0.25178[/C][C]0.30312[/C][C]0.28672[/C][/ROW]
[ROW][C]mode[/C][C]-3.5704[/C][C]-2.7357[/C][C]-0.54509[/C][C]0.97105[/C][C]1.0952[/C][C]1.8981[/C][C]2.5092[/C][C]1.418[/C][C]1.6402[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.14033[/C][C]0.35699[/C][C]0.59926[/C][C]0.82087[/C][C]1.0714[/C][C]1.3266[/C][C]1.3831[/C][C]0.30609[/C][C]0.47218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264857&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-0.33702-0.24642-0.107194.7485e-080.0934970.237550.330730.147590.20068
median-0.0361670.138840.253220.41480.453520.570540.611860.144080.2003
midrange-1.7072-1.6312-1.2306-1.0902-0.94389-0.67087-0.251780.303120.28672
mode-3.5704-2.7357-0.545090.971051.09521.89812.50921.4181.6402
mode k.dens0.140330.356990.599260.820871.07141.32661.38310.306090.47218



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')