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Description of Statistical Computation

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, 27 Aug 2015 22:02:44 +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/27/t1440709394s1tyte7h6y2durs.htm/, Retrieved Thu, 16 May 2024 13:22:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280340, Retrieved Thu, 16 May 2024 13:22:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

Tree of Dependent Computations

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] [] [2015-08-27 21:02:44] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.234051
-5.88163
-5.99852
0.359179
0.341098
-1.82041
-4.89182
-0.882455
-6.09571
-1.47169
-0.483327
-2.87731
-7.09955
1.40207
1.52606
-1.37275
-1.53855
4.73119
-2.86356
-7.99525
-2.86103
-0.186912
-1.79525
-2.53937
0.0849052
-3.34052
-6.42989
-1.25592
1.0033
0.8826
1.61957
-1.84636
-4.3198
-1.5378
-1.07778
-5.35731
-1.08728
-0.766492
0.12954
-3.00467
1.92438
0.513127
-4.72996
3.50556
-2.7499
0.286199
2.9924
2.00772
-5.06989
0.398796
-4.52601
-7.09723
-0.811599
-0.690091
0.220648
-6.10761
-2.42269
-5.11439
-5.38888
-1.20219
1.66484
-4.88518
-1.409
-2.06342
-2.55223
0.0621849
-0.846308
-8.00906
1.6158
4.33325
5.50231
0.416978
4.40388
2.96198
2.389
-2.25139
0.16476
4.11427
1.9237
6.06063
1.8525
2.69499
2.25381
1.56739
0.787359
0.274807
5.31005
-4.71211
1.9768
-0.72603
4.33688
2.33051
3.52351
-1.08056
6.11803
2.92733
4.69579
2.00408
1.49355
5.89402
3.89049
2.7943
-1.28809
1.29789
2.33052
1.93252
2.38131
2.56718
5.45973
0.651204
2.89678
4.75078
0.983365
-0.182383
-1.63935
2.64052
-2.38008
1.91518
5.15381
0.500397
0.771336
-2.49347
0.791222
0.302826
4.27617
-1.41191
1.65773
-0.242532
4.17031
5.55135
-0.62112
-0.920197
3.19719
3.21178
5.62311
0.708657
0.665093
-4.00282
-8.31781
0.571126
-0.896041
0.205242
2.25055
5.10011
-1.61254
2.54924
3.07339
-0.107757
-0.998273
2.20081
0.839854
-4.26318
-3.77422
-1.23986
-2.12248
2.7352
0.564526
3.37444
-1.80181
-0.94694
2.45469
-4.42188

Tables (Output of Computation)





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=280340&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=280340&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280340&T=0

As an alternative you can also use a QR Code:  
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-0.51244-0.41275-0.1883-4.7469e-070.136190.330670.439880.235190.32449
median-0.55291-0.184650.164760.28050.361870.564530.658220.222570.19711
midrange-1.3835-1.2119-1.1286-1.0999-0.97422-0.93861-0.519450.117080.15437
mode-7.1085-4.8944-1.538-4.7469e-071.42493.32345.15692.56552.9629
mode k.dens-1.3138-0.98098-0.135320.321650.882061.912.27520.843411.0174

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.51244 & -0.41275 & -0.1883 & -4.7469e-07 & 0.13619 & 0.33067 & 0.43988 & 0.23519 & 0.32449 \tabularnewline
median & -0.55291 & -0.18465 & 0.16476 & 0.2805 & 0.36187 & 0.56453 & 0.65822 & 0.22257 & 0.19711 \tabularnewline
midrange & -1.3835 & -1.2119 & -1.1286 & -1.0999 & -0.97422 & -0.93861 & -0.51945 & 0.11708 & 0.15437 \tabularnewline
mode & -7.1085 & -4.8944 & -1.538 & -4.7469e-07 & 1.4249 & 3.3234 & 5.1569 & 2.5655 & 2.9629 \tabularnewline
mode k.dens & -1.3138 & -0.98098 & -0.13532 & 0.32165 & 0.88206 & 1.91 & 2.2752 & 0.84341 & 1.0174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280340&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]-0.51244[/C][C]-0.41275[/C][C]-0.1883[/C][C]-4.7469e-07[/C][C]0.13619[/C][C]0.33067[/C][C]0.43988[/C][C]0.23519[/C][C]0.32449[/C][/ROW]
[ROW][C]median[/C][C]-0.55291[/C][C]-0.18465[/C][C]0.16476[/C][C]0.2805[/C][C]0.36187[/C][C]0.56453[/C][C]0.65822[/C][C]0.22257[/C][C]0.19711[/C][/ROW]
[ROW][C]midrange[/C][C]-1.3835[/C][C]-1.2119[/C][C]-1.1286[/C][C]-1.0999[/C][C]-0.97422[/C][C]-0.93861[/C][C]-0.51945[/C][C]0.11708[/C][C]0.15437[/C][/ROW]
[ROW][C]mode[/C][C]-7.1085[/C][C]-4.8944[/C][C]-1.538[/C][C]-4.7469e-07[/C][C]1.4249[/C][C]3.3234[/C][C]5.1569[/C][C]2.5655[/C][C]2.9629[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.3138[/C][C]-0.98098[/C][C]-0.13532[/C][C]0.32165[/C][C]0.88206[/C][C]1.91[/C][C]2.2752[/C][C]0.84341[/C][C]1.0174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280340&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280340&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.51244-0.41275-0.1883-4.7469e-070.136190.330670.439880.235190.32449
median-0.55291-0.184650.164760.28050.361870.564530.658220.222570.19711
midrange-1.3835-1.2119-1.1286-1.0999-0.97422-0.93861-0.519450.117080.15437
mode-7.1085-4.8944-1.538-4.7469e-071.42493.32345.15692.56552.9629
mode k.dens-1.3138-0.98098-0.135320.321650.882061.912.27520.843411.0174


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Figure 11
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Input Parameters & R Code

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')