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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 computationMon, 15 Dec 2014 17:13:54 +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/15/t14186637497igsbnrj2gaquqq.htm/, Retrieved Thu, 16 May 2024 22:34:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268771, Retrieved Thu, 16 May 2024 22:34:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2014-11-04 10:08:09] [32b17a345b130fdf5cc88718ed94a974]
-       [Central Tendency] [WS7 3] [2014-11-12 20:26:33] [81f624c2f0b20a2549c93e7c3dccf981]
-    D    [Central Tendency] [] [2014-12-15 16:40:00] [ae96d02647dd9ad9c105f1fa6642e295]
- RM D        [Bootstrap Plot - Central Tendency] [] [2014-12-15 17:13:54] [4ade5e15fc88dfcb6333f94ac70b9a75] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0.531142
-1.13262
-0.582415
-5.59232
-6.83235
-0.305178
2.34674
-0.147607
-1.45216
-6.53672
-1.47258
-7.01355
-1.5525
-0.691061
-6.47182
-1.3266
-0.910526
-3.5313
-3.71408
-2.47297
-6.55211
0.884966
-1.98207
1.06889
-1.24589
-1.87098
3.21616
0.303614
-3.27261
-0.910074
-3.90055
-1.45827
-3.66334
-2.90078
-1.75345
-2.92291
-2.39109
-0.345755
-4.26578
-0.489436
-7.67222
-1.93212
0.169032
-1.24816
-0.221419
-1.95304
-1.46435
-5.09199
0.00843549
-0.771406
-0.930545
-6.31456
-2.27144
-0.772019
-0.0730031
-2.75141
-6.44107
1.50264
-5.03119
0.208487
-3.99275
-0.302012
-4.30674
2.81801
-3.99675
-3.82478
-1.63475
-0.412881
1.65845
-0.61019
-0.945981
-1.69168
-4.45707
0.72551
0.631932
0.357607
-0.501763
-0.0729741
-2.60926
-5.01083
-2.43116
0.769258
-1.67025
-8.47289
-2.69552
-1.34455
-1.95234
-1.09084
-4.26373
0.589365
-0.514924
-6.88468
-3.25441
-3.55659
-5.54268
-3.99232
-5.75511
-1.42526
0.689277
-4.85155
-2.20238
-2.37146
-4.01912
-3.21153
1.26867
-4.56396
0.202885
-7.63021
-6.72304
-3.71252
-1.4322
-2.07244
-9.48517
-0.169168
5.40647
4.98784
3.06715
-0.672529
3.48706
5.78732
3.12498
0.298365
4.51436
2.00844
-2.51179
-0.302208
3.47768
0.508152
5.80715
1.03604
1.43307
3.20584
2.15964
1.82795
-0.460219
-0.943839
4.23855
-4.13094
5.62711
2.14245
-1.05444
5.09965
2.45493
5.31862
3.4185
3.88483
-0.655842
0.997607
1.37767
4.91301
-2.41178
2.59647
4.84647
3.8999
1.08642
0.185256
5.26481
2.87825
1.10517
-1.00021
5.45728
2.96833
3.80826
2.5065
5.99235
-1.59119
5.93816
2.15775
1.42627
-1.4971
2.9369
5.06377
1.76713
1.96404
2.08435
5.12821
-0.0854529
6.41078
2.27859
-4.94393
0.113974
0.717979
6.24893
2.49879
0.331927
2.92302
4.28397
0.225781
-0.916197
0.0079025
-2.76145
2.73616
-4.01344
5.05511
0.228339
1.07806
2.19985
1.09479
1.88875
2.85672
4.23492
-0.57508
0.608181
3.05997
0.589885
1.96993
-2.90105
-0.90839
6.77032
4.18853
-1.77311
2.48803
-0.771123
0.870016
3.81945
-1.13739
2.88708
5.25838
-1.45252
2.61847
5.3597
-4.62661
-1.05336
3.16702
6.62781
2.50938
3.78732
2.7375
5.68866
6.16807
5.34653
5.92649
0.649042
0.31979
-3.47155
-8.8543
-0.683682
1.04431
-2.9144
-0.643121
0.156948
-0.468542
1.68768
6.87933
-2.02414
2.2298
4.24811
1.21169
3.03694
0.28694
-0.771557
-0.811631
1.94676
2.84017
0.721034
3.71777
-4.7407
-4.58283
0.0957772
-5.5728
-2.77115
-0.200484
-3.3111
1.7629
3.60755
-0.0340522
0.377945
3.06808
3.63718
-1.97265
2.69898
-0.149587
2.31809
4.81833
1.98751
1.13699
3.97591
-4.48868

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268771&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268771&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268771&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'Gertrude Mary Cox' @ cox.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.46366-0.30901-0.11954-2.3781e-070.139660.305310.423220.188350.2592
median-0.50183-0.44189-0.17063-0.0535130.117070.26360.359110.210230.28771
midrange-1.538-1.3574-1.3029-1.3029-0.98748-0.42827-0.375440.275580.31543
mode-7.6425-4.5649-1.5622-2.3781e-071.82145.05515.93822.8053.3836
mode k.dens-0.98778-0.83349-0.60269-0.3946-0.236070.108171.29470.427560.36661

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.46366 & -0.30901 & -0.11954 & -2.3781e-07 & 0.13966 & 0.30531 & 0.42322 & 0.18835 & 0.2592 \tabularnewline
median & -0.50183 & -0.44189 & -0.17063 & -0.053513 & 0.11707 & 0.2636 & 0.35911 & 0.21023 & 0.28771 \tabularnewline
midrange & -1.538 & -1.3574 & -1.3029 & -1.3029 & -0.98748 & -0.42827 & -0.37544 & 0.27558 & 0.31543 \tabularnewline
mode & -7.6425 & -4.5649 & -1.5622 & -2.3781e-07 & 1.8214 & 5.0551 & 5.9382 & 2.805 & 3.3836 \tabularnewline
mode k.dens & -0.98778 & -0.83349 & -0.60269 & -0.3946 & -0.23607 & 0.10817 & 1.2947 & 0.42756 & 0.36661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268771&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]-0.46366[/C][C]-0.30901[/C][C]-0.11954[/C][C]-2.3781e-07[/C][C]0.13966[/C][C]0.30531[/C][C]0.42322[/C][C]0.18835[/C][C]0.2592[/C][/ROW]
[ROW][C]median[/C][C]-0.50183[/C][C]-0.44189[/C][C]-0.17063[/C][C]-0.053513[/C][C]0.11707[/C][C]0.2636[/C][C]0.35911[/C][C]0.21023[/C][C]0.28771[/C][/ROW]
[ROW][C]midrange[/C][C]-1.538[/C][C]-1.3574[/C][C]-1.3029[/C][C]-1.3029[/C][C]-0.98748[/C][C]-0.42827[/C][C]-0.37544[/C][C]0.27558[/C][C]0.31543[/C][/ROW]
[ROW][C]mode[/C][C]-7.6425[/C][C]-4.5649[/C][C]-1.5622[/C][C]-2.3781e-07[/C][C]1.8214[/C][C]5.0551[/C][C]5.9382[/C][C]2.805[/C][C]3.3836[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.98778[/C][C]-0.83349[/C][C]-0.60269[/C][C]-0.3946[/C][C]-0.23607[/C][C]0.10817[/C][C]1.2947[/C][C]0.42756[/C][C]0.36661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268771&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268771&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-0.46366-0.30901-0.11954-2.3781e-070.139660.305310.423220.188350.2592
median-0.50183-0.44189-0.17063-0.0535130.117070.26360.359110.210230.28771
midrange-1.538-1.3574-1.3029-1.3029-0.98748-0.42827-0.375440.275580.31543
mode-7.6425-4.5649-1.5622-2.3781e-071.82145.05515.93822.8053.3836
mode k.dens-0.98778-0.83349-0.60269-0.3946-0.236070.108171.29470.427560.36661


Figures (Output of Computation)

Figure 1
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Figure 2
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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):
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')