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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 23:03:26 +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/t1418684694qyffp3kolmcaatj.htm/, Retrieved Thu, 16 May 2024 10:52:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269097, Retrieved Thu, 16 May 2024 10:52:02 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-15 16:34:59] [bcf5edf18529a33bd1494456d2c6cb9a]
- RMPD    [Bootstrap Plot - Central Tendency] [] [2014-12-15 23:03:26] [023a69c6c348bca0f1811b046758af62] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.19014
-3.84982
0.847881
0.680933
-5.18172
-4.60132
-2.72573
2.27409
1.22495
-1.35467
-6.01337
-1.43149
-8.0785
-0.931756
-1.45418
-3.41495
0.0455309
-1.83304
-2.78172
-2.60291
-1.44698
-5.31476
0.761316
-2.40202
1.77741
-0.856857
-3.97654
2.83178
2.39662
-1.05621
-0.581613
-3.4783
-1.61295
-5.38263
-3.2865
-4.72369
-1.7051
-2.56339
-1.18679
-0.0710296
-2.8525
-2.46651
-4.63475
-2.56168
1.40282
-1.65574
1.95287
-2.08758
-0.593689
-3.15832
2.86924
1.43352
-0.348852
-4.3291
-2.24118
-0.514112
-0.209718
-3.25873
-5.96673
2.18483
-1.4913
1.65679
-3.22552
0.846044
-3.05668
2.19576
-2.07576
-1.4201
-2.94841
-0.282069
1.58111
1.23677
-1.14262
0.130444
-2.36218
3.62547
-4.96321
-0.294649
-0.584309
1.36793
-1.41234
-1.3183
-3.63261
-0.0383094
2.89254
-0.844448
-5.67191
-0.602516
0.375179
0.617402
0.530914
-1.44632
2.3039
2.35356
-5.4166
-1.16186
-3.10261
-3.99298
-1.29273
-4.89959
0.424083
2.21619
-2.70361
-0.162489
-1.30943
-1.89695
-1.21804
2.5713
-1.79995
2.91367
-5.70649
-3.72952
-0.636812
0.879009
0.831957
-5.59545
0.186664
3.31856
2.742
0.872571
2.6556
-5.30161
3.56423
-2.10451
2.72164
0.716227
3.68922
-4.25252
-1.15361
1.09418
-4.27833
3.05685
1.71996
2.68297
-2.00327
1.24501
0.607389
0.173063
-2.11041
3.58207
-3.42874
0.780507
-2.03307
0.552247
5.71605
1.69094
1.38174
1.84303
1.35601
2.92628
0.670659
-0.443067
0.940152
-3.02837
1.82916
-5.26559
2.12465
2.53606
-0.543418
2.08862
0.227444
2.78434
1.0086
-2.45627
-0.644869
5.86531
-1.60823
3.18527
5.32511
5.315
0.504051
2.73913
4.61424
-0.548991
-2.61186
0.902738
4.93735
1.93309
-0.747196
-0.747196
4.2017
-0.360073
2.40747
2.51207
-4.66302
-0.333387
0.455726
1.92756
1.57871
1.0341
2.3964
5.12646
0.955596
2.31746
0.0973935
-1.07571
2.96491
-1.15377
5.04821
1.59005
0.226384
-2.2477
0.266434
3.61393
3.62592
1.68086
1.71865
0.422887
0.467121
0.573933
3.50446
-3.45657
1.66502
-0.218839
2.87541
2.45252
1.48311
2.23467
-3.2325
-2.74934
-0.829607
-0.962329
3.01998
0.768528
3.17676
-0.139798
0.0982817
1.35847
-4.77064
0.478217
4.16833
3.01572
3.44214
3.71533
2.94791
3.61805
3.93191
5.30498
2.34153
1.41291
-0.302584
-1.93173
-6.66604
1.37893
-2.34672
-1.60526
1.05489
-1.43631
-2.41972
3.33463
5.315
5.89022
-0.575618
-0.852672
1.80625
1.62074
0.95301
0.177011
-0.567039
-2.30596
0.170914
2.22606
-0.389632
1.45939
-2.19051
-7.03734
1.32801
-3.47498
0.504051
0.508727
-2.64323
3.33463
2.05762
0.512222
0.544716
4.25728
4.70219
0.731762
3.70614
1.12055
1.14767
1.34143
2.61247
1.27324
1.98508
-2.6409

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'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269097&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269097&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269097&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'George Udny Yule' @ yule.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.35625-0.23878-0.1079-3.9825e-070.0904880.267690.347150.151230.19839
median-0.28213-0.16310.174770.320810.480060.59150.676430.218460.30529
midrange-1.3817-1.191-1.1066-1.0941-0.57356-0.12277-0.0382550.352420.53303
mode-4.2536-2.6133-0.846552.10161.45783.69015.32052.04582.3043
mode k.dens-0.878520.18080.670130.950351.1881.49641.79930.454240.51789

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.35625 & -0.23878 & -0.1079 & -3.9825e-07 & 0.090488 & 0.26769 & 0.34715 & 0.15123 & 0.19839 \tabularnewline
median & -0.28213 & -0.1631 & 0.17477 & 0.32081 & 0.48006 & 0.5915 & 0.67643 & 0.21846 & 0.30529 \tabularnewline
midrange & -1.3817 & -1.191 & -1.1066 & -1.0941 & -0.57356 & -0.12277 & -0.038255 & 0.35242 & 0.53303 \tabularnewline
mode & -4.2536 & -2.6133 & -0.84655 & 2.1016 & 1.4578 & 3.6901 & 5.3205 & 2.0458 & 2.3043 \tabularnewline
mode k.dens & -0.87852 & 0.1808 & 0.67013 & 0.95035 & 1.188 & 1.4964 & 1.7993 & 0.45424 & 0.51789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269097&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]-0.35625[/C][C]-0.23878[/C][C]-0.1079[/C][C]-3.9825e-07[/C][C]0.090488[/C][C]0.26769[/C][C]0.34715[/C][C]0.15123[/C][C]0.19839[/C][/ROW]
[ROW][C]median[/C][C]-0.28213[/C][C]-0.1631[/C][C]0.17477[/C][C]0.32081[/C][C]0.48006[/C][C]0.5915[/C][C]0.67643[/C][C]0.21846[/C][C]0.30529[/C][/ROW]
[ROW][C]midrange[/C][C]-1.3817[/C][C]-1.191[/C][C]-1.1066[/C][C]-1.0941[/C][C]-0.57356[/C][C]-0.12277[/C][C]-0.038255[/C][C]0.35242[/C][C]0.53303[/C][/ROW]
[ROW][C]mode[/C][C]-4.2536[/C][C]-2.6133[/C][C]-0.84655[/C][C]2.1016[/C][C]1.4578[/C][C]3.6901[/C][C]5.3205[/C][C]2.0458[/C][C]2.3043[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.87852[/C][C]0.1808[/C][C]0.67013[/C][C]0.95035[/C][C]1.188[/C][C]1.4964[/C][C]1.7993[/C][C]0.45424[/C][C]0.51789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269097&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269097&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-0.35625-0.23878-0.1079-3.9825e-070.0904880.267690.347150.151230.19839
median-0.28213-0.16310.174770.320810.480060.59150.676430.218460.30529
midrange-1.3817-1.191-1.1066-1.0941-0.57356-0.12277-0.0382550.352420.53303
mode-4.2536-2.6133-0.846552.10161.45783.69015.32052.04582.3043
mode k.dens-0.878520.18080.670130.950351.1881.49641.79930.454240.51789


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):
par1 = TRUE ;
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')