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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 computationTue, 11 Nov 2014 15:49:07 +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/Nov/11/t1415720961rh54rtgkf4pmemb.htm/, Retrieved Sun, 19 May 2024 12:39:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253676, Retrieved Sun, 19 May 2024 12:39:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

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] [] [2014-11-11 15:49:07] [e63466588bf3c49b37383cc70d2c7b07] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-2.71568
0.69641
2.42154
3.74818
-1.86884
-1.32765
4.07918
-1.66259
-1.61445
1.154
1.46349
0.0232872
1.01397
0.866345
-0.7248
-0.0286499
0.985856
3.96348
2.86352
0.779494
0.974259
1.36028
2.76458
1.30181
1.17595
1.20037
1.53744
-1.45594
0.756916
0.317389
-0.377563
-0.171821
-0.462441
0.399385
-1.09209
-2.47635
-2.264
-1.43815
1.88726
1.94521
1.64042
-1.37658
2.48554
0.267099
-0.138379
-4.12374
-2.49119
0.210684
0.876542
-1.34686
-0.863664
0.281439
-2.58789
0.244238
-1.6747
2.16528
0.314502
0.73248
0.203737
2.20285
1.16518
0.603305
-0.285295
-0.43736
0.838258
1.25022
1.87705
3.9841
-3.79999
0.5292
-3.07027
-0.725374
1.19203
0.843888
1.01457
3.92354
-0.411455
1.75373
-1.9672
0.900443
0.504783
0.317257
-0.589244
0.308134
2.11747
0.0697186
0.762877
1.30418
0.898055
-1.67247
0.158657
0.292505
0.137328
-1.80599
1.34373
0.2895
2.5482
0.201591
-0.26364
-1.16758
1.49151
2.42275
0.93218
1.19206
-1.69363
1.22952
0.244867
1.75959
-0.0258758
0.828131
0.0802797
2.1139
-1.30925
-2.62189
1.71689
-1.96176
0.945817
-1.79391
0.498578
-1.38274
0.59722
-2.88564
-1.09112
-1.01043
-0.905568
0.255021
1.26245
0.929307
-2.74673
2.11689
-3.48662
2.53974
-2.14717
-1.70407
-0.152385
1.11726
0.386193
-2.41405
-1.0183
-2.26491
3.11365
1.43197
-0.0252302
1.79725
-3.30831
2.27306
-2.09156
1.06865
0.342468
-2.88851
-1.11619
2.04727
4.29559
1.74307
-2.36692
0.158657
1.41334
0.929307
1.3312
-0.53721
0.327426
0.484461
-0.338264
0.444932
1.3515
-1.42617
-0.481121
-3.27807
-1.84171
1.6352
1.63275
0.292626
-1.97118
-2.298
-3.28938
0.318472
-0.288552
-0.665521
-0.0747474
-1.40943
0.553162
-0.530884
1.80415
-0.503576
-6.78839
1.08538
2.29755
-0.508191
-0.483109
0.672724
-0.830496
0.178896
2.34788
1.63641
-0.918079
-0.395569
2.91844
0.696807
1.52925
1.27902
1.76379
0.461598
-2.68975
-2.96637
2.04537
0.166471
1.09712
0.573751
-2.9403
0.725874
-2.52793
-4.31338
0.684353
2.81493
1.13553
0.542456
1.93732
-0.345776
1.04224
-0.64624
-2.01726
-0.491955
0.433575
-1.46903
0.425944
-3.93664
-0.324338
0.804408
-1.36681
-1.1689
1.52575
-3.52692
4.27468
1.51387
-1.31295
-2.66023
-7.22839
-1.87564
1.41323
-1.92912
-0.398227
-1.83077
1.1151
1.73197
0.859059
-0.0409122
0.815635
-2.92818
1.14857
0.140938
-1.12478
-1.84175
0.323527
2.79936
-1.75809
-0.306836
1.38381
2.13189
-1.78089
-5.50961
0.831553
-4.54522
-0.480633
0.843884

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 Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253676&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253676&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 Ronald Aylmer Fisher' @ fisher.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.22347-0.17349-0.0611873.0303e-080.0810230.173650.254280.109220.14221
median-0.00123440.168470.255020.292570.318470.446340.502020.0842020.063451
midrange-1.6325-1.5746-1.4664-1.4664-1.2464-0.60701-0.124810.330050.22
mode-4.3253-2.72-0.593180.543980.929312.10413.121.51251.5225
mode k.dens0.334050.406840.56470.713020.845891.01131.10080.186780.28119

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.22347 & -0.17349 & -0.061187 & 3.0303e-08 & 0.081023 & 0.17365 & 0.25428 & 0.10922 & 0.14221 \tabularnewline
median & -0.0012344 & 0.16847 & 0.25502 & 0.29257 & 0.31847 & 0.44634 & 0.50202 & 0.084202 & 0.063451 \tabularnewline
midrange & -1.6325 & -1.5746 & -1.4664 & -1.4664 & -1.2464 & -0.60701 & -0.12481 & 0.33005 & 0.22 \tabularnewline
mode & -4.3253 & -2.72 & -0.59318 & 0.54398 & 0.92931 & 2.1041 & 3.12 & 1.5125 & 1.5225 \tabularnewline
mode k.dens & 0.33405 & 0.40684 & 0.5647 & 0.71302 & 0.84589 & 1.0113 & 1.1008 & 0.18678 & 0.28119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253676&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]-0.22347[/C][C]-0.17349[/C][C]-0.061187[/C][C]3.0303e-08[/C][C]0.081023[/C][C]0.17365[/C][C]0.25428[/C][C]0.10922[/C][C]0.14221[/C][/ROW]
[ROW][C]median[/C][C]-0.0012344[/C][C]0.16847[/C][C]0.25502[/C][C]0.29257[/C][C]0.31847[/C][C]0.44634[/C][C]0.50202[/C][C]0.084202[/C][C]0.063451[/C][/ROW]
[ROW][C]midrange[/C][C]-1.6325[/C][C]-1.5746[/C][C]-1.4664[/C][C]-1.4664[/C][C]-1.2464[/C][C]-0.60701[/C][C]-0.12481[/C][C]0.33005[/C][C]0.22[/C][/ROW]
[ROW][C]mode[/C][C]-4.3253[/C][C]-2.72[/C][C]-0.59318[/C][C]0.54398[/C][C]0.92931[/C][C]2.1041[/C][C]3.12[/C][C]1.5125[/C][C]1.5225[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.33405[/C][C]0.40684[/C][C]0.5647[/C][C]0.71302[/C][C]0.84589[/C][C]1.0113[/C][C]1.1008[/C][C]0.18678[/C][C]0.28119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253676&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-0.22347-0.17349-0.0611873.0303e-080.0810230.173650.254280.109220.14221
median-0.00123440.168470.255020.292570.318470.446340.502020.0842020.063451
midrange-1.6325-1.5746-1.4664-1.4664-1.2464-0.60701-0.124810.330050.22
mode-4.3253-2.72-0.593180.543980.929312.10413.121.51251.5225
mode k.dens0.334050.406840.56470.713020.845891.01131.10080.186780.28119


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')