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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, 18 Dec 2014 19:35:36 +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/18/t1418931355pw35capvky3zmnt.htm/, Retrieved Sun, 19 May 2024 18:46:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271228, Retrieved Sun, 19 May 2024 18:46:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

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] [gjg] [2014-12-18 19:35:36] [a9ee49ff8435be51911716bad99dd485] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-5,77966
2,31062
2,30496
1,6084
0,935862
0,841011
-1,49338
1,9359
-0,207792
1,37037
2,08126
4,93731
-2,63799
-1,1918
1,01573
0,305036
2,6042
1,0237
0,974625
-1,19424
0,714398
0,935314
0,636313
0,437507
2,91559
-0,763883
1,83745
2,18504
0,107432
0,808816
-0,772931
3,07651
-0,301352
1,44521
-0,777436
-0,0224648
-0,932825
1,34735
-7,74502
2,03729
1,89446
-1,11171
1,89345
0,0199191
1,96776
1,07254
-1,08308
-2,148
2,41889
2,11097
-0,918685
8,44528
1,58078
0,346372
2,191
0,871114
-0,151388
-2,26566
1,42034
2,53137
-2,11974
-1,24963
-1,24963
2,64639
-0,621933
1,9359
-1,80039
-2,98969
-0,817218
-2,37491
2,72854
-0,129277
-3,50557
0,816228
2,10728
-2,52336
-3,49152
0,43803
-1,90078
0,576255
-3,10655
2,64059
-0,241028
2,66037
-2,0153
-0,0602457
1,002
0,52889
0,924697
1,06436
-2,59557
-0,469915
-1,66849
1,55658
-0,19755
1,43812
2,28247
2,15914
0,120501
-0,877656
-2,35852
-0,641028
-0,586213
-0,571571
1,76689
0,639094
-0,711192
-0,185054
1,44048
-4,01269
-1,38482
0,887429
1,76893
3,50295
1,13231
1,94084
3,96559
2,47154
0,889066
3,10718
-0,745998
-4,2621
-3,39616
-5,80058
-0,0472758
-1,7644
0,399062
-1,20219
-1,02246
-1,64342
0,256391
2,09101
-2,31339
-0,727275
0,0940651
0,458433
0,831898
-1,63821
-3,70793
-0,868298
0,780837
2,17443
-2,92348
1,12889
-4,62867
-5,54224
-2,41483
-6,89164
0,0548131
-2,11772
-3,42013
0,693729
1,62847
1,12409
0,165945
0,0358625
1,35181
-1,05697
-0,940832
-0,791416
0,534251
2,14283
0,385474
-0,155353
0,707699
-1,6968

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271228&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271228&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271228&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'Herman Ole Andreas Wold' @ wold.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.38933-0.28543-0.12463-1.5964e-070.127950.269020.3150.166980.25258
median-0.18173-0.0472760.0940650.280710.438030.583790.744910.217890.34396
midrange-2.1232-1.8897-1.40390.350130.350131.32241.45151.02181.754
mode-5.5446-3.1161-1.060.343141.34932.64163.10911.76212.4094
mode k.dens-0.89693-0.553690.446040.743540.918161.36151.74760.536890.47211

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.38933 & -0.28543 & -0.12463 & -1.5964e-07 & 0.12795 & 0.26902 & 0.315 & 0.16698 & 0.25258 \tabularnewline
median & -0.18173 & -0.047276 & 0.094065 & 0.28071 & 0.43803 & 0.58379 & 0.74491 & 0.21789 & 0.34396 \tabularnewline
midrange & -2.1232 & -1.8897 & -1.4039 & 0.35013 & 0.35013 & 1.3224 & 1.4515 & 1.0218 & 1.754 \tabularnewline
mode & -5.5446 & -3.1161 & -1.06 & 0.34314 & 1.3493 & 2.6416 & 3.1091 & 1.7621 & 2.4094 \tabularnewline
mode k.dens & -0.89693 & -0.55369 & 0.44604 & 0.74354 & 0.91816 & 1.3615 & 1.7476 & 0.53689 & 0.47211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271228&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]-0.38933[/C][C]-0.28543[/C][C]-0.12463[/C][C]-1.5964e-07[/C][C]0.12795[/C][C]0.26902[/C][C]0.315[/C][C]0.16698[/C][C]0.25258[/C][/ROW]
[ROW][C]median[/C][C]-0.18173[/C][C]-0.047276[/C][C]0.094065[/C][C]0.28071[/C][C]0.43803[/C][C]0.58379[/C][C]0.74491[/C][C]0.21789[/C][C]0.34396[/C][/ROW]
[ROW][C]midrange[/C][C]-2.1232[/C][C]-1.8897[/C][C]-1.4039[/C][C]0.35013[/C][C]0.35013[/C][C]1.3224[/C][C]1.4515[/C][C]1.0218[/C][C]1.754[/C][/ROW]
[ROW][C]mode[/C][C]-5.5446[/C][C]-3.1161[/C][C]-1.06[/C][C]0.34314[/C][C]1.3493[/C][C]2.6416[/C][C]3.1091[/C][C]1.7621[/C][C]2.4094[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.89693[/C][C]-0.55369[/C][C]0.44604[/C][C]0.74354[/C][C]0.91816[/C][C]1.3615[/C][C]1.7476[/C][C]0.53689[/C][C]0.47211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271228&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271228&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-0.38933-0.28543-0.12463-1.5964e-070.127950.269020.3150.166980.25258
median-0.18173-0.0472760.0940650.280710.438030.583790.744910.217890.34396
midrange-2.1232-1.8897-1.40390.350130.350131.32241.45151.02181.754
mode-5.5446-3.1161-1.060.343141.34932.64163.10911.76212.4094
mode k.dens-0.89693-0.553690.446040.743540.918161.36151.74760.536890.47211


Figures (Output of Computation)

Figure 1
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Figure 2
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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')