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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Mon, 15 Dec 2014 10:05:03 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/15/t1418638025i8vlccut12bp0hf.htm/, Retrieved Sun, 19 May 2024 14:42:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268008, Retrieved Sun, 19 May 2024 14:42:18 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Bootstrap Plot Permanente evaluatie positieve waarden

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Bootstrap Plot - Central Tendency] [Paper data] [2014-12-15 10:05:03] [99d5c1073827aabbadf7ab1e7da1d584] [Current]

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Dataseries X:

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Histogram

Boxplots

-0.725341
-0.411403
0.6432
-0.152354
0.0533685
-0.0197973
-0.525883
0.259168
-0.632157
-0.706569
0.196562
0.765131
-0.716441
-0.558399
-0.109449
0.203195
0.486991
-0.329013
0.452704
-0.827272
-0.722295
-0.0227425
-0.303096
0.0410223
0.13443
1.35772
0.0731798
0.0970604
-0.255824
0.969976
0.56071
-0.24641
-0.590503
-0.366784
-0.350326
0.931925
-1.16966
0.0912828
0.43002
0.439337
-0.946567
-0.210042
0.514589
-0.495406
-0.291015
0.0107061
-0.664898
-0.757115
-0.577558
-0.250752
-0.0943843
0.489113
0.579163
0.613475
0.323984
0.195811
-0.372142
-0.0186117
-0.0729617
-0.00495819
-0.0854279
0.791128
-0.747697
-0.348254
-0.348254
0.741935
-0.0514595
0.368845
-0.0186991
-0.159826
0.56527
-0.533154
0.175945
0.727667
1.00286
0.713698
0.405516
0.266341
-0.683508
1.117
-0.920066
-0.550048
-0.114937
0.905176
-0.332806
0.0797511
0.238652
0.983043
-0.0405516
0.920891
0.0229058
-0.243527
-0.662264
-0.0766027
-0.205424
-0.100026
0.321198
-0.0797859
0.607202
0.176275
0.801045
-0.783747
-0.475552
-0.972188
0.182732
-0.641859
-0.549266
0.18976
-1.3534
-0.178226
-1.00237
-0.0705181
0.532427
-1.03214
-0.865365
1.24822
0.315697
1.33372
0.96805
-0.259072
0.338187
0.562541
0.886966
0.173912
-0.276963
0.114759
-0.982051
-0.800096
0.820453
0.48094
0.212339
1.26222
-0.118127
0.951806
-0.330538
0.323984
1.11512
-0.82943
-0.944334
-0.3144
1.21621
0.453994
0.105787
0.239371
0.738288
-0.801248
0.0600418
-0.0277984
-0.119039
-0.761657
0.275411
-0.624479
0.363738
0.195811
-1.12318
-0.406065
-0.330538
0.137055
-0.439215
-0.282663
1.16495
0.245576
-0.0745747
-1.02512
0.625998
0.738609
0.258787
-0.625798
-0.549318
-0.233456
-0.675389

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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=268008&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=268008&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268008&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.10559	-0.085751	-0.031594	5.848e-11	0.025393	0.074685	0.099786	0.044138	0.056988
median	-0.11945	-0.10945	-0.070518	-0.022742	-0.0049582	0.079751	0.10607	0.052342	0.06556
midrange	-0.068595	-0.04559	0.00216	0.00216	0.08503	0.11727	0.1663	0.054903	0.08287
mode	-1.1715	-0.8305	-0.33619	-0.039749	0.24308	0.80202	1.2626	0.48906	0.57927
mode k.dens	-0.46225	-0.22298	-0.098489	-0.026593	0.047407	0.15732	0.19625	0.1299	0.1459

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.10559 & -0.085751 & -0.031594 & 5.848e-11 & 0.025393 & 0.074685 & 0.099786 & 0.044138 & 0.056988 \tabularnewline
median & -0.11945 & -0.10945 & -0.070518 & -0.022742 & -0.0049582 & 0.079751 & 0.10607 & 0.052342 & 0.06556 \tabularnewline
midrange & -0.068595 & -0.04559 & 0.00216 & 0.00216 & 0.08503 & 0.11727 & 0.1663 & 0.054903 & 0.08287 \tabularnewline
mode & -1.1715 & -0.8305 & -0.33619 & -0.039749 & 0.24308 & 0.80202 & 1.2626 & 0.48906 & 0.57927 \tabularnewline
mode k.dens & -0.46225 & -0.22298 & -0.098489 & -0.026593 & 0.047407 & 0.15732 & 0.19625 & 0.1299 & 0.1459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268008&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]-0.10559[/C][C]-0.085751[/C][C]-0.031594[/C][C]5.848e-11[/C][C]0.025393[/C][C]0.074685[/C][C]0.099786[/C][C]0.044138[/C][C]0.056988[/C][/ROW]
[ROW][C]median[/C][C]-0.11945[/C][C]-0.10945[/C][C]-0.070518[/C][C]-0.022742[/C][C]-0.0049582[/C][C]0.079751[/C][C]0.10607[/C][C]0.052342[/C][C]0.06556[/C][/ROW]
[ROW][C]midrange[/C][C]-0.068595[/C][C]-0.04559[/C][C]0.00216[/C][C]0.00216[/C][C]0.08503[/C][C]0.11727[/C][C]0.1663[/C][C]0.054903[/C][C]0.08287[/C][/ROW]
[ROW][C]mode[/C][C]-1.1715[/C][C]-0.8305[/C][C]-0.33619[/C][C]-0.039749[/C][C]0.24308[/C][C]0.80202[/C][C]1.2626[/C][C]0.48906[/C][C]0.57927[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.46225[/C][C]-0.22298[/C][C]-0.098489[/C][C]-0.026593[/C][C]0.047407[/C][C]0.15732[/C][C]0.19625[/C][C]0.1299[/C][C]0.1459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268008&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268008&T=1

As an alternative you can also use a QR Code:

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

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.10559	-0.085751	-0.031594	5.848e-11	0.025393	0.074685	0.099786	0.044138	0.056988
median	-0.11945	-0.10945	-0.070518	-0.022742	-0.0049582	0.079751	0.10607	0.052342	0.06556
midrange	-0.068595	-0.04559	0.00216	0.00216	0.08503	0.11727	0.1663	0.054903	0.08287
mode	-1.1715	-0.8305	-0.33619	-0.039749	0.24308	0.80202	1.2626	0.48906	0.57927
mode k.dens	-0.46225	-0.22298	-0.098489	-0.026593	0.047407	0.15732	0.19625	0.1299	0.1459

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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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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code