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*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

Wed, 24 Jan 2018 12:11:19 +0100

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/2018/Jan/24/t15167923249bvpiiyzpq1xlfa.htm/, Retrieved Mon, 06 May 2024 09:22:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=312870, Retrieved Mon, 06 May 2024 09:22:58 +0000

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

-       [Bootstrap Plot - Central Tendency] [] [2018-01-24 11:11:19] [f20a5ac2e47f1f0fbb70a39abdcd877b] [Current]

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2.80030819572237
0.546110929167911
-1.08882457056234
1.85116612407754
1.81431171737364
1.10336738027983
-1.1245171435247
1.71646640041695
0.793858512491152
-1.07059339259452
1.37785351123131
-0.79301277417615
-0.213022653692917
0.325551388183876
-3.07152677312816
-1.6593526905903
-0.977898156347797
2.77746716194583
0.659684775709612
-0.399824712085136
-1.94483744059989
2.63902152884315
0.830351272801263
0.239452118133438
-1.98023780133566
-3.14893729642629
1.52344843344618
1.52324864513037
3.07560476768955
1.68333908953913
-0.0411419750371334
-1.42576307236563
0.603469563697563
-2.02817409689518
2.36128930601495
-4.84900520812879
-0.296289875821015
-0.273406341522266
-2.41797545929271
-0.735276518673968
-0.758208236878704
0.719357808400705
3.82579134038112
0.751147004565213
-0.686160672385733
2.7839041091014
-0.353356298124068
-1.19565368260915
1.51622971016679
0.687899021959271
-1.03746150319703
0.232258589016444
0.954677970598071
0.253726601750312
0.0906111040418593
-0.337464765144114
-0.146645835344418
-2.14580429676713
-2.64273856198557
-1.69885035349378
0.0886900288640063
2.94885012445766
2.74086814209461
-2.78292060200689
2.11354456172898
1.5019821601947
0.928451976610637
0.251386956762454
1.92686717410181
-0.637152825120997
-3.00404371957499
1.53170453971748
-0.46034424964922
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0.241189107006538
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-1.66304983417195
1.54593083942837
0.0543382827538832
-2.76444539571847
-1.5519700760652
1.99785210877644
1.02671169946072
0.965014171087942
0.833645548583962
-1.78443597034853
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0.0905629201358726
1.2672859043733
1.3367497955578
1.00922794934105
2.97970594008853
-0.0198870686489594
1.37558503989304
1.62130116503375
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0.140633397218948
1.088271459054
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-1.36191185169048
-3.501894103396
1.82567198282555
1.00217218900356
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1.52749755170791
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0.782613205637535
0.394327211581645
1.03045019427192
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-2.3911175541089
-2.49477174792859
1.43479131886814
-0.23693199243609
1.54252687780381
0.753916630559178
-1.12999945988543
0.434385612031916
0.497954292194177
-0.415657064566058
1.25212054898028
0.538408325374887
0.363650201264021
-0.525700021635337
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0.651092396769283
0.605424752110409
1.27467788471578
-3.66701110704255
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1.80505691986507
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0.266590824347748
1.08312554226064
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1.90954638692403
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0.720915677264753
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1.12662982290344
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0.873322331496494
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2.73997287136118
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2.49376474567805
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1.71855478201569
1.18881581030952
2.56216432022585
0.443671331515407
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1.31101180554756
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0.204099941303661
-3.73653059676392
2.41609709604484
0.459211471769727
-1.95214272484412
-0.467671553906553
-2.54700443680883
0.530260345025432
2.59956805328044
-0.048523881273836
-1.31831688661928
1.0921477226447
1.49992331623734
-1.21692016754482
-0.350713444316563
-0.709451653003092
0.660733517233603

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=312870&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=312870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=312870&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.26096	-0.21948	-0.082771	8.1863e-17	0.074615	0.18637	0.23746	0.11796	0.15739
median	-0.24712	-0.21302	-0.019887	0.14063	0.24119	0.43439	0.49836	0.17348	0.26108
midrange	-0.95008	-0.93465	-0.53241	-0.51161	-0.33046	0.04463	0.080216	0.28269	0.20194
mode	-3.6073	-2.0475	-0.68508	8.0434e-17	0.79052	2.1163	2.9576	1.3235	1.4756
mode k.dens	-0.33283	-0.24036	0.24485	0.5299	0.74509	1.0996	1.3095	0.39302	0.50024

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.26096 & -0.21948 & -0.082771 & 8.1863e-17 & 0.074615 & 0.18637 & 0.23746 & 0.11796 & 0.15739 \tabularnewline
median & -0.24712 & -0.21302 & -0.019887 & 0.14063 & 0.24119 & 0.43439 & 0.49836 & 0.17348 & 0.26108 \tabularnewline
midrange & -0.95008 & -0.93465 & -0.53241 & -0.51161 & -0.33046 & 0.04463 & 0.080216 & 0.28269 & 0.20194 \tabularnewline
mode & -3.6073 & -2.0475 & -0.68508 & 8.0434e-17 & 0.79052 & 2.1163 & 2.9576 & 1.3235 & 1.4756 \tabularnewline
mode k.dens & -0.33283 & -0.24036 & 0.24485 & 0.5299 & 0.74509 & 1.0996 & 1.3095 & 0.39302 & 0.50024 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=312870&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]-0.26096[/C][C]-0.21948[/C][C]-0.082771[/C][C]8.1863e-17[/C][C]0.074615[/C][C]0.18637[/C][C]0.23746[/C][C]0.11796[/C][C]0.15739[/C][/ROW]
[ROW][C]median[/C][C]-0.24712[/C][C]-0.21302[/C][C]-0.019887[/C][C]0.14063[/C][C]0.24119[/C][C]0.43439[/C][C]0.49836[/C][C]0.17348[/C][C]0.26108[/C][/ROW]
[ROW][C]midrange[/C][C]-0.95008[/C][C]-0.93465[/C][C]-0.53241[/C][C]-0.51161[/C][C]-0.33046[/C][C]0.04463[/C][C]0.080216[/C][C]0.28269[/C][C]0.20194[/C][/ROW]
[ROW][C]mode[/C][C]-3.6073[/C][C]-2.0475[/C][C]-0.68508[/C][C]8.0434e-17[/C][C]0.79052[/C][C]2.1163[/C][C]2.9576[/C][C]1.3235[/C][C]1.4756[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.33283[/C][C]-0.24036[/C][C]0.24485[/C][C]0.5299[/C][C]0.74509[/C][C]1.0996[/C][C]1.3095[/C][C]0.39302[/C][C]0.50024[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=312870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=312870&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	-0.26096	-0.21948	-0.082771	8.1863e-17	0.074615	0.18637	0.23746	0.11796	0.15739
median	-0.24712	-0.21302	-0.019887	0.14063	0.24119	0.43439	0.49836	0.17348	0.26108
midrange	-0.95008	-0.93465	-0.53241	-0.51161	-0.33046	0.04463	0.080216	0.28269	0.20194
mode	-3.6073	-2.0475	-0.68508	8.0434e-17	0.79052	2.1163	2.9576	1.3235	1.4756
mode k.dens	-0.33283	-0.24036	0.24485	0.5299	0.74509	1.0996	1.3095	0.39302	0.50024

Figure 1

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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):

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)
}
x<-na.omit(x)
(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