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Author

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R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Wed, 13 Dec 2017 10:52:19 +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/2017/Dec/13/t1513158771cx5pxkng92ybads.htm/, Retrieved Wed, 15 May 2024 11:15:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309235, Retrieved Wed, 15 May 2024 11:15:12 +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] [] [2017-12-13 09:52:19] [37d4e299f63d60aeb1b8f01e350555e9] [Current]

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0.408940367496196
-0.191303874503801
0.111600037010135
0.0672558440101349
-0.0138233995038002
0.4071745764962
-0.3954455495038
-0.650445356503801
-0.530994491503801
0.472403874496199
0.721974343496199
-0.634541764503801
-0.225104312989865
0.0741408234961994
0.330170878010135
1.3169878334962
0.947214714010135
0.401238678496199
-0.0417548395038005
-0.149749929503801
-0.321614569503801
-0.344741699989865
0.3234957354962
0.5072943894962
-0.5259743205038
-0.6774863385038
-0.204952799503801
0.401000338496199
-0.775070288679054
-0.392745685165118
-0.501307072697213
-0.146554392
-0.352370776503801
0.146554392
-0.0905101784953546
0.358349311142857
0.549755170834882
-0.175465967857143
0.156946347834882
-0.232379362697213
0.216673512302787
-0.303090062989865
-0.179499387989865
-0.710791041989865
-0.425900615857143
-0.131674348697213
0.453850193142857
0.768498514834882
0.441783668834882
-0.00330523385714289
0.420131324142857
0.0571837525046454
-0.0429999474953547
-0.505482061165118
0.256928389504645
0.33991683199071
0.648687271788851
-0.0372079185038003
-0.520518848009291
-0.509162037165119
-0.627659010857143
0.265476369834882

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309235&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309235&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309235&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.14767	-0.10543	-0.043398	-5.5813e-17	0.034973	0.09166	0.12061	0.059528	0.07837
median	-0.19229	-0.17557	-0.09051	-0.039481	-0.00055031	0.10714	0.15724	0.084428	0.08996
midrange	-0.080839	-0.026548	0.086072	0.27096	0.27096	0.31975	0.33327	0.12138	0.18489
mode	-0.67782	-0.58759	-0.22601	-5.594e-17	0.27827	0.55404	0.77029	0.36715	0.50428
mode k.dens	-0.56785	-0.48793	-0.21563	-0.15869	0.27617	0.39692	0.41684	0.28211	0.4918

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.14767 & -0.10543 & -0.043398 & -5.5813e-17 & 0.034973 & 0.09166 & 0.12061 & 0.059528 & 0.07837 \tabularnewline
median & -0.19229 & -0.17557 & -0.09051 & -0.039481 & -0.00055031 & 0.10714 & 0.15724 & 0.084428 & 0.08996 \tabularnewline
midrange & -0.080839 & -0.026548 & 0.086072 & 0.27096 & 0.27096 & 0.31975 & 0.33327 & 0.12138 & 0.18489 \tabularnewline
mode & -0.67782 & -0.58759 & -0.22601 & -5.594e-17 & 0.27827 & 0.55404 & 0.77029 & 0.36715 & 0.50428 \tabularnewline
mode k.dens & -0.56785 & -0.48793 & -0.21563 & -0.15869 & 0.27617 & 0.39692 & 0.41684 & 0.28211 & 0.4918 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309235&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]-0.14767[/C][C]-0.10543[/C][C]-0.043398[/C][C]-5.5813e-17[/C][C]0.034973[/C][C]0.09166[/C][C]0.12061[/C][C]0.059528[/C][C]0.07837[/C][/ROW]
[ROW][C]median[/C][C]-0.19229[/C][C]-0.17557[/C][C]-0.09051[/C][C]-0.039481[/C][C]-0.00055031[/C][C]0.10714[/C][C]0.15724[/C][C]0.084428[/C][C]0.08996[/C][/ROW]
[ROW][C]midrange[/C][C]-0.080839[/C][C]-0.026548[/C][C]0.086072[/C][C]0.27096[/C][C]0.27096[/C][C]0.31975[/C][C]0.33327[/C][C]0.12138[/C][C]0.18489[/C][/ROW]
[ROW][C]mode[/C][C]-0.67782[/C][C]-0.58759[/C][C]-0.22601[/C][C]-5.594e-17[/C][C]0.27827[/C][C]0.55404[/C][C]0.77029[/C][C]0.36715[/C][C]0.50428[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.56785[/C][C]-0.48793[/C][C]-0.21563[/C][C]-0.15869[/C][C]0.27617[/C][C]0.39692[/C][C]0.41684[/C][C]0.28211[/C][C]0.4918[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309235&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309235&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	-0.14767	-0.10543	-0.043398	-5.5813e-17	0.034973	0.09166	0.12061	0.059528	0.07837
median	-0.19229	-0.17557	-0.09051	-0.039481	-0.00055031	0.10714	0.15724	0.084428	0.08996
midrange	-0.080839	-0.026548	0.086072	0.27096	0.27096	0.31975	0.33327	0.12138	0.18489
mode	-0.67782	-0.58759	-0.22601	-5.594e-17	0.27827	0.55404	0.77029	0.36715	0.50428
mode k.dens	-0.56785	-0.48793	-0.21563	-0.15869	0.27617	0.39692	0.41684	0.28211	0.4918

Figure 1

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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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