Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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, 18 Dec 2017 14:19:35 +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/18/t1513603343akuqio6xs1awqsm.htm/, Retrieved Tue, 14 May 2024 14:34:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310172, Retrieved Tue, 14 May 2024 14:34:10 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Bootstrap Plot - Central Tendency] [boodstrap plot ] [2017-12-18 13:19:35] [cc67e55ad731ea545e369166f6dbbbc3] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

-0.829399843258336
-0.420870186280318
-0.420870186280289
1.34107819871831
-0.420870186280284
0.579129813719717
-0.631366336802917
0.960104006219015
0.170600156741648
0.960104006219015
-0.0398959937809846
0.341078198718315
-1.03989599378098
-0.0398959937809846
0.170600156741648
0.749607855696383
-0.0398959937809846
-0.631366336802917
0.368633663197084
-0.250392144303617
-1.25039214430362
1.14304469226288
-0.0398959937809846
0.960104006219015
-0.0398959937809846
0.170600156741648
1.13058204819568
1.17060015674165
0.341078198718315
0.170600156741648
0.579129813719717
0.170600156741648
-0.829399843258352
-0.631366336802917
-1.21037403575765
0.170600156741648
-1.23792950023642
0.960104006219015
0.170600156741648
-0.829399843258352
1.17060015674165
-0.0398959937809846
-2.82939984325835
-0.0398959937809846
-0.0398959937809846
-0.0674514582597537
0.960104006219015
1.34107819871831
0.789625964242349
-0.0398959937809846
0.960104006219015
1.14304469226288
1.34107819871831
0.960104006219015
-0.0398959937809846
0.960104006219015
-0.420870186280284
-0.250392144303617
0.143044692262879
-0.420870186280284
-0.658921801281686
0.341078198718315
0.341078198718315
0.368633663197084
-1.03989599378098
0.749607855696383
-1.25039214430362
-0.0398959937809846
0.368633663197084
-0.23792950023642
-0.856955307737121
-1.65892180128169
0.368633663197084
0.368633663197084
0.170600156741648
-0.0674514582597537
-0.869417951804318
1.17060015674165
-2.03989599378098
-0.0398959937809846
0.960104006219015
-0.829399843258352
-0.0398959937809846
-0.250392144303617
0.368633663197084
-0.0398959937809846
-0.0398959937809846
-1.03989599378098
0.551574349240947
-0.0398959937809846
-0.0398959937809846
0.960104006219015
-0.0398959937809846
-1.03989599378098
0.341078198718315
1.76207049976358
0.368633663197084
-0.0674514582597537
0.551574349240947
-0.0398959937809846
0.960104006219015
-0.829399843258352
0.341078198718315
0.722052391217614
-0.420870186280284
0.960104006219015
1.34107819871831
0.96010400621901

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310172&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310172&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310172&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	-0.12421	-0.088298	0.029623	0.087086	0.13314	0.2242	0.27109	0.077503	0.10352
median	-0.039896	-0.039896	-0.039896	0.051574	0.1706	0.34108	0.34108	0.12021	0.2105
midrange	-0.74466	-0.74416	-0.53366	-0.53366	-0.34941	0.051574	0.25584	0.24031	0.18426
mode	-0.039896	-0.039896	-0.039896	-0.039896	-0.039896	0.9601	0.9601	0.27778	0
mode k.dens	-0.051819	-0.048158	-0.032798	0.010191	0.06161	0.9601	0.97488	0.20555	0.094408

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P0.5 & P2.5 & Q1 & Estimate & Q3 & P97.5 & P99.5 & S.D. & IQR \tabularnewline
mean & -0.12421 & -0.088298 & 0.029623 & 0.087086 & 0.13314 & 0.2242 & 0.27109 & 0.077503 & 0.10352 \tabularnewline
median & -0.039896 & -0.039896 & -0.039896 & 0.051574 & 0.1706 & 0.34108 & 0.34108 & 0.12021 & 0.2105 \tabularnewline
midrange & -0.74466 & -0.74416 & -0.53366 & -0.53366 & -0.34941 & 0.051574 & 0.25584 & 0.24031 & 0.18426 \tabularnewline
mode & -0.039896 & -0.039896 & -0.039896 & -0.039896 & -0.039896 & 0.9601 & 0.9601 & 0.27778 & 0 \tabularnewline
mode k.dens & -0.051819 & -0.048158 & -0.032798 & 0.010191 & 0.06161 & 0.9601 & 0.97488 & 0.20555 & 0.094408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310172&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P0.5[/C][C]P2.5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P97.5[/C][C]P99.5[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]-0.12421[/C][C]-0.088298[/C][C]0.029623[/C][C]0.087086[/C][C]0.13314[/C][C]0.2242[/C][C]0.27109[/C][C]0.077503[/C][C]0.10352[/C][/ROW]
[ROW][C]median[/C][C]-0.039896[/C][C]-0.039896[/C][C]-0.039896[/C][C]0.051574[/C][C]0.1706[/C][C]0.34108[/C][C]0.34108[/C][C]0.12021[/C][C]0.2105[/C][/ROW]
[ROW][C]midrange[/C][C]-0.74466[/C][C]-0.74416[/C][C]-0.53366[/C][C]-0.53366[/C][C]-0.34941[/C][C]0.051574[/C][C]0.25584[/C][C]0.24031[/C][C]0.18426[/C][/ROW]
[ROW][C]mode[/C][C]-0.039896[/C][C]-0.039896[/C][C]-0.039896[/C][C]-0.039896[/C][C]-0.039896[/C][C]0.9601[/C][C]0.9601[/C][C]0.27778[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.051819[/C][C]-0.048158[/C][C]-0.032798[/C][C]0.010191[/C][C]0.06161[/C][C]0.9601[/C][C]0.97488[/C][C]0.20555[/C][C]0.094408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310172&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310172&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	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	-0.12421	-0.088298	0.029623	0.087086	0.13314	0.2242	0.27109	0.077503	0.10352
median	-0.039896	-0.039896	-0.039896	0.051574	0.1706	0.34108	0.34108	0.12021	0.2105
midrange	-0.74466	-0.74416	-0.53366	-0.53366	-0.34941	0.051574	0.25584	0.24031	0.18426
mode	-0.039896	-0.039896	-0.039896	-0.039896	-0.039896	0.9601	0.9601	0.27778	0
mode k.dens	-0.051819	-0.048158	-0.032798	0.010191	0.06161	0.9601	0.97488	0.20555	0.094408

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Figure 11

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P0.5 P2.5 Q1 Q3 P97.5 P99.5 ;

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