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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 14:03:42 +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/t1418652244wy1tb2q7g90mojv.htm/, Retrieved Thu, 16 May 2024 11:31:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268432, Retrieved Thu, 16 May 2024 11:31:56 +0000

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Estimated Impact

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

-       [Bootstrap Plot - Central Tendency] [] [2014-12-15 14:03:42] [ff8f75f765a8f6d34a5ce09978012557] [Current]

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Dataseries X:

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Histogram

Boxplots

7.5	1.8	2.1	1.5
2.5	1.6	1.5	1.8
6	2.1	2	2.1
6.5	2.2	2	2.1
1	2.3	2.1	1.9
1	2.1	2	1.6
5.5	2.7	2.3	2.1
8.5	2.1	2.1	2.1
6.5	2.4	2.1	2.2
4.5	2.9	2.2	1.5
2	2.2	2.1	1.9
5	2.1	2.1	2.2
0.5	2.2	2.1	1.6
5	2.2	2	1.5
5	2.7	2.3	1.9
2.5	1.9	1.8	0.1
5	2	2	2.2
5.5	2.5	2.2	1.8
3.5	2.2	2	1.6
3	2.3	2.1	2.2
4	1.9	2	2.1
0.5	2.1	1.8	1.9
6.5	3.5	2.2	1.6
4.5	2.1	2.2	1.9
7.5	2.3	1.7	2.2
5.5	2.3	2.1	1.8
4	2.2	2.3	2.4
7.5	3.5	2.7	2.4
7	1.9	1.9	2.5
4	1.9	2	1.9
5.5	1.9	2	2.1
2.5	1.9	1.9	1.9
5.5	2.1	2	2.1
0.5	1.6	2	1.9
3.5	2	2	1.5
2.5	3.2	2.1	1.9
4.5	2.3	2	2.1
4.5	2.5	1.8	1.5
4.5	1.8	2	2.1
6	2.4	2.2	2.1
2.5	2.8	2.2	1.8
5	2.3	2.1	2.4
0	2	1.8	2.1
5	2.5	1.9	1.9
6.5	2.3	2.1	2.1
5	1.8	2	1.9
6	1.9	1.9	2.4
4.5	2.6	2.2	2.1
5.5	2	2	2.2
1	2.6	2	2.2
7.5	1.6	1.7	1.8
6	2.2	2	2.1
5	2.1	2.2	2.4
1	1.8	1.7	2.2
5	1.8	2	2.1
6.5	1.9	2.2	1.5
7	2.4	2	1.9
4.5	1.9	1.9	1.8
0	2	2	1.8
8.5	2.1	2	1.6
3.5	1.7	1.6	1.2
7.5	1.9	2.1	1.8
3.5	2.1	2.1	1.5
6	2.4	2	2.1
1.5	1.8	1.9	2.4
9	2.3	2.2	2.4
3.5	2.1	2.1	1.5
3.5	2	1.8	1.8
4	2.8	2.3	2.1
6.5	2	2.3	2.2
7.5	2.7	2.2	2.1
6	2.1	2.1	1.9
5	2.9	2.2	2.1
5.5	2	1.9	1.9
3.5	1.8	1.8	1.6
7.5	2.6	2.1	2.4
1	2.5	1.8	1.9
6.5	2.1	2	1.9
6.5	2.3	2.1	2.1
6.5	2.2	2.1	1.8
7	2	2.1	2.1
3.5	2.2	1.8	2.4
1.5	2.1	2	2.1
4	2.1	2.1	2.2
7.5	1.9	1.9	2.1
4.5	2	2.1	2.2
0	1.7	1	1.6
3.5	2.2	2.2	2.4
5.5	2.2	2.1	2.1
5	2.3	1.9	1.9
4.5	2.4	2	2.4
2.5	2.1	1.9	2.1
7.5	1.9	2	1.8
7	1.7	1.8	2.1
0	1.8	2	1.8
4.5	1.5	2	1.9
3	1.9	2	1.9
1.5	1.9	1.8	2.4
3.5	1.7	2	1.8
2.5	1.9	1.1	1.8
5.5	1.9	1.8	2.1
8	1.8	1.8	2.1
1	2.4	2	2.4
5	1.8	1.9	1.9
4.5	1.9	2.1	1.8
3	1.8	1.6	1.8
3	2.1	2.2	2.2
8	1.9	1.9	2.4
2.5	2.2	2	1.8
7	2	2.1	2.4
0	1.7	1.3	1.8
1	1.7	1.8	1.9
3.5	1.8	1.9	2.4
5.5	1.9	2.1	2.1
5.5	1.8	1.8	1.9
0.5	1	0.75	2.1
7.5	1	1.5	2.7
9	4	3	2.1
9.5	4	2.25	2.1
8.5	3	3	2.1
7	2	1.5	2.1
8	4	3	2.1
10	4	3	2.1
7	4	3	2.1
8.5	2	0.75	2.1
9	4	3	2.4
9.5	1	2.25	1.95
4	3	1.5	2.1
6	3	1.5	2.1
8	4	2.25	1.95
5.5	3	3	2.1
9.5	4	3	2.4
7.5	3	1.5	2.1
7	3	2.25	2.25
7.5	4	2.25	2.4
8	3	1.5	2.25
7	3	2.25	2.55
7	2	1.5	1.95
6	2	2.25	2.4
10	3	2.25	2.1
2.5	1	3	2.1
9	4	3	2.4
8	3	3	2.1
6	2	1.5	2.1
8.5	4	3	2.25
6	4	3	2.25
9	4	2.25	2.4
8	4	1.5	2.1
8	4	2.25	2.1
9	4	2.25	2.4
5.5	3	3	2.1
5	4	0.75	1.95
7	3	2.25	2.1
5.5	4	3	2.25
9	4	3	2.25
2	4	1.5	2.4
8.5	3	2.25	2.25
9	4	3	2.25
8.5	4	2.25	2.1
9	2	1.5	2.1
7.5	2	2.25	2.1
10	4	2.25	2.7
9	3	1.5	2.1
7.5	3	2.25	2.1
6	2	1.5	2.25
10.5	3	2.25	2.7
8.5	2	3	2.4
8	4	3	2.1
10	1	3	2.1
10.5	4	3	2.4
6.5	1	1.5	1.95
9.5	4	2.25	2.7
8.5	3	1.5	2.1
7.5	3	2.25	2.25
5	2	2.25	2.1
8	3	2.25	2.7
10	3	3	2.1
7	4	1.5	2.1
7.5	4	2.25	1.65
7.5	4	2.25	1.65
9.5	3	3	2.1
6	3	2.25	2.1
10	4	3	2.1
7	4	2.25	2.1
3	1	1.5	2.1
6	2	3	2.4
7	3	1.5	2.4
10	4	3	2.1
7	3	3	2.25
3.5	4	3	2.4
8	3	3	2.1
10	3	2.25	2.1
5.5	3	2.25	2.4
6	3	0.75	2.4
6.5	1	3	2.1
6.5	1	0.75	2.1
8.5	3	1.5	2.4
4	2	1.5	2.1
9.5	3	3	2.7
8	2	1.5	2.1
8.5	2	2.25	2.1
5.5	4	3	2.25
7	2	3	2.1
9	2	1.5	2.4
8	3	3	2.25
10	4	3	2.25
8	2	1.5	2.1
6	4	1.5	2.1
8	3	2.25	2.4
5	4	1.5	2.25
9	2	1.5	2.1
4.5	1	2.25	2.1
8.5	1	1.5	1.65
7	1	2.25	1.65
9.5	4	3	2.7
8.5	3	3	2.1
7.5	1	0.75	1.95
7.5	4	1.5	2.25
5	3	1.5	2.4
7	2	2.25	1.95
8	4	2.25	2.1
5.5	3	1.5	2.4
8.5	3	2.25	2.1
7.5	4	0.75	2.1
9.5	4	2.25	2.4
7	1	0.75	2.4
8	3	2.25	2.4
8.5	4	3	2.25
3.5	1	0.75	2.4
6.5	3	0.75	2.1
6.5	4	3	2.1
10.5	4	3	1.8
8.5	1	3	2.7
8	4	3	2.1
10	2	1.5	2.1
10	3	3	2.4
9.5	4	3	2.55
9	4	3	2.55
10	4	3	2.1
7.5	2	1.5	2.1
4.5	4	2.25	2.1
4.5	2	0.75	2.25
0.5	1	0.75	2.25
6.5	1	2.25	2.1
4.5	4	3	2.1
5.5	2	2.25	1.95
5	2	3	2.4
6	3	2.25	2.1
4	2	3	2.4
8	3	1.5	2.4
10.5	4	3	2.4
8.5	4	3	2.25
6.5	2	0.75	1.95
8	3	1.5	2.1
8.5	4	3	2.1
5.5	3	3	2.55
7	4	3	2.1
5	4	2.25	2.1
3.5	4	2.25	2.1
5	2	3	1.95
9	2	1.5	2.25
8.5	2	2.25	2.4
5	4	2.25	1.95
9.5	3	2.25	2.1
3	2	0.75	2.1
1.5	2	2.25	1.95
6	3	1.5	2.1
0.5	3	2.25	2.1
6.5	1	1.5	1.95
7.5	2	0.75	2.1
4.5	2	1.5	1.95
8	3	1.5	2.4
9	3	2.25	2.4
7.5	2	1.5	2.4
8.5	2	1.5	1.95
7	3	3	2.7
9.5	3	2.25	2.1
6.5	1	1.5	1.95
9.5	3	0.75	2.1
6	2	2.25	1.95
8	2	3	2.1
9.5	3	3	2.25
8	3	1.5	2.7
8	3	1.5	2.1
9	3	2.25	2.4
5	1	0.75	1.35

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 0 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268432&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268432&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	0 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

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