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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

Tue, 20 Dec 2016 13:19:40 +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/2016/Dec/20/t14822364810uvgfc0x68767s6.htm/, Retrieved Sun, 28 Apr 2024 07:09:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301626, Retrieved Sun, 28 Apr 2024 07:09:42 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Bootstrap plot residuals

Estimated Impact

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

-       [Bootstrap Plot - Central Tendency] [Bootstrap plot re...] [2016-12-20 12:19:40] [16e0888ced5f28ae20ce1ff74f042113] [Current]

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Dataseries X:

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Histogram

Boxplots

-2.574
-3.04
0.4953
0.9604
-1.04
0.4255
-0.5745
0.9604
-0.03959
0.9604
-4.04
-3.04
1.96
3.426
-0.03959
-1.04
-4.04
-1.04
2.96
3.96
2.96
-3.04
0.9604
-2.04
1.426
0.9604
-2.04
4.96
-1.04
0.9604
-0.5745
-4.04
-0.03959
-1.109
0.9604
-1.04
4.426
0.9604
-3.04
-0.03959
-3.04
0.4255
-5.04
-1.04
-1.04
-2.04
2.96
1.96
1.426
1.96
1.495
-1.505
-0.5745
-2.04
-3.04
-1.04
0.4953
-1.04
-0.03959
-1.04
0.9604
-0.5047
-0.03959
-1.04
-2.109
-0.03959
2.96
0.9604
0.9604
-1.04
-2.04
-0.03959
-2.574
-0.5047
3.96
1.426
3.96
1.96
-2.04
-0.03959
0.4255
-0.03959
-0.03959
-1.04
-2.505
-1.04
0.9604
-1.04
-0.03959
0.9604
-1.04
-0.5047
2.96
0.4953
-1.04
-0.03959
2.495
-6.04
0.9604
-4.04
0.9604
0.9604
-0.03959
0.9604
3.96
-0.03959
4.96
1.96
2.96
3.96
-1.04
-4.04
0.4255
-1.04
-2.04
-1.04
-2.04
-1.04
1.96
-2.04
-0.03959
2.96
-0.03959
-2.04
-0.03959
2.96
-1.04
-3.04
-0.03959
-2.574
2.96
0.4953
-3.574
1.426
1.96
2.96
1.426
-2.04
0.4953
-4.505
-0.5745
2.96
2.96
1.96
0.4953
1.96
1.495
1.426
1.96
0.4255
2.96
-0.03959
-2.04
0.9604
-0.03959
0.9604
-0.03959
-1.04
1.96
-1.04
-0.5047
-1.04
-1.04
0.9604
-0.5745
-0.03959
-1.04
-2.04

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	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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301626&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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301626&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301626&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	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	-0.42572	-0.36762	-0.10505	-0.00018196	0.098965	0.25479	0.35804	0.16154	0.20401
median	-0.5745	-0.045404	-0.03959	-0.03959	-0.03959	0.4255	0.4953	0.12276	0
midrange	-1.04	-1.04	-0.54	-0.54	-0.04	0.46	0.46	0.3431	0.5
mode	-1.0425	-1.04	-1.04	-1.04	-0.03959	0.9604	0.9704	0.7052	1.0004
mode k.dens	-1.0413	-0.89445	-0.23651	-0.20163	0.1289	0.95972	1.4576	0.47059	0.36541

\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 & -0.42572 & -0.36762 & -0.10505 & -0.00018196 & 0.098965 & 0.25479 & 0.35804 & 0.16154 & 0.20401 \tabularnewline
median & -0.5745 & -0.045404 & -0.03959 & -0.03959 & -0.03959 & 0.4255 & 0.4953 & 0.12276 & 0 \tabularnewline
midrange & -1.04 & -1.04 & -0.54 & -0.54 & -0.04 & 0.46 & 0.46 & 0.3431 & 0.5 \tabularnewline
mode & -1.0425 & -1.04 & -1.04 & -1.04 & -0.03959 & 0.9604 & 0.9704 & 0.7052 & 1.0004 \tabularnewline
mode k.dens & -1.0413 & -0.89445 & -0.23651 & -0.20163 & 0.1289 & 0.95972 & 1.4576 & 0.47059 & 0.36541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301626&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]-0.42572[/C][C]-0.36762[/C][C]-0.10505[/C][C]-0.00018196[/C][C]0.098965[/C][C]0.25479[/C][C]0.35804[/C][C]0.16154[/C][C]0.20401[/C][/ROW]
[ROW][C]median[/C][C]-0.5745[/C][C]-0.045404[/C][C]-0.03959[/C][C]-0.03959[/C][C]-0.03959[/C][C]0.4255[/C][C]0.4953[/C][C]0.12276[/C][C]0[/C][/ROW]
[ROW][C]midrange[/C][C]-1.04[/C][C]-1.04[/C][C]-0.54[/C][C]-0.54[/C][C]-0.04[/C][C]0.46[/C][C]0.46[/C][C]0.3431[/C][C]0.5[/C][/ROW]
[ROW][C]mode[/C][C]-1.0425[/C][C]-1.04[/C][C]-1.04[/C][C]-1.04[/C][C]-0.03959[/C][C]0.9604[/C][C]0.9704[/C][C]0.7052[/C][C]1.0004[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.0413[/C][C]-0.89445[/C][C]-0.23651[/C][C]-0.20163[/C][C]0.1289[/C][C]0.95972[/C][C]1.4576[/C][C]0.47059[/C][C]0.36541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301626&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301626&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	-0.42572	-0.36762	-0.10505	-0.00018196	0.098965	0.25479	0.35804	0.16154	0.20401
median	-0.5745	-0.045404	-0.03959	-0.03959	-0.03959	0.4255	0.4953	0.12276	0
midrange	-1.04	-1.04	-0.54	-0.54	-0.04	0.46	0.46	0.3431	0.5
mode	-1.0425	-1.04	-1.04	-1.04	-0.03959	0.9604	0.9704	0.7052	1.0004
mode k.dens	-1.0413	-0.89445	-0.23651	-0.20163	0.1289	0.95972	1.4576	0.47059	0.36541

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 = 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):

par4 <- 'P1 P5 Q1 Q3 P95 P99'
par3 <- '0'
par2 <- '5'
par1 <- '200'
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