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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

Wed, 21 Dec 2016 21:39:24 +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/21/t1482352810kjnxwye72egk6tg.htm/, Retrieved Mon, 06 May 2024 21:29:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302497, Retrieved Mon, 06 May 2024 21:29:16 +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] [bootstrap plot] [2016-12-21 20:39:24] [6f830dc7e8de22be3233942ffbe3aaba] [Current]

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Histogram

Boxplots

-1.893
2.514
0.141
0.624
-1.376
3.18
-1.82
2.216
-1.784
-1.894
2.106
-0.376
3.624
1.699
-1.376
-2.894
3.624
-0.2662
-0.7844
-0.4859
-6.376
2.514
2.624
-0.7844
-1.6
1.624
0.624
2.216
0.2156
-1.858
2.29
3.514
-11.78
2.773
-0.8942
-1.376
-0.1915
1.844
-0.8942
-1.376
0.1058
-1.893
3.216
2.624
-9.674
-3.784
-0.376
0.1422
1.106
1.031
-0.5605
1.327
2.734
-0.1563
2.624
-3.56
-0.376
-3.376
-4.894
0.5141
0.5494
0.5141
-0.376
-0.2268
-2.894
-0.2662
-3.156
-0.4859
-2.376
3.624
-3.968
-3.192
1.44
-2.376
2.18
1.734
-2.156
1.106
2.216
-0.5635
-2.784
0.5141
2.216
-3.969
2.216
1.106
3.624
-1.486
-0.6692
-1.376
3.141
-1.784
2.141
1.624
1.624
-1.451
3.106
0.5141
1.216
2.624
3.844
-3.376
0.2156
-1.376
-0.7844
1.624
1.624
-2.376
-0.9677
-4.376
1.217
-2.894
1.624
0.5141
0.2156
3.514
-0.2662
-2.376
-1.266
1.624
3.624
0.2156
0.624
0.5141
0.2156
-1.376
0.624
2.327
0.624
3.549
-1.192
0.3267
-1.376
1.216
-1.376
1.031
3.141
2.808
-2.266
-0.376
-0.8578
0.141
1.844
3.031
0.6986
1.624
-0.7832
-0.376
-5.486
-1.376
2.252
0.624
-0.8942
-3.968
2.624
1.106
-1.376
0.03117
2.734
1.624
2.624
-0.1915
-0.376
0.03232
-2.266

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=302497&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=302497&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302497&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	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.4529	-0.29465	-0.093474	6.4182e-05	0.14938	0.28323	0.35862	0.1799	0.24286
median	-0.376	-0.19326	0.03232	0.1422	0.2156	0.5141	0.624	0.22655	0.18328
midrange	-4.078	-4.078	-3.9955	-3.968	-2.915	-0.931	-0.821	1.0304	1.0805
mode	-1.376	-1.376	-1.376	-1.376	1.624	2.624	3.624	1.4258	3
mode k.dens	-1.205	-0.7878	-0.38322	-0.10751	0.2757	1.9169	2.1842	0.73119	0.65892

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.4529 & -0.29465 & -0.093474 & 6.4182e-05 & 0.14938 & 0.28323 & 0.35862 & 0.1799 & 0.24286 \tabularnewline
median & -0.376 & -0.19326 & 0.03232 & 0.1422 & 0.2156 & 0.5141 & 0.624 & 0.22655 & 0.18328 \tabularnewline
midrange & -4.078 & -4.078 & -3.9955 & -3.968 & -2.915 & -0.931 & -0.821 & 1.0304 & 1.0805 \tabularnewline
mode & -1.376 & -1.376 & -1.376 & -1.376 & 1.624 & 2.624 & 3.624 & 1.4258 & 3 \tabularnewline
mode k.dens & -1.205 & -0.7878 & -0.38322 & -0.10751 & 0.2757 & 1.9169 & 2.1842 & 0.73119 & 0.65892 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302497&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]-0.4529[/C][C]-0.29465[/C][C]-0.093474[/C][C]6.4182e-05[/C][C]0.14938[/C][C]0.28323[/C][C]0.35862[/C][C]0.1799[/C][C]0.24286[/C][/ROW]
[ROW][C]median[/C][C]-0.376[/C][C]-0.19326[/C][C]0.03232[/C][C]0.1422[/C][C]0.2156[/C][C]0.5141[/C][C]0.624[/C][C]0.22655[/C][C]0.18328[/C][/ROW]
[ROW][C]midrange[/C][C]-4.078[/C][C]-4.078[/C][C]-3.9955[/C][C]-3.968[/C][C]-2.915[/C][C]-0.931[/C][C]-0.821[/C][C]1.0304[/C][C]1.0805[/C][/ROW]
[ROW][C]mode[/C][C]-1.376[/C][C]-1.376[/C][C]-1.376[/C][C]-1.376[/C][C]1.624[/C][C]2.624[/C][C]3.624[/C][C]1.4258[/C][C]3[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.205[/C][C]-0.7878[/C][C]-0.38322[/C][C]-0.10751[/C][C]0.2757[/C][C]1.9169[/C][C]2.1842[/C][C]0.73119[/C][C]0.65892[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302497&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302497&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	-0.4529	-0.29465	-0.093474	6.4182e-05	0.14938	0.28323	0.35862	0.1799	0.24286
median	-0.376	-0.19326	0.03232	0.1422	0.2156	0.5141	0.624	0.22655	0.18328
midrange	-4.078	-4.078	-3.9955	-3.968	-2.915	-0.931	-0.821	1.0304	1.0805
mode	-1.376	-1.376	-1.376	-1.376	1.624	2.624	3.624	1.4258	3
mode k.dens	-1.205	-0.7878	-0.38322	-0.10751	0.2757	1.9169	2.1842	0.73119	0.65892

Figure 1

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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 = 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)
}
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