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

Tue, 13 Dec 2016 11:47:55 +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/13/t14816261455x6my7ob4i8xwnp.htm/, Retrieved Sun, 05 May 2024 06:47:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299040, Retrieved Sun, 05 May 2024 06:47:14 +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

120

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...] [2016-12-13 10:47:55] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

-0.07114
0.9168
1.316
0.2179
0.3155
0.8239
2.218
0.8239
0.2059
0.2059
1.316
-0.2905
0.7095
-0.0664
0.3155
1.824
0.8239
-1.909
1.316
1.218
-0.6845
0.3155
-0.6845
1.316
-0.1761
1.218
-0.6845
1.332
-0.1928
-1.575
-1.794
0.8072
0.4252
-0.558
0.2059
-0.1761
-0.1808
-0.5748
2.316
-1.193
0.2059
-1.066
-0.9085
-0.7989
-0.2905
0.3155
-0.8918
-2.684
0.2059
-0.3025
-0.3025
-3.575
-0.1808
-2.684
0.2179
1.824
0.7095
-0.6845
-0.3025
1.332
0.3155
0.8239
-0.6845
-1.465
1.71
-1.29
-1.799
1.218
0.2179
-2.188
-2.684
-2.684
0.9216
-0.1761
0.3155
-0.7821
0.9336
2.425
0.3155
-2.066
0.3155
1.316
1.316
3.807
0.4132
2.316
-0.1761
1.824
0.9289
-0.6845
1.316
0.3155
0.2059
0.2179
0.3155
1.807
1.413
-0.7941
1.917
-0.6845
-3.193
0.2179
0.4132
0.7095
0.7095
-0.6845
0.3155
0.8239
-2.684
-0.2905
-1.672
3.71
0.8239
0.3155
0.4132
-0.1928
-1.684
-0.2905
-0.7821
0.3155
-0.6845
0.2059
-0.1761
0.7095
0.3155
0.2179
-0.2905
-1.169
0.7095
2.201
-1.274
-3.066
0.7095
0.6975
-1.794
1.304
0.2059
-2.29
-1.176
0.3155
-2.684
1.316
-1.684
3.201
-0.5748
0.3155
-1.672
-0.7941
0.7095
-0.6845
-2.782
-0.6845
0.8072
-2.684
1.442
2.316
0.8192
2.316
-2.188
-1.193
0.2011
-1.066
0.3155
-2.684
2.048

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299040&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]

5 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299040&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299040&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	5 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	-0.23383	-0.19762	-0.079462	6.9455e-05	0.079074	0.21688	0.26537	0.1056	0.15854
median	-0.1761	-0.073764	0.2059	0.2059	0.2179	0.3155	0.3155	0.11012	0.012
midrange	-0.57527	-0.38536	0.116	0.116	0.307	0.5125	0.51324	0.18911	0.191
mode	-1.1842	-0.6845	0.3155	0.3155	0.3155	0.7095	1.316	0.39833	0
mode k.dens	-0.69316	-0.28016	0.23012	0.29542	0.34621	0.56181	0.78523	0.22463	0.11608

\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 & -0.23383 & -0.19762 & -0.079462 & 6.9455e-05 & 0.079074 & 0.21688 & 0.26537 & 0.1056 & 0.15854 \tabularnewline
median & -0.1761 & -0.073764 & 0.2059 & 0.2059 & 0.2179 & 0.3155 & 0.3155 & 0.11012 & 0.012 \tabularnewline
midrange & -0.57527 & -0.38536 & 0.116 & 0.116 & 0.307 & 0.5125 & 0.51324 & 0.18911 & 0.191 \tabularnewline
mode & -1.1842 & -0.6845 & 0.3155 & 0.3155 & 0.3155 & 0.7095 & 1.316 & 0.39833 & 0 \tabularnewline
mode k.dens & -0.69316 & -0.28016 & 0.23012 & 0.29542 & 0.34621 & 0.56181 & 0.78523 & 0.22463 & 0.11608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299040&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]-0.23383[/C][C]-0.19762[/C][C]-0.079462[/C][C]6.9455e-05[/C][C]0.079074[/C][C]0.21688[/C][C]0.26537[/C][C]0.1056[/C][C]0.15854[/C][/ROW]
[ROW][C]median[/C][C]-0.1761[/C][C]-0.073764[/C][C]0.2059[/C][C]0.2059[/C][C]0.2179[/C][C]0.3155[/C][C]0.3155[/C][C]0.11012[/C][C]0.012[/C][/ROW]
[ROW][C]midrange[/C][C]-0.57527[/C][C]-0.38536[/C][C]0.116[/C][C]0.116[/C][C]0.307[/C][C]0.5125[/C][C]0.51324[/C][C]0.18911[/C][C]0.191[/C][/ROW]
[ROW][C]mode[/C][C]-1.1842[/C][C]-0.6845[/C][C]0.3155[/C][C]0.3155[/C][C]0.3155[/C][C]0.7095[/C][C]1.316[/C][C]0.39833[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.69316[/C][C]-0.28016[/C][C]0.23012[/C][C]0.29542[/C][C]0.34621[/C][C]0.56181[/C][C]0.78523[/C][C]0.22463[/C][C]0.11608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299040&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299040&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	-0.23383	-0.19762	-0.079462	6.9455e-05	0.079074	0.21688	0.26537	0.1056	0.15854
median	-0.1761	-0.073764	0.2059	0.2059	0.2179	0.3155	0.3155	0.11012	0.012
midrange	-0.57527	-0.38536	0.116	0.116	0.307	0.5125	0.51324	0.18911	0.191
mode	-1.1842	-0.6845	0.3155	0.3155	0.3155	0.7095	1.316	0.39833	0
mode k.dens	-0.69316	-0.28016	0.23012	0.29542	0.34621	0.56181	0.78523	0.22463	0.11608

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

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