Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationSat, 25 May 2013 12:47:16 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/May/25/t1369500506h74a58hmmw8to21.htm/, Retrieved Thu, 02 May 2024 17:59:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=210530, Retrieved Thu, 02 May 2024 17:59:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [] [2013-05-25 16:47:16] [f2eb9e6f8a572d38d7977956fe5c285e] [Current]
Feedback Forum

Post a new message
Dataseries X:
67.66
68
68.02
68.11
68.41
68.4
68.4
68.55
68.54
68.99
68.97
68.98
68.98
68.94
69.21
69.21
69.67
69.66
69.66
69.66
69.77
70.32
70.34
70.38
70.38
70.29
70.42
70.29
70.59
70.64
70.64
70.68
70.78
70.9
71.04
71.15
71.15
71.15
71.07
71.17
71.24
71.23
71.23
71.23
71.24
71.28
71.52
71.52
71.52
71.6
71.61
71.78
71.66
71.86
71.86
71.82
71.8
72.22
72.51
72.56
72.56
72.78
72.88
73.05
73.02
73.08
73.08
73.24
73.82
74
74.37
74.38




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210530&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210530&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210530&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean70.42870.53470.73370.87171.00371.2271.3270.202120.26927
median70.470.63470.971.1171.1771.23571.240.224190.27
midrange70.53970.8371.01571.0271.1971.271.3850.138360.175
mode68.468.9870.14670.8971.5272.88773.241.1031.3744
mode k.dens68.56570.52371.08571.18371.31671.47371.6090.500930.23073

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 70.428 & 70.534 & 70.733 & 70.871 & 71.003 & 71.22 & 71.327 & 0.20212 & 0.26927 \tabularnewline
median & 70.4 & 70.634 & 70.9 & 71.11 & 71.17 & 71.235 & 71.24 & 0.22419 & 0.27 \tabularnewline
midrange & 70.539 & 70.83 & 71.015 & 71.02 & 71.19 & 71.2 & 71.385 & 0.13836 & 0.175 \tabularnewline
mode & 68.4 & 68.98 & 70.146 & 70.89 & 71.52 & 72.887 & 73.24 & 1.103 & 1.3744 \tabularnewline
mode k.dens & 68.565 & 70.523 & 71.085 & 71.183 & 71.316 & 71.473 & 71.609 & 0.50093 & 0.23073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210530&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]70.428[/C][C]70.534[/C][C]70.733[/C][C]70.871[/C][C]71.003[/C][C]71.22[/C][C]71.327[/C][C]0.20212[/C][C]0.26927[/C][/ROW]
[ROW][C]median[/C][C]70.4[/C][C]70.634[/C][C]70.9[/C][C]71.11[/C][C]71.17[/C][C]71.235[/C][C]71.24[/C][C]0.22419[/C][C]0.27[/C][/ROW]
[ROW][C]midrange[/C][C]70.539[/C][C]70.83[/C][C]71.015[/C][C]71.02[/C][C]71.19[/C][C]71.2[/C][C]71.385[/C][C]0.13836[/C][C]0.175[/C][/ROW]
[ROW][C]mode[/C][C]68.4[/C][C]68.98[/C][C]70.146[/C][C]70.89[/C][C]71.52[/C][C]72.887[/C][C]73.24[/C][C]1.103[/C][C]1.3744[/C][/ROW]
[ROW][C]mode k.dens[/C][C]68.565[/C][C]70.523[/C][C]71.085[/C][C]71.183[/C][C]71.316[/C][C]71.473[/C][C]71.609[/C][C]0.50093[/C][C]0.23073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210530&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210530&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
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean70.42870.53470.73370.87171.00371.2271.3270.202120.26927
median70.470.63470.971.1171.1771.23571.240.224190.27
midrange70.53970.8371.01571.0271.1971.271.3850.138360.175
mode68.468.9870.14670.8971.5272.88773.241.1031.3744
mode k.dens68.56570.52371.08571.18371.31671.47371.6090.500930.23073



Parameters (Session):
par1 = 200 ; par2 = 5 ; par3 = 0 ;
Parameters (R input):
par1 = 200 ; par2 = 5 ; par3 = 0 ;
R code (references can be found in the software module):
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)
}
(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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',10,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'P1',header=TRUE)
a<-table.element(a,'P5',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'P95',header=TRUE)
a<-table.element(a,'P99',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
p01 <- quantile(r$t[,1],0.01)[[1]]
p05 <- quantile(r$t[,1],0.05)[[1]]
p95 <- quantile(r$t[,1],0.95)[[1]]
p99 <- quantile(r$t[,1],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,2],0.05)[[1]]
p95 <- quantile(r$t[,2],0.95)[[1]]
p99 <- quantile(r$t[,2],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,3],0.05)[[1]]
p95 <- quantile(r$t[,3],0.95)[[1]]
p99 <- quantile(r$t[,3],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,4],0.05)[[1]]
p95 <- quantile(r$t[,4],0.95)[[1]]
p99 <- quantile(r$t[,4],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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],0.01)[[1]]
p05 <- quantile(r$t[,5],0.05)[[1]]
p95 <- quantile(r$t[,5],0.95)[[1]]
p99 <- quantile(r$t[,5],0.99)[[1]]
a<-table.element(a,signif(p01,par2))
a<-table.element(a,signif(p05,par2))
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$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')