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Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationMon, 27 Aug 2012 20:27:35 -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/2012/Aug/27/t1346113897fij28q2c41nhk2p.htm/, Retrieved Thu, 02 May 2024 20:57:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=169497, Retrieved Thu, 02 May 2024 20:57:53 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kernel Density Estimation] [CDPH 06112 Bootst...] [2012-08-22 18:17:57] [74be16979710d4c4e7c6647856088456]
- RMPD    [Bootstrap Plot - Central Tendency] [CDPH pool 06182 b...] [2012-08-28 00:27:35] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
0.192
0.194
0.197
0.198
0.198
0.199
0.199
0.200
0.200
0.201
0.201
0.201
0.202
0.202
0.202
0.202
0.203
0.203
0.203
0.204
0.204
0.204
0.205
0.205
0.205
0.205
0.205
0.205
0.206
0.206
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.208
0.208
0.208
0.208
0.208
0.208
0.208
0.208
0.208
0.208
0.208
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.209
0.210
0.210
0.210
0.210
0.210
0.210
0.210
0.210
0.210
0.210
0.210
0.210
0.211
0.211
0.211
0.211
0.211
0.211
0.211
0.211
0.211
0.211
0.211
0.211
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.212
0.213
0.213
0.213
0.213
0.213
0.213
0.213
0.213
0.213
0.213
0.213
0.214
0.214
0.214
0.214
0.215
0.215
0.216
0.216
0.216
0.216
0.217
0.217
0.217
0.217
0.218
0.218
0.218
0.219
0.220
0.220
0.220
0.221




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169497&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169497&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169497&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 time13 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean0.209050.209370.20970.000475220.00065152
median0.209880.210.210.000545640.000125
midrange0.20650.20650.20750.000996280.001
mode0.2090.2090.2120.00141990.003
mode k.dens0.209930.210170.210490.000419970.00056626

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 0.20905 & 0.20937 & 0.2097 & 0.00047522 & 0.00065152 \tabularnewline
median & 0.20988 & 0.21 & 0.21 & 0.00054564 & 0.000125 \tabularnewline
midrange & 0.2065 & 0.2065 & 0.2075 & 0.00099628 & 0.001 \tabularnewline
mode & 0.209 & 0.209 & 0.212 & 0.0014199 & 0.003 \tabularnewline
mode k.dens & 0.20993 & 0.21017 & 0.21049 & 0.00041997 & 0.00056626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169497&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]0.20905[/C][C]0.20937[/C][C]0.2097[/C][C]0.00047522[/C][C]0.00065152[/C][/ROW]
[ROW][C]median[/C][C]0.20988[/C][C]0.21[/C][C]0.21[/C][C]0.00054564[/C][C]0.000125[/C][/ROW]
[ROW][C]midrange[/C][C]0.2065[/C][C]0.2065[/C][C]0.2075[/C][C]0.00099628[/C][C]0.001[/C][/ROW]
[ROW][C]mode[/C][C]0.209[/C][C]0.209[/C][C]0.212[/C][C]0.0014199[/C][C]0.003[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.20993[/C][C]0.21017[/C][C]0.21049[/C][C]0.00041997[/C][C]0.00056626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169497&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169497&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
statisticQ1EstimateQ3S.D.IQR
mean0.209050.209370.20970.000475220.00065152
median0.209880.210.210.000545640.000125
midrange0.20650.20650.20750.000996280.001
mode0.2090.2090.2120.00141990.003
mode k.dens0.209930.210170.210490.000419970.00056626



Parameters (Session):
par1 = 500 ; par2 = 5 ; par3 = 0.00393 ;
Parameters (R input):
par1 = 500 ; par2 = 5 ; par3 = 0.00393 ;
R code (references can be found in the software module):
par3 <- '0.00393'
par2 <- '5'
par1 <- '500'
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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[1],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element( a,signif( 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$t[,2],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[2],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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$t[,3],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[3],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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$t[,4],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[4],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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$t[,5],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[5],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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')