Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Thu, 15 Aug 2013 20:46:13 -0400

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/2013/Aug/15/t1376614122ez414dqvx3oz19m.htm/, Retrieved Mon, 29 Apr 2024 21:48:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=211105, Retrieved Mon, 29 Apr 2024 21:48:31 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

pool 06173 Jul 2013

IsPrivate?

No (this computation is public)

User-defined keywords

FAA PT CA CDPH Forensic alcohol analysis

Estimated Impact

177

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

-     [Maximum-likelihood Fitting - Weibull Distribution] [CDPH pool 10082-W...] [2012-12-07 00:31:39] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bootstrap Plot - Central Tendency] [CDPH pool 10152 b...] [2012-12-11 21:29:01] [74be16979710d4c4e7c6647856088456]
- R PD      [Bootstrap Plot - Central Tendency] [CDPH pool 06173 b...] [2013-08-16 00:46:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	16 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 & 16 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=211105&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]16 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=211105&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=211105&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	16 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	0.12919	0.12944	0.12968	0.1299	0.13015	0.13048	0.13065	0.00032572	0.00046368
median	0.129	0.129	0.13	0.13	0.131	0.131	0.131	0.00060427	0.001
midrange	0.1265	0.127	0.127	0.1275	0.1275	0.1295	0.1295	0.00076838	5e-04
mode	0.128	0.128	0.128	0.1295	0.131	0.132	0.135	0.0016392	0.003
mode k.dens	0.12934	0.12966	0.13006	0.13031	0.13064	0.13102	0.13123	0.00041443	0.00057303

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 0.12919 & 0.12944 & 0.12968 & 0.1299 & 0.13015 & 0.13048 & 0.13065 & 0.00032572 & 0.00046368 \tabularnewline
median & 0.129 & 0.129 & 0.13 & 0.13 & 0.131 & 0.131 & 0.131 & 0.00060427 & 0.001 \tabularnewline
midrange & 0.1265 & 0.127 & 0.127 & 0.1275 & 0.1275 & 0.1295 & 0.1295 & 0.00076838 & 5e-04 \tabularnewline
mode & 0.128 & 0.128 & 0.128 & 0.1295 & 0.131 & 0.132 & 0.135 & 0.0016392 & 0.003 \tabularnewline
mode k.dens & 0.12934 & 0.12966 & 0.13006 & 0.13031 & 0.13064 & 0.13102 & 0.13123 & 0.00041443 & 0.00057303 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=211105&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]0.12919[/C][C]0.12944[/C][C]0.12968[/C][C]0.1299[/C][C]0.13015[/C][C]0.13048[/C][C]0.13065[/C][C]0.00032572[/C][C]0.00046368[/C][/ROW]
[ROW][C]median[/C][C]0.129[/C][C]0.129[/C][C]0.13[/C][C]0.13[/C][C]0.131[/C][C]0.131[/C][C]0.131[/C][C]0.00060427[/C][C]0.001[/C][/ROW]
[ROW][C]midrange[/C][C]0.1265[/C][C]0.127[/C][C]0.127[/C][C]0.1275[/C][C]0.1275[/C][C]0.1295[/C][C]0.1295[/C][C]0.00076838[/C][C]5e-04[/C][/ROW]
[ROW][C]mode[/C][C]0.128[/C][C]0.128[/C][C]0.128[/C][C]0.1295[/C][C]0.131[/C][C]0.132[/C][C]0.135[/C][C]0.0016392[/C][C]0.003[/C][/ROW]
[ROW][C]mode k.dens[/C][C]0.12934[/C][C]0.12966[/C][C]0.13006[/C][C]0.13031[/C][C]0.13064[/C][C]0.13102[/C][C]0.13123[/C][C]0.00041443[/C][C]0.00057303[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=211105&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=211105&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	0.12919	0.12944	0.12968	0.1299	0.13015	0.13048	0.13065	0.00032572	0.00046368
median	0.129	0.129	0.13	0.13	0.131	0.131	0.131	0.00060427	0.001
midrange	0.1265	0.127	0.127	0.1275	0.1275	0.1295	0.1295	0.00076838	5e-04
mode	0.128	0.128	0.128	0.1295	0.131	0.132	0.135	0.0016392	0.003
mode k.dens	0.12934	0.12966	0.13006	0.13031	0.13064	0.13102	0.13123	0.00041443	0.00057303

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 = 0.00244 ; par4 = P1 P5 Q1 Q3 P95 P99 ;

Parameters (R input):

par1 = 500 ; par2 = 5 ; par3 = 0.00244 ; 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)
}
(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