FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationMon, 02 May 2016 20:55:36 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/May/02/t1462218983ki90q1mozig9a9o.htm/, Retrieved Sun, 05 May 2024 17:13:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295162, Retrieved Sun, 05 May 2024 17:13:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Blocked Bootstrap Plot - Central Tendency] [bootstrap plot pr...] [2016-05-02 19:55:36] [b54f462b245e496e54620f8b97639ccc] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
92.49
92.46
92.55
92.24
92.41
92.83
92.85
93.04
93.04
92.83
92.96
92.83
93.01
93.21
93.58
94.07
94.57
95.03
95.21
95.89
96.43
96.35
96.71
96.32
97.23
97.88
98.2
98.56
99.31
99.69
99.77
101.06
101.77
101.91
102.52
102.09
102.22
102.74
103.56
104.4
104.76
104.86
104.84
104.96
104.83
104.58
104.8
104.17
104.63
105.32
106.16
107.22
107.51
107.87
107.79
108.04
107.74
107.71
111.19
110.82

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295162&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295162&T=0

As an alternative you can also use a QR Code:  
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'Herman Ole Andreas Wold' @ wold.wessa.net






Estimation Results of Blocked Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean95.25296.55298.43199.927101.48103.37104.822.11043.0491
median93.03293.5896.4399.73102.74104.82106.163.75736.31
midrange97.42298.78100.64101.72101.72102.01103.611.13721.0738
mode92.8392.8392.8392.83102.38106.77108.445.20079.5519
mode k.dens92.64892.7592.97993.136104.74107.68107.745.637711.761

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 95.252 & 96.552 & 98.431 & 99.927 & 101.48 & 103.37 & 104.82 & 2.1104 & 3.0491 \tabularnewline
median & 93.032 & 93.58 & 96.43 & 99.73 & 102.74 & 104.82 & 106.16 & 3.7573 & 6.31 \tabularnewline
midrange & 97.422 & 98.78 & 100.64 & 101.72 & 101.72 & 102.01 & 103.61 & 1.1372 & 1.0738 \tabularnewline
mode & 92.83 & 92.83 & 92.83 & 92.83 & 102.38 & 106.77 & 108.44 & 5.2007 & 9.5519 \tabularnewline
mode k.dens & 92.648 & 92.75 & 92.979 & 93.136 & 104.74 & 107.68 & 107.74 & 5.6377 & 11.761 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295162&T=1

[TABLE]
[ROW][C]Estimation Results of Blocked 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]95.252[/C][C]96.552[/C][C]98.431[/C][C]99.927[/C][C]101.48[/C][C]103.37[/C][C]104.82[/C][C]2.1104[/C][C]3.0491[/C][/ROW]
[ROW][C]median[/C][C]93.032[/C][C]93.58[/C][C]96.43[/C][C]99.73[/C][C]102.74[/C][C]104.82[/C][C]106.16[/C][C]3.7573[/C][C]6.31[/C][/ROW]
[ROW][C]midrange[/C][C]97.422[/C][C]98.78[/C][C]100.64[/C][C]101.72[/C][C]101.72[/C][C]102.01[/C][C]103.61[/C][C]1.1372[/C][C]1.0738[/C][/ROW]
[ROW][C]mode[/C][C]92.83[/C][C]92.83[/C][C]92.83[/C][C]92.83[/C][C]102.38[/C][C]106.77[/C][C]108.44[/C][C]5.2007[/C][C]9.5519[/C][/ROW]
[ROW][C]mode k.dens[/C][C]92.648[/C][C]92.75[/C][C]92.979[/C][C]93.136[/C][C]104.74[/C][C]107.68[/C][C]107.74[/C][C]5.6377[/C][C]11.761[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295162&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimation Results of Blocked Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean95.25296.55298.43199.927101.48103.37104.822.11043.0491
median93.03293.5896.4399.73102.74104.82106.163.75736.31
midrange97.42298.78100.64101.72101.72102.01103.611.13721.0738
mode92.8392.8392.8392.83102.38106.77108.445.20079.5519
mode k.dens92.64892.7592.97993.136104.74107.68107.745.637711.761


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 750 ; par2 = 12 ; par3 = 5 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 750 ; par2 = 12 ; par3 = 5 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
R code (references can be found in the software module):
par4 <- 'P1 P5 Q1 Q3 P95 P99'
par3 <- '5'
par2 <- '12'
par1 <- '200'
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
if (par2 < 3) par2 = 3
if (par2 > length(x)) par2 = length(x)
library(modeest)
library(lattice)
library(boot)
boot.stat <- function(s)
{
s.mean <- mean(s)
s.median <- median(s)
s.midrange <- (max(s) + min(s)) / 2
s.mode <- mlv(s,method='mfv')$M
s.kernelmode <- mlv(s, method='kernel')$M
c(s.mean, s.median, s.midrange, s.mode, s.kernelmode)
}
(r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))
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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot7a.png')
plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')
grid()
dev.off()
bitmap(file='plot8a.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()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]) )
colnames(z) <- list('mean','median','midrange','mode','mode.k.dens')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked Bootstrap',10,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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$t[,1],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[1],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element( a,signif( sqrt(var(r$t[,1])),par3 ) )
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$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$t[,2],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[2],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,2])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$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$t[,3],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[3],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,3])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode',header=TRUE)
q1 <- quantile(r$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$t[,4],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[4],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,4])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode k.dens',header=TRUE)
q1 <- quantile(r$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$t[,5],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[5],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,5])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')