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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationWed, 29 Nov 2017 11:16:17 +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/2017/Nov/29/t15119506716vhi3krgtkq2bd5.htm/, Retrieved Sat, 18 May 2024 15:17:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308284, Retrieved Sat, 18 May 2024 15:17:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSection C - Manufacturing
Estimated Impact112

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] [Blocked Bootstrap...] [2017-11-29 10:16:17] [e32c8f3a6c40fa6b5d041988204898ea] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
58.4
64.8
73.8
65
73
71.1
58.2
64
75
74.9
75
68.3
72.5
72.4
79.6
70.7
76.4
79.7
64.2
67.9
74.1
78.5
73.4
65.4
69.9
69.6
76.8
75.6
74
76
68.1
65.5
76.9
81.7
73.6
68.7
73.3
71.5
78.3
76.5
71.8
77.6
70
64
81.3
82.5
73.1
78.1
70.7
74.9
88
81.3
75.7
89.8
74.6
74.9
90
88.1
84.9
87.7
80.5
79
89.9
86.3
81.1
92.4
71.8
76.1
92.5
87
89.5
88.7
83.8
84.9
99
84.6
92.7
97.6
78
81.9
96.5
99.9
96.2
90.5
91.4
89.7
102.7
91.5
96.2
104.5
90.3
90.3
100.4
111.3
101.3
94.4
100.4
102
104.3
108.8
101.3
108.9
98.5
88.8
111.8
109.6
92.5
94.5
80.8
83.7
94.2
86.2
89
94.7
81.9
80.2
96.5
95.6
91.9
89.9
86.3
94
108
96.3
94.6
111.7
92
91.9
109.2
106.8
105.8
103.6
97.6
102.8
124.8
103.9
112.2
108.5
92.4
101.1
114.9
106.4
104
101.6
99.4
102.3
121.3
99.3
102.9
111.4
98.5
98.5
108.5
112.1
105.3
95.2
98.2
96.6
109.6
108
106.7
111.5
104.5
94.3
109.6
116.4
106.5
100.5
101.7
104.1
112.3
111.2
108.2
115.1
102.3
93.6
120.6
118.4
106.6
105.3
101.5
100.1
119.5
111.2
103.7
117.8
101.7
97.4
120
117
110.6
105.3
100.9
108.1
119.3
113
108.6
123.3
101.4
103.5
119.4
113.1
112
115.8

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R ServerBig 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308284&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]11 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308284&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R ServerBig Analytics Cloud Computing Center






Estimation Results of Blocked Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean84.67887.6990.80992.80895.28598.256100.533.33144.4763
median81.09786.6391.4594.3597.9101.3102.814.77886.45
midrange89.489.7590.8591.591.694.495.11.42060.75
mode71.58174.986.22997.075102.3109.6111.211.6716.071
mode k.dens73.85574.42392.226101.83103.19106.39108.1210.65610.961

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & 84.678 & 87.69 & 90.809 & 92.808 & 95.285 & 98.256 & 100.53 & 3.3314 & 4.4763 \tabularnewline
median & 81.097 & 86.63 & 91.45 & 94.35 & 97.9 & 101.3 & 102.81 & 4.7788 & 6.45 \tabularnewline
midrange & 89.4 & 89.75 & 90.85 & 91.5 & 91.6 & 94.4 & 95.1 & 1.4206 & 0.75 \tabularnewline
mode & 71.581 & 74.9 & 86.229 & 97.075 & 102.3 & 109.6 & 111.2 & 11.67 & 16.071 \tabularnewline
mode k.dens & 73.855 & 74.423 & 92.226 & 101.83 & 103.19 & 106.39 & 108.12 & 10.656 & 10.961 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308284&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]84.678[/C][C]87.69[/C][C]90.809[/C][C]92.808[/C][C]95.285[/C][C]98.256[/C][C]100.53[/C][C]3.3314[/C][C]4.4763[/C][/ROW]
[ROW][C]median[/C][C]81.097[/C][C]86.63[/C][C]91.45[/C][C]94.35[/C][C]97.9[/C][C]101.3[/C][C]102.81[/C][C]4.7788[/C][C]6.45[/C][/ROW]
[ROW][C]midrange[/C][C]89.4[/C][C]89.75[/C][C]90.85[/C][C]91.5[/C][C]91.6[/C][C]94.4[/C][C]95.1[/C][C]1.4206[/C][C]0.75[/C][/ROW]
[ROW][C]mode[/C][C]71.581[/C][C]74.9[/C][C]86.229[/C][C]97.075[/C][C]102.3[/C][C]109.6[/C][C]111.2[/C][C]11.67[/C][C]16.071[/C][/ROW]
[ROW][C]mode k.dens[/C][C]73.855[/C][C]74.423[/C][C]92.226[/C][C]101.83[/C][C]103.19[/C][C]106.39[/C][C]108.12[/C][C]10.656[/C][C]10.961[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308284&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
mean84.67887.6990.80992.80895.28598.256100.533.33144.4763
median81.09786.6391.4594.3597.9101.3102.814.77886.45
midrange89.489.7590.8591.591.694.495.11.42060.75
mode71.58174.986.22997.075102.3109.6111.211.6716.071
mode k.dens73.85574.42392.226101.83103.19106.39108.1210.65610.961


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Input Parameters & R Code

Parameters (Session):
par1 = grey ; par2 = no ;
Parameters (R input):
par1 = 500 ; par2 = 12 ; par3 = 5 ; 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)
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')