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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationSat, 13 Dec 2014 20:27:13 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/13/t14185024419b9bplhwi6uk34h.htm/, Retrieved Thu, 16 May 2024 06:15:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267287, Retrieved Thu, 16 May 2024 06:15:30 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
-    D    [Bootstrap Plot - Central Tendency] [] [2014-12-13 20:27:13] [a97fb05c06a04cb9398859e294d4eb9c] [Current]
- RMPD      [Testing Mean with unknown Variance - Critical Value] [] [2014-12-14 11:33:03] [8f0f7d8870e334acea674e48ede2c797]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-4,27791
34,7317
-8,64075
-0,328568
33,5756
-26,6184
-26,4893
-0,441997
16,539
-38,8372
63,3774
38,6653
-19,727
4,12145
-18,6939
11,4978
-12,8698
-20,219
20,4518
-18,5627
0,591859
24,5708
-14,2738
14,1122
27,5253
-6,01334
-23,3625
-29,2387
-0,95199
40,65
-7,42194
36,7659
46,5858
17,1041
17,1339
39,1
37,3241
15,8342
20,6053
-1,34888
-18,3336
5,88315
3,24557
25,6868
2,20964
2,66188
9,37267
6,82545
-26,079
5,8751
27,6959
17,8983
-19,978
-1,94792
-16,3411
1,48271
-13,1618
-1,41653
4,92832
-21,7146
11,9629
0,710745
38,4861
-50,6929
10,4626
15,283
-2,18355
-19,5889
-8,07609
11,5571
31,9772
5,16677
47,0483
-27,8685
30,1541
2,55884
68,9409
6,38428
20,7367
14,1086
21,9279
-28,4885
-22,1138
5,28836
-7,07211
10,4943
-4,05707
-15,9959
-8,49194
-18,7145
7,18354
-13,2506
27,211
-31,8653
-17,7936
-27,5299
18,209
-11,9025
-42,9355
-9,78619
0,183474
-32,8626
4,97127
-39,247
-26,6281
-24,3556
-6,59482
-29,472
-12,2773
-34,5339
2,24069
35,4566
-4,36551
7,57695
-1,74565
-12,5965
-1,74151
-26,1197
-11,7943
9,39099
-44,6021
-2,57324
-11,6591
1,26088
0,457117
-17,177
0,917461
-5,84016
24,1891
-30,9971
28,3648
-17,5735
-15,4827
-0,0896821
4,27147
18,0731
-41,8084

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267287&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267287&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267287&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-4.192-3.0082-1.23326.4891e-071.29852.89074.25491.82952.5317
median-4.2953-2.203-1.4165-0.328570.591862.24074.28041.67862.0084
midrange-2.0832-1.82239.1249.12412.16913.00313.5664.11293.0454
mode-42.952-30.997-12.3576.4891e-0711.15533.73539.11518.37323.512
mode k.dens-19.849-7.8187-1.2939-0.246351.55773.92726.17654.26262.8517

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -4.192 & -3.0082 & -1.2332 & 6.4891e-07 & 1.2985 & 2.8907 & 4.2549 & 1.8295 & 2.5317 \tabularnewline
median & -4.2953 & -2.203 & -1.4165 & -0.32857 & 0.59186 & 2.2407 & 4.2804 & 1.6786 & 2.0084 \tabularnewline
midrange & -2.0832 & -1.8223 & 9.124 & 9.124 & 12.169 & 13.003 & 13.566 & 4.1129 & 3.0454 \tabularnewline
mode & -42.952 & -30.997 & -12.357 & 6.4891e-07 & 11.155 & 33.735 & 39.115 & 18.373 & 23.512 \tabularnewline
mode k.dens & -19.849 & -7.8187 & -1.2939 & -0.24635 & 1.5577 & 3.9272 & 6.1765 & 4.2626 & 2.8517 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267287&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]-4.192[/C][C]-3.0082[/C][C]-1.2332[/C][C]6.4891e-07[/C][C]1.2985[/C][C]2.8907[/C][C]4.2549[/C][C]1.8295[/C][C]2.5317[/C][/ROW]
[ROW][C]median[/C][C]-4.2953[/C][C]-2.203[/C][C]-1.4165[/C][C]-0.32857[/C][C]0.59186[/C][C]2.2407[/C][C]4.2804[/C][C]1.6786[/C][C]2.0084[/C][/ROW]
[ROW][C]midrange[/C][C]-2.0832[/C][C]-1.8223[/C][C]9.124[/C][C]9.124[/C][C]12.169[/C][C]13.003[/C][C]13.566[/C][C]4.1129[/C][C]3.0454[/C][/ROW]
[ROW][C]mode[/C][C]-42.952[/C][C]-30.997[/C][C]-12.357[/C][C]6.4891e-07[/C][C]11.155[/C][C]33.735[/C][C]39.115[/C][C]18.373[/C][C]23.512[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-19.849[/C][C]-7.8187[/C][C]-1.2939[/C][C]-0.24635[/C][C]1.5577[/C][C]3.9272[/C][C]6.1765[/C][C]4.2626[/C][C]2.8517[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267287&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267287&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 Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-4.192-3.0082-1.23326.4891e-071.29852.89074.25491.82952.5317
median-4.2953-2.203-1.4165-0.328570.591862.24074.28041.67862.0084
midrange-2.0832-1.82239.1249.12412.16913.00313.5664.11293.0454
mode-42.952-30.997-12.3576.4891e-0711.15533.73539.11518.37323.512
mode k.dens-19.849-7.8187-1.2939-0.246351.55773.92726.17654.26262.8517


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 7
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Figure 9
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Figure 10
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Figure 11
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Input Parameters & R Code

Parameters (Session):
par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 200 ; par2 = 5 ; par3 = 0 ; 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')