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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 computationTue, 16 Dec 2014 11:48:24 +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/16/t1418730538jyj9huu732cbj08.htm/, Retrieved Thu, 16 May 2024 10:20:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269368, Retrieved Thu, 16 May 2024 10:20:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact51

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]
- R  D    [Bootstrap Plot - Central Tendency] [] [2014-12-16 11:48:24] [6260c34aa94cecca073345f42e0d4b5d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.25946
-2.4719
1.0096
1.1682
-4.54351
-4.03988
-1.05197
2.94151
0.839146
-0.818886
-4.47846
-0.689097
-6.24587
-0.212901
-0.834214
-2.20756
-0.336908
-0.669597
-2.02579
-2.47327
-1.24672
-4.55276
0.998092
-0.934858
2.5371
-0.0840563
-2.30537
1.75493
1.79652
-0.751125
0.129225
-3.17135
-0.542086
-4.67574
-2.11485
-3.88572
-1.41426
-1.61835
-0.613347
0.202532
-2.56552
-1.5806
-4.71864
-1.40297
1.60248
-0.545481
0.93428
-1.20464
-0.401875
-4.06093
3.01699
0.925343
-0.534208
-4.64289
-1.05607
0.841487
0.657871
-1.93779
-5.28081
3.04322
-1.15378
1.93421
-2.04005
1.13715
-3.46275
3.1131
-1.04562
-1.57361
-2.10828
0.823078
2.15968
0.982356
-0.726749
0.459096
-1.97448
2.52637
-4.27908
0.556427
0.36687
1.38465
0.139278
-1.89718
-3.72971
-0.818728
2.80049
-1.17996
-4.56877
-1.29112
0.186616
0.0905103
-0.593428
-2.06402
2.69461
2.07121
-5.05805
-0.507041
-2.71798
-4.04158
-1.06689
-3.14575
0.403755
2.57203
-3.65876
-0.333248
-0.982184
-1.76283
-1.72066
2.45102
-2.10343
2.16465
-4.98633
-4.04919
-1.2212
0.705405
0.816111
-3.92003
0.985431
2.02149
2.34984
0.516746
2.70065
-3.2169
2.78958
-1.95866
3.3264
0.177203
3.84087
-3.39463
-0.727631
0.678622
-2.88851
1.52862
1.62631
1.51167
-1.20723
2.00593
0.111595
0.873263
-1.5213
3.33029
-3.89686
0.213474
-1.29139
1.01071
3.83997
-0.113568
0.856642
1.53564
1.33266
1.91082
-0.556325
-0.519977
0.501029
-4.03663
1.23235
-5.10255
1.74465
1.51943
0.0415863
2.66056
0.537691
2.29986
1.81944
-1.38306
-0.500521
4.50221
-0.816057
1.23407
4.084
3.85224
1.06277
1.67408
3.73391
-0.331011
-2.13973
0.422269
3.71577
1.25894
0.217204
0.217204
2.96754
-0.61916
1.87533
1.12591
-3.24706
-0.855504
0.120107
1.56581
0.210322
-1.8176
1.31773
4.24625
-0.198131
1.94459
0.0775331
0.795724
2.01158
-1.31939
3.18276
2.04813
0.97288
-2.86957
-0.406382
3.2089
2.0093
2.04374
2.01552
-0.129218
0.425382
-1.04075
3.64765
-2.49794
2.95705
0.430449
1.25028
1.04567
2.61759
1.14933
-2.54112
-1.83724
-0.469881
-0.992767
2.26391
1.35322
2.55145
1.31937
-0.18007
0.585726
-2.99074
0.530619
1.33458
2.68526
2.57037
1.6605
3.49243
2.6098
2.23239
3.22863
1.17567
1.87583
-1.28738
-0.994567
-5.12761
1.24849
-3.1025
-1.35107
-0.394909
-1.43811
-3.19345
2.66229
3.85224
3.09741
0.482139
0.274379
0.779264
-0.638856
-0.597312
-0.713575
-1.57311
-2.30095
1.59155
1.96161
-1.18665
1.36829
-1.80106
-6.39785
1.29748
-4.71047
1.06277
1.73626
-1.40752
2.66229
1.24021
0.831368
1.24591
1.8308
3.79902
1.4701
4.22159
0.67848
0.958519
1.5856
1.92752
1.67587
2.23905
-0.558394

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'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269368&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269368&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269368&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'Sir Maurice George Kendall' @ kendall.wessa.net






Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.29572-0.19938-0.10199-1.5594e-070.102160.237560.35830.140340.20415
median-0.2129-0.00416030.17720.21720.427920.547790.679240.195480.25071
midrange-1.1569-1.0881-1.0121-0.94782-0.87183-0.3893-0.31270.202150.14031
mode-4.6803-3.1983-0.870811.94861.30343.65113.85221.912.1743
mode k.dens-0.96272-0.763090.903351.15491.29781.55561.66510.634410.39447

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.29572 & -0.19938 & -0.10199 & -1.5594e-07 & 0.10216 & 0.23756 & 0.3583 & 0.14034 & 0.20415 \tabularnewline
median & -0.2129 & -0.0041603 & 0.1772 & 0.2172 & 0.42792 & 0.54779 & 0.67924 & 0.19548 & 0.25071 \tabularnewline
midrange & -1.1569 & -1.0881 & -1.0121 & -0.94782 & -0.87183 & -0.3893 & -0.3127 & 0.20215 & 0.14031 \tabularnewline
mode & -4.6803 & -3.1983 & -0.87081 & 1.9486 & 1.3034 & 3.6511 & 3.8522 & 1.91 & 2.1743 \tabularnewline
mode k.dens & -0.96272 & -0.76309 & 0.90335 & 1.1549 & 1.2978 & 1.5556 & 1.6651 & 0.63441 & 0.39447 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269368&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]-0.29572[/C][C]-0.19938[/C][C]-0.10199[/C][C]-1.5594e-07[/C][C]0.10216[/C][C]0.23756[/C][C]0.3583[/C][C]0.14034[/C][C]0.20415[/C][/ROW]
[ROW][C]median[/C][C]-0.2129[/C][C]-0.0041603[/C][C]0.1772[/C][C]0.2172[/C][C]0.42792[/C][C]0.54779[/C][C]0.67924[/C][C]0.19548[/C][C]0.25071[/C][/ROW]
[ROW][C]midrange[/C][C]-1.1569[/C][C]-1.0881[/C][C]-1.0121[/C][C]-0.94782[/C][C]-0.87183[/C][C]-0.3893[/C][C]-0.3127[/C][C]0.20215[/C][C]0.14031[/C][/ROW]
[ROW][C]mode[/C][C]-4.6803[/C][C]-3.1983[/C][C]-0.87081[/C][C]1.9486[/C][C]1.3034[/C][C]3.6511[/C][C]3.8522[/C][C]1.91[/C][C]2.1743[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.96272[/C][C]-0.76309[/C][C]0.90335[/C][C]1.1549[/C][C]1.2978[/C][C]1.5556[/C][C]1.6651[/C][C]0.63441[/C][C]0.39447[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269368&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269368&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-0.29572-0.19938-0.10199-1.5594e-070.102160.237560.35830.140340.20415
median-0.2129-0.00416030.17720.21720.427920.547790.679240.195480.25071
midrange-1.1569-1.0881-1.0121-0.94782-0.87183-0.3893-0.31270.202150.14031
mode-4.6803-3.1983-0.870811.94861.30343.65113.85221.912.1743
mode k.dens-0.96272-0.763090.903351.15491.29781.55561.66510.634410.39447


Figures (Output of Computation)

Figure 1
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Figure 2
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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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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')