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Author

*The author of this computation has been verified*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Sun, 14 Dec 2014 13:00:35 +0000

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/2014/Dec/14/t1418562088eb0xss8gu4oogof.htm/, Retrieved Thu, 16 May 2024 06:29:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267532, Retrieved Thu, 16 May 2024 06:29:19 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

104

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] [] [2014-11-12 15:33:27] [d253a55552bf9917a397def3be261e30]
-    D    [Bootstrap Plot - Central Tendency] [] [2014-12-13 12:30:20] [d253a55552bf9917a397def3be261e30]
-    D        [Bootstrap Plot - Central Tendency] [] [2014-12-14 13:00:35] [940a3d9bc049bdd1effc6e8b1116301d] [Current]

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Dataseries X:

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Histogram

Boxplots

0.930232
-3.40521
2.02182
1.36969
-3.79215
-3.37923
-1.23382
3.31456
3.26075
-0.165989
-4.14243
-0.0335755
-5.40131
1.74607
-0.986595
-0.309542
1.91394
-0.327325
-0.89044
-2.14762
0.791583
-3.57304
2.88251
-2.73464
1.69646
0.902231
-3.62451
2.53148
3.31196
-0.604015
0.720093
-0.824376
0.429387
-4.31361
-0.823784
-2.52619
1.55265
0.929294
-0.0289107
1.74827
-2.28123
-0.525751
-3.45105
-0.167651
1.71842
-0.437273
3.83774
-1.61331
2.23669
-2.01368
4.19201
2.83074
0.877681
-1.05345
0.0338844
0.299579
2.82962
0.134707
-5.45701
4.75094
0.386812
2.65834
-2.6914
1.48738
-0.597236
2.58097
-2.44384
0.583801
-1.73785
-0.934774
2.46862
1.39291
-0.717696
1.43155
-0.856457
3.77396
-4.06016
1.74878
0.903159
1.95283
0.784645
0.322499
-1.6619
0.118537
3.16621
0.390905
-4.71432
-0.766198
1.02652
1.98851
2.11668
-1.09769
2.04049
3.91253
-4.32922
-1.18636
-1.20051
-2.32142
-1.32521
-3.74392
2.09449
3.67999
-2.24686
1.8847
-0.00138148
-0.561207
-1.10791
4.06735
-1.52051
3.86172
-4.82336
-1.81619
-0.673258
0.986615
1.79907
-6.14221
0.571834
3.01493
2.35191
-1.29061
1.30383
-3.13186
1.86218
-2.27961
2.06061
1.25248
4.74694
-3.25767
-0.605609
1.25981
-2.05434
1.74765
1.25994
1.73564
-1.85395
1.10537
0.938882
0.27089
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3.25287
-1.69574
1.49124
0.0988109
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3.28949
0.804418
2.87297
0.859365
0.272281
1.31216
-0.22338
-2.44715
0.153112
-1.1713
1.80552
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1.33443
2.2369
-0.781063
1.36043
-0.629536
1.79828
1.09999
-2.85353
-2.49528
2.25146
0.520604
0.298607
6.90909
1.28231
-0.262004
1.46464
1.88438
-1.00143
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0.777597
2.59824
-1.46655
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2.72724
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2.22985
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-3.65571
-2.27573
-2.09533
2.38629
-0.0258131
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0.438568
2.79009
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-0.150236
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1.49501
-2.89766
3.28964
-0.539803
0.420198
-2.1092
-0.188277
1.23821
0.941709
0.680047
0.382231
-1.0876
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1.36667
-1.43758
0.420898
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2.42003
1.67243
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0.797683
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1.34484
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2.23363
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2.60945
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2.3026
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0.131149
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0.749191
1.95535
4.11493
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0.561115
0.950764
0.930866
-0.344856
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1.19406
-1.63891
0.204536
-4.23402
-5.46786
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1.18437
1.22329
-0.0406617
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1.62326
2.03548
-1.03108
0.847509
-0.66061
0.603647
0.92984
0.508684
0.00407915
0.725297
-2.63265

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267532&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]7 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=267532&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267532&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	7 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.35447	-0.20622	-0.090548	-6.3042e-08	0.091232	0.22492	0.31912	0.13609	0.18178
median	-0.34578	-0.1981	-0.031826	0.10867	0.27106	0.42057	0.52075	0.20507	0.30289
midrange	-1.1639	-0.8855	-0.8835	0.19557	0.19557	0.30815	0.5169	0.55301	1.0791
mode	-4.1433	-2.337	-0.81915	-6.3042e-08	1.0254	2.8734	4.7686	1.7371	1.8445
mode k.dens	-0.94437	-0.81835	-0.43525	0.43148	0.94099	1.4295	1.6169	0.77442	1.3762

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.35447 & -0.20622 & -0.090548 & -6.3042e-08 & 0.091232 & 0.22492 & 0.31912 & 0.13609 & 0.18178 \tabularnewline
median & -0.34578 & -0.1981 & -0.031826 & 0.10867 & 0.27106 & 0.42057 & 0.52075 & 0.20507 & 0.30289 \tabularnewline
midrange & -1.1639 & -0.8855 & -0.8835 & 0.19557 & 0.19557 & 0.30815 & 0.5169 & 0.55301 & 1.0791 \tabularnewline
mode & -4.1433 & -2.337 & -0.81915 & -6.3042e-08 & 1.0254 & 2.8734 & 4.7686 & 1.7371 & 1.8445 \tabularnewline
mode k.dens & -0.94437 & -0.81835 & -0.43525 & 0.43148 & 0.94099 & 1.4295 & 1.6169 & 0.77442 & 1.3762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267532&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]-0.35447[/C][C]-0.20622[/C][C]-0.090548[/C][C]-6.3042e-08[/C][C]0.091232[/C][C]0.22492[/C][C]0.31912[/C][C]0.13609[/C][C]0.18178[/C][/ROW]
[ROW][C]median[/C][C]-0.34578[/C][C]-0.1981[/C][C]-0.031826[/C][C]0.10867[/C][C]0.27106[/C][C]0.42057[/C][C]0.52075[/C][C]0.20507[/C][C]0.30289[/C][/ROW]
[ROW][C]midrange[/C][C]-1.1639[/C][C]-0.8855[/C][C]-0.8835[/C][C]0.19557[/C][C]0.19557[/C][C]0.30815[/C][C]0.5169[/C][C]0.55301[/C][C]1.0791[/C][/ROW]
[ROW][C]mode[/C][C]-4.1433[/C][C]-2.337[/C][C]-0.81915[/C][C]-6.3042e-08[/C][C]1.0254[/C][C]2.8734[/C][C]4.7686[/C][C]1.7371[/C][C]1.8445[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.94437[/C][C]-0.81835[/C][C]-0.43525[/C][C]0.43148[/C][C]0.94099[/C][C]1.4295[/C][C]1.6169[/C][C]0.77442[/C][C]1.3762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267532&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267532&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	-0.35447	-0.20622	-0.090548	-6.3042e-08	0.091232	0.22492	0.31912	0.13609	0.18178
median	-0.34578	-0.1981	-0.031826	0.10867	0.27106	0.42057	0.52075	0.20507	0.30289
midrange	-1.1639	-0.8855	-0.8835	0.19557	0.19557	0.30815	0.5169	0.55301	1.0791
mode	-4.1433	-2.337	-0.81915	-6.3042e-08	1.0254	2.8734	4.7686	1.7371	1.8445
mode k.dens	-0.94437	-0.81835	-0.43525	0.43148	0.94099	1.4295	1.6169	0.77442	1.3762

Figure 1

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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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Figure 10

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Figure 11

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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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code