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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

Thu, 06 Sep 2018 14:39:27 +0200

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/2018/Sep/06/t1536237590vc0qmw4else6bq8.htm/, Retrieved Thu, 02 May 2024 12:15:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315474, Retrieved Thu, 02 May 2024 12:15:08 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Bootstrap Plot - Central Tendency] [mean] [2018-09-06 12:39:27] [33956d13de8d8b5d5d1b78ead3554acb] [Current]

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Dataseries X:

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Histogram

Boxplots

1.729
0.1224
0.5238
-0.5018
-1.864
0.07174
-0.3806
-0.2985
1.943
-1.248
1.353
2.844
1.123
-2.325
-2.899
0.2469
-0.7311
1.978
-0.08931
-1.649
1.79
-0.655
-0.9552
-0.5014
-1.475
2.426
-0.3887
-1.42
-0.4357
0.5883
-1.731
0.4162
-0.1356
1.561
-0.6195
0.1301
-0.4857
1.19
0.5049
-0.2667
1.899
0.9836
0.221
-0.1769
-0.1622
0.7891
1.412
0.5182
2.365
-1.244
1.091
0.4222
-0.998
0.7547
-1.444
0.5686
1.354
-1.343
-0.09686
0.2428
0.2557
-0.2432
0.4616
0.06508
2.26
1.805
2.325
0.6828
-0.3356
-0.9979
0.9751
-1.173
-0.0003373
-1.715
-1.349
-1.652
-1.483
0.925
-0.2265
0.7749
-0.3398
0.8931
-0.8646
-1.952
-0.1132
1.025
-1.257
-0.6021
-0.8323
-0.4466
-4.011
0.2885
-1.008
-0.5728
-0.2974
1.216
-0.7275
-0.8439
-1.209
-0.6497
3.2
0.5727
-0.01996
0.4623
-1.287
-1.808
-2.154
0.7078
1.364
0.3147
-4.1
1.27
1.801
-0.4297
0.53
1.188
-0.3982
-0.4219
-2.69
1.014
-0.5421
-0.3687
1.314
-0.4287
-0.6636
1.372
-1.273
0.8102
1.574
-1.025
-0.9989
-0.05362
1.663
-0.178
-1.092
1.968
0.6342
1.388
0.7557
-2.321
1.62
0.4232
-1.705
0.542
-0.593
0.2136
-0.6968
0.07773
-0.7253
2.132
-0.3365
-0.9792
-0.2422
-1.83
-1.148
0.4961
-0.378
0.5646
0.269
0.7527
-2.302
2.826
-0.6076
3.774
1.084
-2.251
0.2907
-1.135
1.416
0.59
1.221
0.0971
-0.906
-0.5977
0.4372
0.7425
1.229
-0.9526
-0.7553

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	15 seconds
R Server	Big 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 time15 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315474&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]

15 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315474&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315474&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	15 seconds
R Server	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.19068	-0.14654	-0.064602	-2.443e-06	0.06304	0.16366	0.21341	0.095454	0.12764
median	-0.3365	-0.2974	-0.16588	-0.08931	0.06508	0.2136	0.25603	0.14589	0.23096
midrange	-0.637	-0.628	-0.45	-0.163	-0.1185	0.4375	0.542	0.26711	0.3315
mode	-2.2705	-1.7055	-0.5988	-2.443e-06	0.45527	1.364	2.426	0.94634	1.0541
mode k.dens	-0.76817	-0.59711	-0.47769	-0.27377	-0.056109	0.47189	0.94811	0.36029	0.42158

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.19068 & -0.14654 & -0.064602 & -2.443e-06 & 0.06304 & 0.16366 & 0.21341 & 0.095454 & 0.12764 \tabularnewline
median & -0.3365 & -0.2974 & -0.16588 & -0.08931 & 0.06508 & 0.2136 & 0.25603 & 0.14589 & 0.23096 \tabularnewline
midrange & -0.637 & -0.628 & -0.45 & -0.163 & -0.1185 & 0.4375 & 0.542 & 0.26711 & 0.3315 \tabularnewline
mode & -2.2705 & -1.7055 & -0.5988 & -2.443e-06 & 0.45527 & 1.364 & 2.426 & 0.94634 & 1.0541 \tabularnewline
mode k.dens & -0.76817 & -0.59711 & -0.47769 & -0.27377 & -0.056109 & 0.47189 & 0.94811 & 0.36029 & 0.42158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315474&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]-0.19068[/C][C]-0.14654[/C][C]-0.064602[/C][C]-2.443e-06[/C][C]0.06304[/C][C]0.16366[/C][C]0.21341[/C][C]0.095454[/C][C]0.12764[/C][/ROW]
[ROW][C]median[/C][C]-0.3365[/C][C]-0.2974[/C][C]-0.16588[/C][C]-0.08931[/C][C]0.06508[/C][C]0.2136[/C][C]0.25603[/C][C]0.14589[/C][C]0.23096[/C][/ROW]
[ROW][C]midrange[/C][C]-0.637[/C][C]-0.628[/C][C]-0.45[/C][C]-0.163[/C][C]-0.1185[/C][C]0.4375[/C][C]0.542[/C][C]0.26711[/C][C]0.3315[/C][/ROW]
[ROW][C]mode[/C][C]-2.2705[/C][C]-1.7055[/C][C]-0.5988[/C][C]-2.443e-06[/C][C]0.45527[/C][C]1.364[/C][C]2.426[/C][C]0.94634[/C][C]1.0541[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.76817[/C][C]-0.59711[/C][C]-0.47769[/C][C]-0.27377[/C][C]-0.056109[/C][C]0.47189[/C][C]0.94811[/C][C]0.36029[/C][C]0.42158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315474&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315474&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	-0.19068	-0.14654	-0.064602	-2.443e-06	0.06304	0.16366	0.21341	0.095454	0.12764
median	-0.3365	-0.2974	-0.16588	-0.08931	0.06508	0.2136	0.25603	0.14589	0.23096
midrange	-0.637	-0.628	-0.45	-0.163	-0.1185	0.4375	0.542	0.26711	0.3315
mode	-2.2705	-1.7055	-0.5988	-2.443e-06	0.45527	1.364	2.426	0.94634	1.0541
mode k.dens	-0.76817	-0.59711	-0.47769	-0.27377	-0.056109	0.47189	0.94811	0.36029	0.42158

Figure 1

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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 = 12 ; par2 = Single ; par3 = additive ; par4 = 12 ;

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)
}
x<-na.omit(x)
(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