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Author's title

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

Mon, 15 Dec 2014 10:33:50 +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/15/t14186396598pvzr0gxgkdd1cg.htm/, Retrieved Thu, 16 May 2024 15:56:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268048, Retrieved Thu, 16 May 2024 15:56:12 +0000

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User-defined keywords

Bootstrap Plot Paper score positieve waarden

Estimated Impact

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

-       [Bootstrap Plot - Central Tendency] [Paper data] [2014-12-15 10:33:50] [99d5c1073827aabbadf7ab1e7da1d584] [Current]

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Dataseries X:

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Histogram

Boxplots

0.000835892
0.529895
-0.0939039
-0.0908877
-0.275613
-0.0036702
-0.107372
-0.222908
-0.404341
0.000197477
0.202906
-0.145611
-0.0568477
-0.0408403
-0.223828
-0.106075
0.101821
-0.0466793
0.0968636
0.0919463
0.0760569
0.334341
-0.187227
0.239296
-0.0570786
0.00674334
0.154932
-0.12846
-0.0266104
0.0825937
-0.0585921
0.237022
-0.0499755
-0.124717
0.118617
-0.10272
-0.220234
-0.106474
0.0971047
0.0738534
0.20582
-0.0457694
0.0588074
-0.079498
-0.0195393
-0.109579
0.372696
-0.0763741
-0.13612
0.063656
0.385364
0.303056
-0.214632
0.107402
0.0560124
-0.197892
0.451636
-0.0340794
0.0928652
-0.11073
0.516083
-0.140772
-0.179701
-0.488478
-0.488478
-0.139551
-0.141148
-0.0826939
-0.166582
-0.00201794
0.0961075
0.190375
-0.133774
0.0243258
0.191636
-0.138128
-0.0548539
0.190225
0.246677
-0.0548526
-0.042776
0.138234
-0.0443967
0.503565
-0.0632757
-0.0542748
0.0308984
-0.0881737
0.228238
0.02696
-0.0246886
-0.0376905
-0.105205
0.241035
0.0894601
-0.0506431
-0.0399592
-0.47944
-0.500231
0.427494
-0.144458
-0.234123
0.0475154
0.164683
-0.13349
-0.157491
0.228533
-0.107823
-0.0707374
0.20582
0.220307
0.187066
0.0886605
0.279072
-0.0444225
-0.0441662
-0.422621
0.575235
-0.0355119
-0.0836333
0.0985849
0.353771
0.353074
-0.214877
-0.0615814
-0.101022
0.10413
0.137815
-0.00242473
-0.110393
-0.220774
0.226726
-0.0958664
0.25446
0.201339
0.0560124
0.0698585
-0.206788
-0.0965751
-0.140242
0.36058
-0.128694
-0.0460112
-0.046854
-0.247344
0.0597078
0.157703
-0.266819
-0.137662
-0.0624257
-0.171054
-0.170093
-0.127627
-0.197892
-0.0845684
-0.237453
0.201339
0.217464
0.249746
-0.174054
0.489815
-0.110371
-0.220817
-0.0599557
-0.19904
-0.113254
0.0501692
0.46328
-0.0730747
0.20534
-0.740707

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268048&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268048&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.031857	-0.029863	-0.012144	3.2977e-08	0.0093001	0.026715	0.042042	0.016497	0.021444
median	-0.06159	-0.056859	-0.046854	-0.044422	-0.04084	-0.019539	0.0010708	0.013824	0.0060137
midrange	-0.11864	-0.11231	-0.082736	-0.082736	0.020708	0.043379	0.043424	0.060524	0.10344
mode	-0.42328	-0.26682	-0.13761	-0.04464	0.13836	0.24975	0.35409	0.17669	0.27597
mode k.dens	-0.12286	-0.10813	-0.090869	-0.079437	-0.065291	-0.056169	-0.05211	0.016845	0.025578

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.031857 & -0.029863 & -0.012144 & 3.2977e-08 & 0.0093001 & 0.026715 & 0.042042 & 0.016497 & 0.021444 \tabularnewline
median & -0.06159 & -0.056859 & -0.046854 & -0.044422 & -0.04084 & -0.019539 & 0.0010708 & 0.013824 & 0.0060137 \tabularnewline
midrange & -0.11864 & -0.11231 & -0.082736 & -0.082736 & 0.020708 & 0.043379 & 0.043424 & 0.060524 & 0.10344 \tabularnewline
mode & -0.42328 & -0.26682 & -0.13761 & -0.04464 & 0.13836 & 0.24975 & 0.35409 & 0.17669 & 0.27597 \tabularnewline
mode k.dens & -0.12286 & -0.10813 & -0.090869 & -0.079437 & -0.065291 & -0.056169 & -0.05211 & 0.016845 & 0.025578 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268048&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]-0.031857[/C][C]-0.029863[/C][C]-0.012144[/C][C]3.2977e-08[/C][C]0.0093001[/C][C]0.026715[/C][C]0.042042[/C][C]0.016497[/C][C]0.021444[/C][/ROW]
[ROW][C]median[/C][C]-0.06159[/C][C]-0.056859[/C][C]-0.046854[/C][C]-0.044422[/C][C]-0.04084[/C][C]-0.019539[/C][C]0.0010708[/C][C]0.013824[/C][C]0.0060137[/C][/ROW]
[ROW][C]midrange[/C][C]-0.11864[/C][C]-0.11231[/C][C]-0.082736[/C][C]-0.082736[/C][C]0.020708[/C][C]0.043379[/C][C]0.043424[/C][C]0.060524[/C][C]0.10344[/C][/ROW]
[ROW][C]mode[/C][C]-0.42328[/C][C]-0.26682[/C][C]-0.13761[/C][C]-0.04464[/C][C]0.13836[/C][C]0.24975[/C][C]0.35409[/C][C]0.17669[/C][C]0.27597[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.12286[/C][C]-0.10813[/C][C]-0.090869[/C][C]-0.079437[/C][C]-0.065291[/C][C]-0.056169[/C][C]-0.05211[/C][C]0.016845[/C][C]0.025578[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268048&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	-0.031857	-0.029863	-0.012144	3.2977e-08	0.0093001	0.026715	0.042042	0.016497	0.021444
median	-0.06159	-0.056859	-0.046854	-0.044422	-0.04084	-0.019539	0.0010708	0.013824	0.0060137
midrange	-0.11864	-0.11231	-0.082736	-0.082736	0.020708	0.043379	0.043424	0.060524	0.10344
mode	-0.42328	-0.26682	-0.13761	-0.04464	0.13836	0.24975	0.35409	0.17669	0.27597
mode k.dens	-0.12286	-0.10813	-0.090869	-0.079437	-0.065291	-0.056169	-0.05211	0.016845	0.025578

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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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