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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

Sat, 17 Dec 2016 04:32:54 +0100

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/2016/Dec/17/t148194565549ey5t7tkjl2i2z.htm/, Retrieved Fri, 01 Nov 2024 03:39:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300594, Retrieved Fri, 01 Nov 2024 03:39:52 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

144

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

-       [Bootstrap Plot - Central Tendency] [bootstrap plot (a...] [2016-12-17 03:32:54] [1e2c9196efc58119c3757b6c78ac7c5f] [Current]

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Dataseries X:

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Histogram

Boxplots

-2.759
2.241
0.7181
0.7515
-1.792
3.28
-1.572
2.461
-1.394
-1.321
2.645
-0.7157
3.131
1.202
-1.288
-2.541
3.897
-0.4605
-0.6528
-0.6863
-5.52
3.325
2.862
-0.03237
-1.215
1.284
0.7124
2.319
0.6414
-2.649
2.602
3.712
-11.33
2.351
-0.6488
-1.615
-0.6488
2.617
-0.4687
-1.288
1.078
-2.5
3.394
2.385
-9.105
-3.176
-0.7964
-0.6863
-0.8689
1.862
1.675
-0.5762
0.4238
2.534
-0.3267
2.461
-3.826
-0.3251
-3.254
-4.496
0.7557
0.7124
0.7515
-0.4661
0.07357
-1.891
-0.6153
-3.504
-0.3977
-2.649
3.241
-4.398
-3.649
1.858
-2.508
2.968
1.169
-2.394
1.824
2.645
-1.392
-2.392
1.474
2.492
-3.288
2.314
1.785
3.131
-1.398
-1.253
-1.031
3.641
-0.4495
2.679
1.241
2.107
-1.288
3.092
1.401
-4.323
1.351
2.275
3.901
-3.248
0.3137
-1.653
-0.3977
1.636
1.165
-2.725
-1.576
-4.215
0.4238
-2.253
1.314
0.9676
0.1645
3.422
-0.07149
-2.105
-1.215
1.496
3.347
0.6749
0.6749
0.3512
0.2411
-1.392
0.7124
1.863
0.4196
3.208
-1.543
-0.4271
-1.288
1.752
-5.398
-0.9599
1.822
3.347
2.714
-2.465
-0.7629
-0.7438
0.8225
2.874
3.457
0.789
1.208
-0.7772
-0.2876
-5.398
-1.215
1.858
0.2411
-0.3935
-4.763
2.208
1.752
-1.724
0.4613
2.853
1.857
2.602
-0.2819
-0.8355
-0.2876
-2.248

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	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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300594&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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300594&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300594&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	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.52477	-0.37505	-0.15691	-6.7798e-05	0.11813	0.31843	0.43375	0.20703	0.27504
median	-0.44964	-0.3977	-0.2819	0.2028	0.3512	0.65899	0.7124	0.33446	0.6331
midrange	-3.9367	-3.809	-3.7145	-3.7145	-2.604	-0.8095	-0.7485	0.92138	1.1105
mode	-5.398	-2.649	-1.288	-1.288	0.7124	2.6041	3.1332	1.6153	2.0004
mode k.dens	-1.1195	-1.0568	-0.76592	-0.60613	0.92274	2.5036	2.7023	1.1875	1.6887

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.52477 & -0.37505 & -0.15691 & -6.7798e-05 & 0.11813 & 0.31843 & 0.43375 & 0.20703 & 0.27504 \tabularnewline
median & -0.44964 & -0.3977 & -0.2819 & 0.2028 & 0.3512 & 0.65899 & 0.7124 & 0.33446 & 0.6331 \tabularnewline
midrange & -3.9367 & -3.809 & -3.7145 & -3.7145 & -2.604 & -0.8095 & -0.7485 & 0.92138 & 1.1105 \tabularnewline
mode & -5.398 & -2.649 & -1.288 & -1.288 & 0.7124 & 2.6041 & 3.1332 & 1.6153 & 2.0004 \tabularnewline
mode k.dens & -1.1195 & -1.0568 & -0.76592 & -0.60613 & 0.92274 & 2.5036 & 2.7023 & 1.1875 & 1.6887 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300594&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]-0.52477[/C][C]-0.37505[/C][C]-0.15691[/C][C]-6.7798e-05[/C][C]0.11813[/C][C]0.31843[/C][C]0.43375[/C][C]0.20703[/C][C]0.27504[/C][/ROW]
[ROW][C]median[/C][C]-0.44964[/C][C]-0.3977[/C][C]-0.2819[/C][C]0.2028[/C][C]0.3512[/C][C]0.65899[/C][C]0.7124[/C][C]0.33446[/C][C]0.6331[/C][/ROW]
[ROW][C]midrange[/C][C]-3.9367[/C][C]-3.809[/C][C]-3.7145[/C][C]-3.7145[/C][C]-2.604[/C][C]-0.8095[/C][C]-0.7485[/C][C]0.92138[/C][C]1.1105[/C][/ROW]
[ROW][C]mode[/C][C]-5.398[/C][C]-2.649[/C][C]-1.288[/C][C]-1.288[/C][C]0.7124[/C][C]2.6041[/C][C]3.1332[/C][C]1.6153[/C][C]2.0004[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-1.1195[/C][C]-1.0568[/C][C]-0.76592[/C][C]-0.60613[/C][C]0.92274[/C][C]2.5036[/C][C]2.7023[/C][C]1.1875[/C][C]1.6887[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300594&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300594&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	-0.52477	-0.37505	-0.15691	-6.7798e-05	0.11813	0.31843	0.43375	0.20703	0.27504
median	-0.44964	-0.3977	-0.2819	0.2028	0.3512	0.65899	0.7124	0.33446	0.6331
midrange	-3.9367	-3.809	-3.7145	-3.7145	-2.604	-0.8095	-0.7485	0.92138	1.1105
mode	-5.398	-2.649	-1.288	-1.288	0.7124	2.6041	3.1332	1.6153	2.0004
mode k.dens	-1.1195	-1.0568	-0.76592	-0.60613	0.92274	2.5036	2.7023	1.1875	1.6887

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 = 1111110.95200 ; par2 = 22222205 ; par3 = TRUETRUETRUEFALSETRUETRUE0 ; 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)
}
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