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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_bootstrapplot.wasp

Title produced by software

Blocked Bootstrap Plot - Central Tendency

Date of computation

Fri, 04 Dec 2015 22:10:14 +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/2015/Dec/04/t1449267045462xqxsx9l9lg8v.htm/, Retrieved Sat, 18 May 2024 12:18:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285183, Retrieved Sat, 18 May 2024 12:18:15 +0000

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Original text written by user:

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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)

-       [Blocked Bootstrap Plot - Central Tendency] [] [2015-12-04 22:10:14] [63a9f0ea7bb98050796b649e85481845] [Current]

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Dataseries X:

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Histogram

Boxplots

-30.75
-209.8
-210.8
-332.8
-85.75
-206.8
-158.8
-87.75
-138.8
-64.75
434.2
430.2
34.25
47.25
-0.7515
-159.8
-142.8
-197.8
87.25
82.25
1.249
290.2
524.2
760.2
312.2
-62.75
-24.75
-94.75
87.25
28.25
77.25
208.2
-98.75
274.2
515.2
474.2
362.2
50.25
117.2
-148.8
258.2
135.2
247.2
-28.75
60.25
258.2
679.2
936.2
379.2
245.2
-40.75
223.2
285.2
95.25
294.2
194.2
366.2
362.2
400.2
432.2
-109.8
-214.8
-169.8
-335.8
13.25
80.25
61.25
169.2
286.2
359.2
374.2
333.2
-140.8
-361.8
-65.75
-335.8
-198.8
-296.8
-275.8
-174.8
-61.75
-156.8
187.2
481.2
-244.8
-62.75
-310.8
-322.8
-187.8
-408.8
-191.8
-390.8
-90.75
30.25
240.2
556.2
-69.75
-316.8
-306.8
-314.8
-323.8
-197.8
-189.8
-74.75
-202.8
-32.75
282.2
497.2
238.2
-255.8
-154.8
-258.8
-271.8
-95.75
-60.75
-79.75
-74.75
-34.75
332.2
544.2
95.25
-272.8
44.25
-256.8
-161.8
-286.8
-290.8
-163.8
-72.75
-64.75
298.2
489.2
-52.75
-356.8
-211.8
-357.8
-264.8
-195.8
-257.8
-165.8
-169.8
109.2
19.25
223.2
-243.8
-259.8
-175.8
-313.8
-195.8
-332.8
-76.75
-207.8
-36.75
220.2
150.2
8.249
-261.8
-272.8
-261.8
-352.8
-230.8
-159.8
-229.8
-33.75
-123.8
132.2
280.2
361.2
-223.8
-264.7
-103.7
-153.7
-85.7
-245.7
-147.7
-182.7
105.3
165.3
161.3
191.3
35.3
-156.7
-39.7
-211.7
-24.7
-136.7
-99.7
-37.7
122.3
253.3
415.3
441.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'George Udny Yule' @ yule.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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285183&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285183&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285183&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	9 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Estimation Results of Blocked Bootstrap
statistic	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	-73.852	-61.932	-25.34	-0.030747	20.25	56.856	81.336	31.055	45.59
median	-149.49	-138.8	-76.875	-63.75	-37.7	23.75	52.775	34.441	39.175
midrange	67.7	73.7	175.7	263.7	263.7	289.2	291.7	66.624	88
mode	-335.8	-335.09	-195.8	-68.909	95.25	362.2	362.2	187.39	291.05
mode k.dens	-240.89	-224.78	-191.67	-170.47	-141.4	-79.505	-27.455	41.601	50.271

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & P0.5 & P2.5 & Q1 & Estimate & Q3 & P97.5 & P99.5 & S.D. & IQR \tabularnewline
mean & -73.852 & -61.932 & -25.34 & -0.030747 & 20.25 & 56.856 & 81.336 & 31.055 & 45.59 \tabularnewline
median & -149.49 & -138.8 & -76.875 & -63.75 & -37.7 & 23.75 & 52.775 & 34.441 & 39.175 \tabularnewline
midrange & 67.7 & 73.7 & 175.7 & 263.7 & 263.7 & 289.2 & 291.7 & 66.624 & 88 \tabularnewline
mode & -335.8 & -335.09 & -195.8 & -68.909 & 95.25 & 362.2 & 362.2 & 187.39 & 291.05 \tabularnewline
mode k.dens & -240.89 & -224.78 & -191.67 & -170.47 & -141.4 & -79.505 & -27.455 & 41.601 & 50.271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285183&T=1

[TABLE]
[ROW][C]Estimation Results of Blocked Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P0.5[/C][C]P2.5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P97.5[/C][C]P99.5[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]-73.852[/C][C]-61.932[/C][C]-25.34[/C][C]-0.030747[/C][C]20.25[/C][C]56.856[/C][C]81.336[/C][C]31.055[/C][C]45.59[/C][/ROW]
[ROW][C]median[/C][C]-149.49[/C][C]-138.8[/C][C]-76.875[/C][C]-63.75[/C][C]-37.7[/C][C]23.75[/C][C]52.775[/C][C]34.441[/C][C]39.175[/C][/ROW]
[ROW][C]midrange[/C][C]67.7[/C][C]73.7[/C][C]175.7[/C][C]263.7[/C][C]263.7[/C][C]289.2[/C][C]291.7[/C][C]66.624[/C][C]88[/C][/ROW]
[ROW][C]mode[/C][C]-335.8[/C][C]-335.09[/C][C]-195.8[/C][C]-68.909[/C][C]95.25[/C][C]362.2[/C][C]362.2[/C][C]187.39[/C][C]291.05[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-240.89[/C][C]-224.78[/C][C]-191.67[/C][C]-170.47[/C][C]-141.4[/C][C]-79.505[/C][C]-27.455[/C][C]41.601[/C][C]50.271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285183&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285183&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 Blocked Bootstrap
statistic	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	-73.852	-61.932	-25.34	-0.030747	20.25	56.856	81.336	31.055	45.59
median	-149.49	-138.8	-76.875	-63.75	-37.7	23.75	52.775	34.441	39.175
midrange	67.7	73.7	175.7	263.7	263.7	289.2	291.7	66.624	88
mode	-335.8	-335.09	-195.8	-68.909	95.25	362.2	362.2	187.39	291.05
mode k.dens	-240.89	-224.78	-191.67	-170.47	-141.4	-79.505	-27.455	41.601	50.271

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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Parameters (Session):

par1 = 500 ; par2 = 12 ; par3 = 5 ; par4 = P0.5 P2.5 Q1 Q3 P97.5 P99.5 ;

Parameters (R input):

par1 = 500 ; par2 = 12 ; par3 = 5 ; par4 = P0.5 P2.5 Q1 Q3 P97.5 P99.5 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
if (par2 < 3) par2 = 3
if (par2 > length(x)) par2 = length(x)
library(modeest)
library(lattice)
library(boot)
boot.stat <- function(s)
{
s.mean <- mean(s)
s.median <- median(s)
s.midrange <- (max(s) + min(s)) / 2
s.mode <- mlv(s,method='mfv')$M
s.kernelmode <- mlv(s, method='kernel')$M
c(s.mean, s.median, s.midrange, s.mode, s.kernelmode)
}
(r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))
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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot7a.png')
plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')
grid()
dev.off()
bitmap(file='plot8a.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()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]) )
colnames(z) <- list('mean','median','midrange','mode','mode.k.dens')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
if (par4 == 'P1 P5 Q1 Q3 P95 P99') {
myq.1 <- 0.01
myq.2 <- 0.05
myq.3 <- 0.95
myq.4 <- 0.99
myl.1 <- 'P1'
myl.2 <- 'P5'
myl.3 <- 'P95'
myl.4 <- 'P99'
}
if (par4 == 'P0.5 P2.5 Q1 Q3 P97.5 P99.5') {
myq.1 <- 0.005
myq.2 <- 0.025
myq.3 <- 0.975
myq.4 <- 0.995
myl.1 <- 'P0.5'
myl.2 <- 'P2.5'
myl.3 <- 'P97.5'
myl.4 <- 'P99.5'
}
if (par4 == 'P10 P20 Q1 Q3 P80 P90') {
myq.1 <- 0.10
myq.2 <- 0.20
myq.3 <- 0.80
myq.4 <- 0.90
myl.1 <- 'P10'
myl.2 <- 'P20'
myl.3 <- 'P80'
myl.4 <- 'P90'
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked Bootstrap',10,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,myl.1,header=TRUE)
a<-table.element(a,myl.2,header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,myl.3,header=TRUE)
a<-table.element(a,myl.4,header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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$t[,1],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[1],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element( a,signif( sqrt(var(r$t[,1])),par3 ) )
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$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$t[,2],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[2],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,2])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$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$t[,3],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[3],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,3])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode',header=TRUE)
q1 <- quantile(r$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$t[,4],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[4],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,4])),par3))
a<-table.element(a,signif(q3-q1,par3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mode k.dens',header=TRUE)
q1 <- quantile(r$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$t[,5],myq.4)[[1]]
a<-table.element(a,signif(p01,par3))
a<-table.element(a,signif(p05,par3))
a<-table.element(a,signif(q1,par3))
a<-table.element(a,signif(r$t0[5],par3))
a<-table.element(a,signif(q3,par3))
a<-table.element(a,signif(p95,par3))
a<-table.element(a,signif(p99,par3))
a<-table.element(a,signif(sqrt(var(r$t[,5])),par3))
a<-table.element(a,signif(q3-q1,par3))
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