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Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationFri, 19 Apr 2013 07:22:52 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Apr/19/t13663706879e9u202ec895ti9.htm/, Retrieved Sun, 28 Apr 2024 10:16:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=208132, Retrieved Sun, 28 Apr 2024 10:16:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [750 simultaties e...] [2013-04-19 11:22:52] [09688f513f3d2798cb35a3603f8bd204] [Current]
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Dataseries X:
68.906
39.556
50.669
36.432
40.891
48.428
36.222
33.425
39.401
37.967
34.801
12.657
69.116
41.519
51.321
38.529
41.547
52.073
38.401
40.898
40.439
41.888
37.898
8.771
68.184
50.530
47.221
41.756
45.633
48.138
39.486
39.341
41.117
41.629
29.722
7.054
56.676
34.870
35.117
30.169
30.936
35.699
33.228
27.733
33.666
35.429
27.438
8.170
63.410
38.040
45.389
37.353
37.024
50.957
37.994
36.454
46.080
43.373
37.395
10.963
76.058
50.179
57.452
47.568
50.050
50.856
41.992
39.284
44.521
43.832
41.153
17.100




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time19 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 19 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208132&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]19 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208132&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208132&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time19 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean39.28240.48941.3431.52722.0616
median39.34139.99840.8981.021.557
midrange38.08541.55641.5562.11743.471
mode35.69940.48944.50110.2898.8016
mode k.dens38.233939.7371.46251.5065

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 39.282 & 40.489 & 41.343 & 1.5272 & 2.0616 \tabularnewline
median & 39.341 & 39.998 & 40.898 & 1.02 & 1.557 \tabularnewline
midrange & 38.085 & 41.556 & 41.556 & 2.1174 & 3.471 \tabularnewline
mode & 35.699 & 40.489 & 44.501 & 10.289 & 8.8016 \tabularnewline
mode k.dens & 38.23 & 39 & 39.737 & 1.4625 & 1.5065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208132&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]39.282[/C][C]40.489[/C][C]41.343[/C][C]1.5272[/C][C]2.0616[/C][/ROW]
[ROW][C]median[/C][C]39.341[/C][C]39.998[/C][C]40.898[/C][C]1.02[/C][C]1.557[/C][/ROW]
[ROW][C]midrange[/C][C]38.085[/C][C]41.556[/C][C]41.556[/C][C]2.1174[/C][C]3.471[/C][/ROW]
[ROW][C]mode[/C][C]35.699[/C][C]40.489[/C][C]44.501[/C][C]10.289[/C][C]8.8016[/C][/ROW]
[ROW][C]mode k.dens[/C][C]38.23[/C][C]39[/C][C]39.737[/C][C]1.4625[/C][C]1.5065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208132&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208132&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
statisticQ1EstimateQ3S.D.IQR
mean39.28240.48941.3431.52722.0616
median39.34139.99840.8981.021.557
midrange38.08541.55641.5562.11743.471
mode35.69940.48944.50110.2898.8016
mode k.dens38.233939.7371.46251.5065



Parameters (Session):
par1 = 750 ; par2 = 5 ; par3 = 0 ;
Parameters (R input):
par1 = 750 ; par2 = 5 ; par3 = 0 ;
R code (references can be found in the software module):
par3 <- '0'
par2 <- '5'
par1 <- '200'
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$t[,5]))
colnames(z) <- list('mean','median','midrange','mode','mode k.dens')
bitmap(file='plot11.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[1],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element( a,signif( 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$t[,2],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[2],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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$t[,3],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[3],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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$t[,4],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[4],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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$t[,5],0.75)[[1]]
a<-table.element(a,signif(q1,par2))
a<-table.element(a,signif(r$t0[5],par2))
a<-table.element(a,signif(q3,par2))
a<-table.element(a,signif(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')