Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationWed, 17 Dec 2014 13:55:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418824653dpvpb5dygs0axqz.htm/, Retrieved Thu, 16 May 2024 13:15:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270274, Retrieved Thu, 16 May 2024 13:15:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [normaal verdeling ] [2014-12-17 13:55:55] [a9ee49ff8435be51911716bad99dd485] [Current]
Feedback Forum

Post a new message
Dataseries X:
-6,399530
1,957360
2,478650
1,589720
0,184681
0,918505
-1,995250
1,586390
-0,140506
1,356850
1,727480
5,067280
-2,558530
-0,947617
1,134260
0,264652
2,593240
0,743810
0,743969
-1,112320
1,219600
1,306220
0,553112
0,291992
2,656690
-0,855357
1,923590
1,744140
0,735418
1,048160
0,426579
3,418350
-0,513488
0,833442
-1,064710
-0,237221
-0,759509
1,165110
-7,109370
1,726660
1,589090
-1,344360
1,481350
-0,236383
1,388380
1,018800
-1,946000
-2,255320
1,808120
2,125650
-1,159330
7,908360
0,926642
0,590183
2,058870
0,760719
-0,455908
-2,371740
1,112290
2,440940
-2,108970
-1,545510
-1,471520
3,059330
-0,878558
2,184800
-1,854340
-2,484340
-1,072920
-2,315740
2,722060
-0,029346
-3,150900
0,404606
1,887430
-2,627560
-3,574700
0,754203
-2,192110
1,409100
-3,193550
2,848590
-0,385986
1,743400
-2,023100
0,413897
1,122490
0,182360
-0,302073
0,411861
-2,430770
-0,489293
-0,852392
1,413970
0,056108
1,001380
3,783360
3,091870
-0,146159
-0,833078
-1,827790
0,067725
-0,429261
-0,310928
1,809600
0,624170
-0,700127
-0,723910
1,868270
-3,616350
-1,970360
1,675560
1,876060
3,632780
1,012410
2,040960
3,995540
2,484770
1,588610
2,972830
-0,499202
-3,911090
-2,829110
-6,120530
0,339289
-0,944523
0,035212
-1,042880
-1,323190
-1,133120
0,183183
1,967530
-2,511840
-0,287708
-0,369008
0,685670
0,758509
-1,400430
-3,497010
-0,776372
0,760543
2,310800
-2,673360
0,700963
-4,503390
-5,186020
-1,880700
-7,006000
-0,421422
-2,080020
-3,165650
0,737049
1,532660
0,811355
-0,429889
0,602124
1,069580
-1,168290
-0,541346
-0,852089
1,349240
1,763480
0,356035
-0,821847
0,901744
-1,230250




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270274&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270274&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270274&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.35947-0.27347-0.11109-3.6145e-080.114420.316240.404610.168830.22551
median-0.29022-0.23670.125150.278320.404610.624170.700960.238890.27946
midrange-1.663-1.5569-1.0210.399490.399490.754411.36120.821741.4205
mode-3.4756-2.8404-0.87276-3.6145e-081.11232.80063.77921.74551.985
mode k.dens-0.67616-0.527580.517610.865351.08351.45731.71580.597510.56589

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.35947 & -0.27347 & -0.11109 & -3.6145e-08 & 0.11442 & 0.31624 & 0.40461 & 0.16883 & 0.22551 \tabularnewline
median & -0.29022 & -0.2367 & 0.12515 & 0.27832 & 0.40461 & 0.62417 & 0.70096 & 0.23889 & 0.27946 \tabularnewline
midrange & -1.663 & -1.5569 & -1.021 & 0.39949 & 0.39949 & 0.75441 & 1.3612 & 0.82174 & 1.4205 \tabularnewline
mode & -3.4756 & -2.8404 & -0.87276 & -3.6145e-08 & 1.1123 & 2.8006 & 3.7792 & 1.7455 & 1.985 \tabularnewline
mode k.dens & -0.67616 & -0.52758 & 0.51761 & 0.86535 & 1.0835 & 1.4573 & 1.7158 & 0.59751 & 0.56589 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270274&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]-0.35947[/C][C]-0.27347[/C][C]-0.11109[/C][C]-3.6145e-08[/C][C]0.11442[/C][C]0.31624[/C][C]0.40461[/C][C]0.16883[/C][C]0.22551[/C][/ROW]
[ROW][C]median[/C][C]-0.29022[/C][C]-0.2367[/C][C]0.12515[/C][C]0.27832[/C][C]0.40461[/C][C]0.62417[/C][C]0.70096[/C][C]0.23889[/C][C]0.27946[/C][/ROW]
[ROW][C]midrange[/C][C]-1.663[/C][C]-1.5569[/C][C]-1.021[/C][C]0.39949[/C][C]0.39949[/C][C]0.75441[/C][C]1.3612[/C][C]0.82174[/C][C]1.4205[/C][/ROW]
[ROW][C]mode[/C][C]-3.4756[/C][C]-2.8404[/C][C]-0.87276[/C][C]-3.6145e-08[/C][C]1.1123[/C][C]2.8006[/C][C]3.7792[/C][C]1.7455[/C][C]1.985[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.67616[/C][C]-0.52758[/C][C]0.51761[/C][C]0.86535[/C][C]1.0835[/C][C]1.4573[/C][C]1.7158[/C][C]0.59751[/C][C]0.56589[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270274&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270274&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
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.35947-0.27347-0.11109-3.6145e-080.114420.316240.404610.168830.22551
median-0.29022-0.23670.125150.278320.404610.624170.700960.238890.27946
midrange-1.663-1.5569-1.0210.399490.399490.754411.36120.821741.4205
mode-3.4756-2.8404-0.87276-3.6145e-081.11232.80063.77921.74551.985
mode k.dens-0.67616-0.527580.517610.865351.08351.45731.71580.597510.56589



Parameters (Session):
par1 = 166 ; par2 = 5 ; par3 = 0 ; par4 = P1 P5 Q1 Q3 P95 P99 ;
Parameters (R input):
par1 = 166 ; 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')