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Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationMon, 08 Dec 2014 12:22:24 +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/08/t1418041360dfgps1am9lxrd1d.htm/, Retrieved Tue, 28 May 2024 05:19:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263961, Retrieved Tue, 28 May 2024 05:19:05 +0000
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Original text written by user:
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Estimated Impact111
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [paper] [2014-12-08 12:22:24] [e9c088684c75c49d5852bf7a76ea4083] [Current]
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Dataseries X:
120.093
-351.676
-338.479
0.472612
380.597
171.015
0.00666056
-286.698
0.51677
-374.454
-0.83164
0.387595
141.365
0.236455
-0.710433
0.765947
14.665
-15.723
109.504
-184.515
0.682901
-0.515642
120.615
-0.732037
0.822836
-0.160341
11.178
0.34291
165.886
-0.457584
134.941
-0.0444863
245.207
-125.313
0.738716
0.650728
194.344
278.003
0.161855
511.005
-204.652
109.887
-15.939
-174.185
239.571
-115.684
206.264
152.867
119.696
223.949
-0.317213
-244.084
-114.364
-0.132442
-357.088
-146.265
0.441838
-433.909
-119.998
-0.798762
-171.408
-239.087
0.948733
-500.284
125.928
183.187
221.836
-133.161
106.308
-147.631
120.906
-172.207
238.378
0.621191
471.725
-22.675
0.195026
0.663776
-204.718
0.932959
0.591262
0.461232
-111.488
125.758
0.746978
0.203522
0.239535
294.732
-244.552
0.991447
0.739422
-0.172084
105.195
-0.100977
250.145
-0.257124
0.671047
-104.831
-0.222806
-148.214
105.551
-541.034
116.555
126.885
-0.163632
223.316
0.208934
153.487
148.537
-140.243
-207.335
156.269
154.121
-108.727
548.502
0.477977
0.765523
0.317907
122.327
-0.632595
-208.584
0.52982
182.179
-150.862
0.00135998
-0.0521667
197.515
-117.608
154.831
-140.358
-266.258
-235.626
-150.684
159.896
-0.799114
-393.111
-0.446119
213.326
-261.327
-178.088
0.415253
-0.835093
240.309
-254.673
214.049
0.138108
106.069
-287.822
0.746425
162.854
0.241399
124.857
12.705
-133.526
0.105196
-110.337
114.058
-0.799817
157.709
191.762
0.979804
107.901
0.0411016
-168.842
0.262141
-0.442875
-0.552741
0.716457
188.287
-0.0393462
-0.833234
119.687
-177.247
-0.57501
0.520721
273.503
203.026
127.582
280.783
294.232
18.665
185.112
143.155
0.765099
-304.298
-0.503136
-496.376
0.225924
-237.984
0.0842546
-122.323
-112.792
-251.432
0.396121
108.721
-115.282
0.250501
0.525974
-136.848
-0.0447008
-155.841
-356.726
-162.088
163.111
153.262
-241.612
0.704191
-315.322
-443.223
-171.711
-729.104
-0.184478
-0.168019
-22.065
0.645385
120.996
0.831122
0.383327
-0.11983
164.108
0.0467481
222.182
-0.749133
140.015
0.954716
0.37954
0.299912
119.992
-0.811057




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263961&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263961&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263961&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-30.024-24.358-11.975-5.59330.8047213.98919.21111.33412.779
median0.00666060.0467480.203520.25050.342910.461230.526930.113540.13939
midrange-174.25-128.69-90.301-90.301-15.01424.10938.68951.8275.287
mode-729.1-282.68-77.313-5.5933103.7213.26511.38177.21181.01
mode k.dens-114.3-28.8820.142580.21670.316335.78844.94238.9480.17372

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -30.024 & -24.358 & -11.975 & -5.5933 & 0.80472 & 13.989 & 19.211 & 11.334 & 12.779 \tabularnewline
median & 0.0066606 & 0.046748 & 0.20352 & 0.2505 & 0.34291 & 0.46123 & 0.52693 & 0.11354 & 0.13939 \tabularnewline
midrange & -174.25 & -128.69 & -90.301 & -90.301 & -15.014 & 24.109 & 38.689 & 51.82 & 75.287 \tabularnewline
mode & -729.1 & -282.68 & -77.313 & -5.5933 & 103.7 & 213.26 & 511.38 & 177.21 & 181.01 \tabularnewline
mode k.dens & -114.3 & -28.882 & 0.14258 & 0.2167 & 0.3163 & 35.788 & 44.942 & 38.948 & 0.17372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263961&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]-30.024[/C][C]-24.358[/C][C]-11.975[/C][C]-5.5933[/C][C]0.80472[/C][C]13.989[/C][C]19.211[/C][C]11.334[/C][C]12.779[/C][/ROW]
[ROW][C]median[/C][C]0.0066606[/C][C]0.046748[/C][C]0.20352[/C][C]0.2505[/C][C]0.34291[/C][C]0.46123[/C][C]0.52693[/C][C]0.11354[/C][C]0.13939[/C][/ROW]
[ROW][C]midrange[/C][C]-174.25[/C][C]-128.69[/C][C]-90.301[/C][C]-90.301[/C][C]-15.014[/C][C]24.109[/C][C]38.689[/C][C]51.82[/C][C]75.287[/C][/ROW]
[ROW][C]mode[/C][C]-729.1[/C][C]-282.68[/C][C]-77.313[/C][C]-5.5933[/C][C]103.7[/C][C]213.26[/C][C]511.38[/C][C]177.21[/C][C]181.01[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-114.3[/C][C]-28.882[/C][C]0.14258[/C][C]0.2167[/C][C]0.3163[/C][C]35.788[/C][C]44.942[/C][C]38.948[/C][C]0.17372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263961&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263961&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-30.024-24.358-11.975-5.59330.8047213.98919.21111.33412.779
median0.00666060.0467480.203520.25050.342910.461230.526930.113540.13939
midrange-174.25-128.69-90.301-90.301-15.01424.10938.68951.8275.287
mode-729.1-282.68-77.313-5.5933103.7213.26511.38177.21181.01
mode k.dens-114.3-28.8820.142580.21670.316335.78844.94238.9480.17372



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')