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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 computationThu, 13 Nov 2014 20:13:25 +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/Nov/13/t1415909620d084hzmtojq86gn.htm/, Retrieved Sun, 19 May 2024 10:06:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=254629, Retrieved Sun, 19 May 2024 10:06:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [] [2014-11-04 10:49:59] [32b17a345b130fdf5cc88718ed94a974]
- R  D    [Bootstrap Plot - Central Tendency] [] [2014-11-13 20:13:25] [f11ff77f11120bba23ccca75f7c5b5e0] [Current]
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Dataseries X:
-202.299
7.494
-328.892
-27.246
886.727
442.772
-12.973
682.049
-0.2965
369.717
368.786
-239.836
-156.832
-187.865
10.701
-123.056
442.937
122.132
-106.871
-151.104
-959.867
-0.467128
-196.324
-143.937
108.181
735.019
191.578
-131.256
690.733
-232.521
541.369
-134.635
-397.318
103.516
302.827
-348.996
105.703
-174.286
201.697
-384.932
-0.853791
340.719
-232.472
-173.174
-410.134
-188.461
-107.255
195.176
181.206
242.766
-292.171
-272.907
-259.056
-812.117
-679.598
544.658
0.700685
-707.565
0.137859
111.292
-825.729
478.038
91.329
-180.934
-177.844
870.295
-185.765
298.153
-98.386
-269.844
-41.493
-167.145
815.544
-668.813
122.151
136.579
-390.455
122.404
638.864
-196.264
557.687
-419.453
-0.529929
-0.201342
615.515
256.753
-302.497
-146.376
894.669
0.937884
-161.392
429.194
127.816
100.564
539.514
0.0670371
55.854
605.386
246.471
-0.19633
431.513
10.457
-425.997
89.642
299.667
127.668
-0.341277
-141.489
-165.542
142.728
58.288
154.524
-312.911
1.84
107.383
-170.249
-425.156
-478.924
191.791
-146.616
608.799
148.926
-0.514799
-0.506566
682.039
131.598
-303.872
586.966
0.419371
-0.707066
387.902
226.248
-376.795
0.721455
139.173
-637.364
-763.212
-125.051
-868.453
-141.311
-193.233
12.964
138.727
570.431
154.574
32.631
-100.058
374.379
910.721
119.552
942.455
-429.606
112.344
453.737
120.169
-161.392
-0.680447
586.966
105.527
179.739
0.795888
658.255
121.684
134.768
615.338
-599.276
681.101
667.135
153.195
854.235
-8.907
-17.883
80.654
-640.626
-0.00809423
471.089
185.628
376.599
-654.335
478.822
-193.224
-534.134
-378.684
608.905
180.436
940.579
295.884
147.808
-137.803
-176.174
-315.107
0.372594
0.253211
-600.097
166.763
-174.295
123.268
168.399
-122.693
740.696
39.433
-148.981
158.006
-156.927
-0.676103
204.732
125.709
-194.654
0.278092
111.612
-149.765
861.345
207.809
110.393
-0.270594
-499.331
105.954
105.283
-0.507829
100.943
567.545
-155.667
589.696
-146.109
-246.571
499.101
-20.553
-737.405
570.919
0.834923
0.168965
100.219
-157.744
247.166
-266.757
152.079
101.703
-203.949
227.432
252.005
175.841
-107.343
-724.632
-132.715
108.155
55.919
-0.750061
-196.754
-430.115
-115.273
0.223532
113.642
-0.496627
174.534
0.67617
-159.456
114.586
-197.289
-36.508
126.323
-660.879
-454.858
0.682932
128.438




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254629&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]7 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=254629&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254629&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Estimation Results of Bootstrap
statisticP1P5Q1EstimateQ3P95P99S.D.IQR
mean-0.190296.227326.77939.13951.79777.08595.55621.59125.017
median-0.319740.0987990.530110.7586710.45758.788101.1422.229.9269
midrange-32.639-24.573-8.706-8.70636.06358.36389.62228.06744.769
mode-825.73-452.65-108.93212.79185.68586.97682.04287.22294.62
mode k.dens-34.838-2.016-0.37377-0.0020049.9745122.75128.0641.45810.348

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.19029 & 6.2273 & 26.779 & 39.139 & 51.797 & 77.085 & 95.556 & 21.591 & 25.017 \tabularnewline
median & -0.31974 & 0.098799 & 0.53011 & 0.75867 & 10.457 & 58.788 & 101.14 & 22.22 & 9.9269 \tabularnewline
midrange & -32.639 & -24.573 & -8.706 & -8.706 & 36.063 & 58.363 & 89.622 & 28.067 & 44.769 \tabularnewline
mode & -825.73 & -452.65 & -108.93 & 212.79 & 185.68 & 586.97 & 682.04 & 287.22 & 294.62 \tabularnewline
mode k.dens & -34.838 & -2.016 & -0.37377 & -0.002004 & 9.9745 & 122.75 & 128.06 & 41.458 & 10.348 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=254629&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]-0.19029[/C][C]6.2273[/C][C]26.779[/C][C]39.139[/C][C]51.797[/C][C]77.085[/C][C]95.556[/C][C]21.591[/C][C]25.017[/C][/ROW]
[ROW][C]median[/C][C]-0.31974[/C][C]0.098799[/C][C]0.53011[/C][C]0.75867[/C][C]10.457[/C][C]58.788[/C][C]101.14[/C][C]22.22[/C][C]9.9269[/C][/ROW]
[ROW][C]midrange[/C][C]-32.639[/C][C]-24.573[/C][C]-8.706[/C][C]-8.706[/C][C]36.063[/C][C]58.363[/C][C]89.622[/C][C]28.067[/C][C]44.769[/C][/ROW]
[ROW][C]mode[/C][C]-825.73[/C][C]-452.65[/C][C]-108.93[/C][C]212.79[/C][C]185.68[/C][C]586.97[/C][C]682.04[/C][C]287.22[/C][C]294.62[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-34.838[/C][C]-2.016[/C][C]-0.37377[/C][C]-0.002004[/C][C]9.9745[/C][C]122.75[/C][C]128.06[/C][C]41.458[/C][C]10.348[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=254629&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=254629&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-0.190296.227326.77939.13951.79777.08595.55621.59125.017
median-0.319740.0987990.530110.7586710.45758.788101.1422.229.9269
midrange-32.639-24.573-8.706-8.70636.06358.36389.62228.06744.769
mode-825.73-452.65-108.93212.79185.68586.97682.04287.22294.62
mode k.dens-34.838-2.016-0.37377-0.0020049.9745122.75128.0641.45810.348



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')