Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_bootstrapplot1.wasp

Title produced by software

Bootstrap Plot - Central Tendency

Date of computation

Thu, 01 Feb 2018 11:46:39 +0100

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/2018/Feb/01/t1517482012e6pkt397nzppe3j.htm/, Retrieved Mon, 29 Apr 2024 01:13:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=314895, Retrieved Mon, 29 Apr 2024 01:13:06 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

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)

-       [Bootstrap Plot - Central Tendency] [] [2018-02-01 10:46:39] [767bae2faba658f23149559b7968621e] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

1.60662064915769
0.410128402876922
-1.16377920133699
1.80340467942787
2.38589051032377
0.907778692066111
-0.78660933160705
1.59703526975859
-0.199367415496587
-0.349815576801861
0.564392642090369
-1.95747451429373
-0.687516608822357
1.32542273724884
-1.31049695570509
-1.52509750872398
-0.503709812049412
1.47518172243876
0.602327712441519
0.40266471358115
-2.46376436924969
2.54376467241789
1.13798307314442
0.430011689125775
-1.02261105982714
-3.77617413371121
1.47323614912204
1.93771551220772
2.8169217523755
1.16911252811059
0.745036748057668
-1.40238049028007
0.575294780513104
-2.43150050782371
2.29110924981554
-4.19016004113439
-0.0335432364908737
-0.769465223104435
-2.28778223092082
-0.506273502754917
-1.50177822186338
0.169633340417429
3.16011105816948
0.719716393366407
-0.511534470493481
2.01634792511037
-0.937490809501962
-1.25227479449756
0.226173657506377
1.14698454308552
-1.3758370890086
0.00762261135933977
1.26320807255057
-0.123929402867701
0.727891344583534
-0.543810750278323
-0.735243797821103
-1.22308128630223
-2.20806387663908
-1.55690031296219
-0.0396945297472815
2.62215888026809
2.12728484366897
-2.40048835229199
0.78226380394563
0.466420538670235
-0.257000189790489
-0.0935218094123086
1.79299790343857
-0.0931211562832655
-2.99901907682355
1.83359118050514
-0.392079347431283
0.401999426083162
0.813360303716075
0.369830781169385
-0.748450730695918
0.900296859090284
0.148391413905535
-2.70465915255315
-1.16919924106049
1.29973132376862
1.26445664890118
1.70205980975602
0.761304298843252
-1.98223654199183
-0.58789464617678
0.348538115162152
1.45490167380993
1.3402316495971
2.66741357644177
2.40879591924632
0.437345252141074
1.43021559807306
1.51545249615207
-0.871749020583807
0.447697125344695
1.30760777927205
-2.98295933852865
-0.86760786525222
-4.42589402152457
1.29741594252144
0.862887889915862
-1.66116356118572
-0.740498445769239
2.11614787459454
0.0174225572572351
0.347799773463786
-0.278807236234121
0.736143182787931
0.153664096759714
-2.6096685056419
-2.93753798875167
1.4161614644138
-0.440738803890321
0.779032647307211
0.821846789611045
-0.772664397590676
1.58239725608346
-0.032752587784386
-0.109854947093452
1.23302125011036
-0.133515112770153
0.503057937844264
-0.148846244686579
-2.007561754851
1.12853566609982
0.150714625143973
0.376628839104367
-2.66241119336979
-0.482391001291208
-1.69308722510401
-0.859597441540925
1.61820613191919
-2.01241704464161
-0.659804920922682
0.637027872614141
-0.681431008494567
1.28641815976828
-0.249211316843449
-0.115979383215134
-1.30030223727631
1.72841215689037
-0.799608773354121
1.01137728253744
-0.561466276324694
-0.0150026222339227
2.29953430978656
0.116763396205775
1.16410217633373
-2.22394229611676
0.0403881004381959
1.5734542462899
1.83759271881349
2.70052498194996
0.15442541046845
-1.07757710253679
-2.00673046191454
0.995812906675979
-1.52186791685738
-0.17479957422021
-1.93035019589904
0.447753592902192
-4.88460245764446
1.56993996773968
1.40572626651795
-1.79430184033269
0.112882412710452
-2.8082541765221
0.185876035798846
1.66483590534653
-0.0851061442043672
-0.714922423653885
1.19992233946685
1.08097127309889
-1.3714962917344
-0.852798952903961
-0.175156020132694
0.903371967339544

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	14 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time14 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314895&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

14 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=314895&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314895&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	14 seconds
R Server	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.27688	-0.14769	-0.069364	8.7431e-17	0.068494	0.15775	0.19372	0.10303	0.13786
median	-0.11598	-0.093522	0.0076226	0.11676	0.16963	0.3496	0.41033	0.13648	0.16201
midrange	-1.1088	-1.092	-0.96426	-0.86225	-0.63289	-0.51502	-0.091049	0.209	0.33137
mode	-3.7803	-2.7047	-0.77615	8.5779e-17	0.72789	1.8049	2.6677	1.3181	1.504
mode k.dens	-0.40153	-0.20324	0.049047	0.2192	0.67781	1.2565	1.3913	0.45789	0.62876

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P1 & P5 & Q1 & Estimate & Q3 & P95 & P99 & S.D. & IQR \tabularnewline
mean & -0.27688 & -0.14769 & -0.069364 & 8.7431e-17 & 0.068494 & 0.15775 & 0.19372 & 0.10303 & 0.13786 \tabularnewline
median & -0.11598 & -0.093522 & 0.0076226 & 0.11676 & 0.16963 & 0.3496 & 0.41033 & 0.13648 & 0.16201 \tabularnewline
midrange & -1.1088 & -1.092 & -0.96426 & -0.86225 & -0.63289 & -0.51502 & -0.091049 & 0.209 & 0.33137 \tabularnewline
mode & -3.7803 & -2.7047 & -0.77615 & 8.5779e-17 & 0.72789 & 1.8049 & 2.6677 & 1.3181 & 1.504 \tabularnewline
mode k.dens & -0.40153 & -0.20324 & 0.049047 & 0.2192 & 0.67781 & 1.2565 & 1.3913 & 0.45789 & 0.62876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=314895&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]-0.27688[/C][C]-0.14769[/C][C]-0.069364[/C][C]8.7431e-17[/C][C]0.068494[/C][C]0.15775[/C][C]0.19372[/C][C]0.10303[/C][C]0.13786[/C][/ROW]
[ROW][C]median[/C][C]-0.11598[/C][C]-0.093522[/C][C]0.0076226[/C][C]0.11676[/C][C]0.16963[/C][C]0.3496[/C][C]0.41033[/C][C]0.13648[/C][C]0.16201[/C][/ROW]
[ROW][C]midrange[/C][C]-1.1088[/C][C]-1.092[/C][C]-0.96426[/C][C]-0.86225[/C][C]-0.63289[/C][C]-0.51502[/C][C]-0.091049[/C][C]0.209[/C][C]0.33137[/C][/ROW]
[ROW][C]mode[/C][C]-3.7803[/C][C]-2.7047[/C][C]-0.77615[/C][C]8.5779e-17[/C][C]0.72789[/C][C]1.8049[/C][C]2.6677[/C][C]1.3181[/C][C]1.504[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.40153[/C][C]-0.20324[/C][C]0.049047[/C][C]0.2192[/C][C]0.67781[/C][C]1.2565[/C][C]1.3913[/C][C]0.45789[/C][C]0.62876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=314895&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=314895&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
statistic	P1	P5	Q1	Estimate	Q3	P95	P99	S.D.	IQR
mean	-0.27688	-0.14769	-0.069364	8.7431e-17	0.068494	0.15775	0.19372	0.10303	0.13786
median	-0.11598	-0.093522	0.0076226	0.11676	0.16963	0.3496	0.41033	0.13648	0.16201
midrange	-1.1088	-1.092	-0.96426	-0.86225	-0.63289	-0.51502	-0.091049	0.209	0.33137
mode	-3.7803	-2.7047	-0.77615	8.5779e-17	0.72789	1.8049	2.6677	1.3181	1.504
mode k.dens	-0.40153	-0.20324	0.049047	0.2192	0.67781	1.2565	1.3913	0.45789	0.62876

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Figure 11

PNG link

Postscript link

PDF link

Parameters (Session):

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)
}
x<-na.omit(x)
(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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code