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 09:14:47 +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/t1517472938br1r9il1xcqfp4s.htm/, Retrieved Mon, 29 Apr 2024 07:39:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=313381, Retrieved Mon, 29 Apr 2024 07:39:48 +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 08:14:47] [5890cec7eb26e6825249cd142542fa6d] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

1.72856105193432
0.537017873831517
0.867980543338276
-0.469786520936907
-1.65809537332321
0.146636501699633
-0.0672386349137408
-0.13498404516995
2.12223240279242
-1.19320887060888
1.27933928266697
2.96694884090103
1.39545186365274
-2.1451992140415
-2.43398275751913
0.632864700711983
-0.682707813636235
2.07131204525417
-0.104531746575379
-1.43248560723516
2.06729526607686
-0.762199524917308
-0.656220032961174
-0.272891878011051
-1.38725178006058
2.73448056175203
-0.406928566252687
-1.06644693820818
-0.267090064307712
0.603099469782987
-2.24501673460205
0.704074348654696
-0.14323212663897
1.96584936240914
-0.644282435815208
0.51371072453424
-0.219904883989661
1.53433608960596
0.873410873202486
-0.0810741216198124
2.22709173221706
0.172181871131915
0.156343873949484
-0.119778140438289
0.201567470778364
0.72333345086232
1.94101724859539
0.96842651759471
2.62676169681312
-1.00490408093144
1.4317321639036
0.363326953488502
-1.19455057992329
0.874908023486463
-1.33600415902196
0.777564046322009
1.7059917118051
-1.51728851106142
0.280648212437612
0.628560037292622
-0.380970785994089
-1.10180574201106
0.488976525397815
0.548138796768258
2.34695896401334
1.8680589844253
2.42664197141552
0.772644235160232
-0.148150496596256
-0.66314751310936
1.18319180419306
-1.85385290900922
-0.192985025699194
-1.88497624723516
-1.60767366973841
-1.96420262347225
-1.83163580988418
0.356353819920789
-0.139424675968572
1.03002834236818
-0.87383034679475
1.0942591515073
-0.75970903681286
-1.86572130831276
0.0307532695368973
1.45943548478181
-0.968566254388339
-0.27359733158947
-0.779667001008091
-0.450534010320096
-3.78219026865631
0.570020715704707
-0.908921286671703
-0.550266728957996
0.0788218936010009
1.29430043581788
-0.406246225019453
-0.83598776925008
-0.719487754941648
-0.207533768023798
3.51491483206611
0.741532568484479
0.0363195667803673
0.636057003639965
-1.76614953455778
-1.79476412153827
-1.94638170405188
0.0661695996320116
0.664714600509923
0.39974322111863
-3.75942479132569
1.63115007447768
2.20831840196341
-0.268443445172315
0.783843599184011
1.27822692217559
-0.354551216060357
-0.134550526350053
-3.22470676015799
1.20211034507058
-0.368260965732864
-0.248380427983121
1.57769314333774
-0.198083020227395
-0.505520871287781
1.04816849249361
-1.52550138468108
0.153544508257518
1.72694554270275
-1.19023140010116
-0.742410307645107
0.485072601254582
1.20775671780436
-0.183102393486175
-1.34917720203908
1.35985348068112
-0.0818385426949573
1.10174444630446
0.614187843931161
-2.76102656252592
0.960157328132108
0.0691798496834958
-2.56236140147681
0.135687170538491
-1.08769931577129
0.385580902989905
-1.02234900782415
-0.6986228107012
-1.49310553498315
1.53209806266973
-0.0385009625534758
-1.3395232933726
-1.08832226555382
-2.10030958695191
-1.11463205606125
0.667893597484437
-0.105826159943256
0.547492310686565
-0.285955141601865
1.26088518311837
-1.99925770819766
2.40402841094769
-0.789845751580985
3.53418064863639
0.308019428734996
-1.87700317613243
-0.383228286520615
-1.07287170169405
1.87214923937838
0.125541199509366
0.574284797540187
0.337349699639604
-0.653471487322515
-0.494244507381467
-0.359198646483944
0.902932440228196
1.4871314356976
-1.69322526038076
-1.45487145328323

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 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 time13 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313381&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]

13 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=313381&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313381&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	13 seconds
R Server	Big Analytics Cloud Computing Center

Estimation Results of Bootstrap
statistic	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	-0.25475	-0.20703	-0.069392	-5.7361e-17	0.070543	0.20848	0.28432	0.10611	0.13993
median	-0.2736	-0.26712	-0.13498	-0.081839	0.06617	0.15634	0.2021	0.11428	0.20115
midrange	-0.57821	-0.41053	-0.13364	-0.124	-0.11262	0.38906	0.5501	0.17867	0.021016
mode	-3.7595	-1.8772	-0.40541	-5.806e-17	0.70821	2.2088	2.4282	1.0209	1.1136
mode k.dens	-0.64979	-0.43827	-0.23045	-0.083666	0.025	0.46894	0.53976	0.21415	0.25545

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & P0.5 & P2.5 & Q1 & Estimate & Q3 & P97.5 & P99.5 & S.D. & IQR \tabularnewline
mean & -0.25475 & -0.20703 & -0.069392 & -5.7361e-17 & 0.070543 & 0.20848 & 0.28432 & 0.10611 & 0.13993 \tabularnewline
median & -0.2736 & -0.26712 & -0.13498 & -0.081839 & 0.06617 & 0.15634 & 0.2021 & 0.11428 & 0.20115 \tabularnewline
midrange & -0.57821 & -0.41053 & -0.13364 & -0.124 & -0.11262 & 0.38906 & 0.5501 & 0.17867 & 0.021016 \tabularnewline
mode & -3.7595 & -1.8772 & -0.40541 & -5.806e-17 & 0.70821 & 2.2088 & 2.4282 & 1.0209 & 1.1136 \tabularnewline
mode k.dens & -0.64979 & -0.43827 & -0.23045 & -0.083666 & 0.025 & 0.46894 & 0.53976 & 0.21415 & 0.25545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=313381&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]P0.5[/C][C]P2.5[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]P97.5[/C][C]P99.5[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]-0.25475[/C][C]-0.20703[/C][C]-0.069392[/C][C]-5.7361e-17[/C][C]0.070543[/C][C]0.20848[/C][C]0.28432[/C][C]0.10611[/C][C]0.13993[/C][/ROW]
[ROW][C]median[/C][C]-0.2736[/C][C]-0.26712[/C][C]-0.13498[/C][C]-0.081839[/C][C]0.06617[/C][C]0.15634[/C][C]0.2021[/C][C]0.11428[/C][C]0.20115[/C][/ROW]
[ROW][C]midrange[/C][C]-0.57821[/C][C]-0.41053[/C][C]-0.13364[/C][C]-0.124[/C][C]-0.11262[/C][C]0.38906[/C][C]0.5501[/C][C]0.17867[/C][C]0.021016[/C][/ROW]
[ROW][C]mode[/C][C]-3.7595[/C][C]-1.8772[/C][C]-0.40541[/C][C]-5.806e-17[/C][C]0.70821[/C][C]2.2088[/C][C]2.4282[/C][C]1.0209[/C][C]1.1136[/C][/ROW]
[ROW][C]mode k.dens[/C][C]-0.64979[/C][C]-0.43827[/C][C]-0.23045[/C][C]-0.083666[/C][C]0.025[/C][C]0.46894[/C][C]0.53976[/C][C]0.21415[/C][C]0.25545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=313381&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=313381&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	P0.5	P2.5	Q1	Estimate	Q3	P97.5	P99.5	S.D.	IQR
mean	-0.25475	-0.20703	-0.069392	-5.7361e-17	0.070543	0.20848	0.28432	0.10611	0.13993
median	-0.2736	-0.26712	-0.13498	-0.081839	0.06617	0.15634	0.2021	0.11428	0.20115
midrange	-0.57821	-0.41053	-0.13364	-0.124	-0.11262	0.38906	0.5501	0.17867	0.021016
mode	-3.7595	-1.8772	-0.40541	-5.806e-17	0.70821	2.2088	2.4282	1.0209	1.1136
mode k.dens	-0.64979	-0.43827	-0.23045	-0.083666	0.025	0.46894	0.53976	0.21415	0.25545

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):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;

Parameters (R input):

par1 = 200 ; par2 = 5 ; par3 = 0 ; par4 = P0.5 P2.5 Q1 Q3 P97.5 P99.5 ;

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