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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_fitdistrweibull.wasp
Title produced by softwareMaximum-likelihood Fitting - Weibull Distribution
Date of computationMon, 27 Oct 2008 14:09:55 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/27/t122513837093cmh1f9ju07vjr.htm/, Retrieved Sun, 19 May 2024 13:04:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19563, Retrieved Sun, 19 May 2024 13:04:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Central Tendency] [Q6 Distributions] [2007-10-22 19:20:42] [b731da8b544846036771bbf9bf2f34ce]
F    D  [Central Tendency] [Q6] [2008-10-23 11:15:50] [28075c6928548bea087cb2be962cfe7e]
F RMPD      [Maximum-likelihood Fitting - Weibull Distribution] [Investigation Dis...] [2008-10-27 20:09:55] [3bb0537fcae9c337e49b9ce75ff3d4da] [Current]
Feedback Forum
2008-10-31 16:03:08 [Bob Leysen] [reply] 
De dataserie is correct, maar is het niet beter om de lags op 36 in te stellen waardoor je op de lag plot duidelijk kan zien dat er geen afhankelijkheid is tussen de data.
2008-11-03 17:40:37 [Dries Van Gheluwe] [reply] 
Goede poging om een beter overzicht te krijgen van de oefening.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,989130435
0,919087137
0,925417076
0,925612053
1,066666667
0,851108765
1,030693069
0,989031079
0,913000978
0,792723264
0,978170478
0,987513007
0,909433962
0,883608147
0,82745098
0,8252149
1,023255814
0,815418024
1,026192703
0,914742451
0,807276303
0,739130435
0,98973306
0,972164948
0,853889943
0,856864654
0,775739042
0,789473684
0,931350114
0,73971079
0,885245902
0,842435094
0,818458418
0,72755418
0,923238696
0,922680412
0,883762201
0,818270165
0,771047228
0,825852783
0,924485126
0,755165289
0,874671341
0,815956482
0,799807507
0,712598425
0,832980973
0,910323253
0,869149952
0,779182879
0,750254842
0,75856014
0,920889988
0,743991641
0,816254417
0,769593957
0,784007353
0,683284457
0,850505051
0,900695134
0,868398268

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19563&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19563&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19563&T=0

As an alternative you can also use a QR Code:  
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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ParameterEstimated ValueStandard Deviation
shape10.27891985754870.981914874544965
scale0.9027461757108150.0119228457781417

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
shape & 10.2789198575487 & 0.981914874544965 \tabularnewline
scale & 0.902746175710815 & 0.0119228457781417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19563&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]shape[/C][C]10.2789198575487[/C][C]0.981914874544965[/C][/ROW]
[ROW][C]scale[/C][C]0.902746175710815[/C][C]0.0119228457781417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19563&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19563&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ParameterEstimated ValueStandard Deviation
shape10.27891985754870.981914874544965
scale0.9027461757108150.0119228457781417


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 8 ;
Parameters (R input):
par1 = 1 ; par2 = 8 ;
R code (references can be found in the software module):
library(MASS)
PPCCWeibull <- function(shape, scale, x)
{
x <- sort(x)
pp <- ppoints(x)
cor(qweibull(pp, shape=shape, scale=scale), x)
}
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1 < 0.1) par1 <- 0.1
if (par1 > 50) par1 <- 50
if (par2 < 0.1) par2 <- 0.1
if (par2 > 50) par2 <- 50
par1h <- par1*10
par2h <- par2*10
sortx <- sort(x)
c <- array(NA,dim=c(par2h))
for (i in par1h:par2h)
{
c[i] <- cor(qweibull(ppoints(x), shape=i/10,scale=2),sortx)
}
bitmap(file='test1.png')
plot((par1h:par2h)/10,c[par1h:par2h],xlab='shape',ylab='correlation',main='PPCC Plot - Weibull')
dev.off()
f<-fitdistr(x, 'weibull')
f$estimate
f$sd
xlab <- paste('Weibull(shape=',round(f$estimate[[1]],2))
xlab <- paste(xlab,', scale=')
xlab <- paste(xlab,round(f$estimate[[2]],2))
xlab <- paste(xlab,')')
bitmap(file='test2.png')
qqplot(qweibull(ppoints(x), shape=f$estimate[[1]], scale=f$estimate[[2]]), x, main='QQ plot (Weibull)', xlab=xlab )
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Parameter',1,TRUE)
a<-table.element(a,'Estimated Value',1,TRUE)
a<-table.element(a,'Standard Deviation',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'shape',header=TRUE)
a<-table.element(a,f$estimate[1])
a<-table.element(a,f$sd[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'scale',header=TRUE)
a<-table.element(a,f$estimate[2])
a<-table.element(a,f$sd[2])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')