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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_fitdistrweibull.wasp
Title produced by softwareMaximum-likelihood Fitting - Weibull Distribution
Date of computationThu, 06 Dec 2012 19:31:39 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/06/t1354840573qt2bn06axo4py09.htm/, Retrieved Sat, 27 Apr 2024 00:20:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197255, Retrieved Sat, 27 Apr 2024 00:20:45 +0000
QR Codes:

Original text written by user:target conc. 0.15%
IsPrivate?No (this computation is public)
User-defined keywordsForensic alcohol analysis (FAA) Proficiency testing Crime Labs in California USA blood pool November 2012, CDPH ASAS
Estimated Impact123

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Maximum-likelihood Fitting - Weibull Distribution] [CDPH pool 10082-W...] [2012-12-07 00:31:39] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   PD    [Maximum-likelihood Fitting - Weibull Distribution] [CDPH pool 10082-W...] [2012-12-11 18:47:19] [74be16979710d4c4e7c6647856088456]
- RMPD    [Bootstrap Plot - Central Tendency] [CDPH pool 10152 b...] [2012-12-11 19:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD    [Kernel Density Estimation] [CDPH pool 10152 G...] [2012-12-11 19:59:00] [74be16979710d4c4e7c6647856088456]
- RMPD    [Bootstrap Plot - Central Tendency] [CDPH pool 10152 b...] [2012-12-11 21:29:01] [74be16979710d4c4e7c6647856088456]
- R PD      [Bootstrap Plot - Central Tendency] [CDPH pool 06173 b...] [2013-08-16 00:46:13] [74be16979710d4c4e7c6647856088456]
- R  D    [Maximum-likelihood Fitting - Weibull Distribution] [CDPH pool 10152-W...] [2012-12-11 22:10:22] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.133
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0.141
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 2 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197255&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197255&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 time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






ParameterEstimated ValueStandard Deviation
shape20.34694247340032.36375789345456
scale0.1535070242379360.00130032130585167

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
shape & 20.3469424734003 & 2.36375789345456 \tabularnewline
scale & 0.153507024237936 & 0.00130032130585167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197255&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]shape[/C][C]20.3469424734003[/C][C]2.36375789345456[/C][/ROW]
[ROW][C]scale[/C][C]0.153507024237936[/C][C]0.00130032130585167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197255&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
shape20.34694247340032.36375789345456
scale0.1535070242379360.00130032130585167


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 30 ; par2 = 40 ;
Parameters (R input):
par1 = 30 ; par2 = 40 ;
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')