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Author's title

Author*Unverified author*
R Software Modulerwasp_fitdistrweibull.wasp
Title produced by softwareMaximum-likelihood Fitting - Weibull Distribution
Date of computationFri, 17 Apr 2020 21:31:13 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Apr/17/t1587151953fhomty56i8jy7x7.htm/, Retrieved Wed, 21 Apr 2021 07:00:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319136, Retrieved Wed, 21 Apr 2021 07:00:32 +0000
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Original text written by user:orignal
IsPrivate?No (this computation is public)
User-defined keywordswebull
Estimated Impact44
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Maximum-likelihood Fitting - Weibull Distribution] [Zix] [2020-04-17 19:31:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
5.44
18.78
35.97
53.71
65.86
78.08
89.77
94.08
96.07
97.42




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig 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 time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319136&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]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319136&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319136&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 Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center







ParameterEstimated ValueStandard Deviation
shape1.903454705493430.53634913686807
scale70.66009052822212.1765344971951

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
shape & 1.90345470549343 & 0.53634913686807 \tabularnewline
scale & 70.660090528222 & 12.1765344971951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319136&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]shape[/C][C]1.90345470549343[/C][C]0.53634913686807[/C][/ROW]
[ROW][C]scale[/C][C]70.660090528222[/C][C]12.1765344971951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319136&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319136&T=1

As an alternative you can also use a QR Code:  

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

ParameterEstimated ValueStandard Deviation
shape1.903454705493430.53634913686807
scale70.66009052822212.1765344971951



Parameters (Session):
par1 = 1 ; par2 = 100 ;
Parameters (R input):
par1 = 1 ; par2 = 100 ;
R code (references can be found in the software module):
par2 <- '90'
par1 <- '1'
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')