7; are the
quantiles of a GEV distribution fitted to 'zz_gev' (you can verify that
'CI_gev$Qhat' is the same as 'quagev(Fx,pelgev(samlmu(zz_gev)))').
For inference about 'max_data', 'GEV' is likely to be better.
For inference about 'zz_gev',
27;green') # add Gumbel fit
evdistq(qualn3, pelln3(samlmu(Prec), bound=0), col='blue') # lognormal
legend("topleft",c("Gumbel","lognormal"),lty=1,col=c("green","blue"))
J. R. M. Hosking
__
ggplot2 is "an implementation of the Grammar of Graphics".
It seems likely that whatever you were hoping to achieve with ggplot2
can also be done natively in SPSS.
J. R. M. Hosking
plugin requires R 14.0 (14.2 or 15 does not work).
When I install R it gives an warning:
library(
? Thanks.
[[alternative HTML version deleted]]
The help file for function evplot() in package lmom has an example that
does exactly what you ask.
J. R. M. Hosking
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rized version
of the 3-parameter lognormal distribution). Is this what you want?
If not, I don't know what else you want to know about "the
specification of the parameter (lmom)".
J. R. M. Hosking
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lower bound,
by fitting a 3-parameter gamma distribution ("Pearson type III" in
the terminology of package lmom), gives a visually much better fit:
evdistq(quape3, pelpe3(samlmu(x)), col='blue')
J. R. M. Hosking
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t>0.
Reference:
J.R.M. Hosking (2007). Some theory and practical uses of trimmed
L-moments. Journal of Statistical Planning and Inference, 137,
3024-3039.
J. R. M. Hosking
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; 102881" " 10"
Looks fine to me. Now I can use as.numeric() to convert to numbers (leading
and lagging spaces should not be a problem):
as.numeric(gsub("[$,]", "", as.character(moose$V3)))
[1] NA NA NA NA NA NA NA NA
tained, reproducible
example I will see what I can do.
J. R. M. Hosking
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal
e intended to be a way of
getting as close as possible to what confidence intervals would be
(or ought to be, given consideration (b) above).
J. R. M. Hosking
Thank you in advance,
Tonja
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an(x)
p.mean <- which.min((x.sort - x.mean)^2) / x.length
Another user pointed out a function suited for this purpose, findInterval()
p.mean <- findInterval(x.mean, xsort) / x.length
Thanks for your help,
David
No need to sort:
x <- runif(1000)
p.mean <- mean(x <= mean(x))
J. R.
rst define the quantile function of the LPE3, then use it ...
quaLPE3 <- function(f, para) exp(quape3(f, para))
evdistq(quaLPE3, parLPE3, col='magenta')
(and thank you for providing such clear sample code)
J. R. M. Hosking
__
R-help
is to sapply as ? is to tapply )
Thanks for your help.
coef(lm(data ~ -1 + as.factor(groups), weights=weights))
Not the fastest, but IMO more comprehensible than the constructions
involving anonymous functions.
J. R. M. Hosking
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non-finite value supplied by optim
Any help or suggestions are most welcomed
Use function pelwei() in package lmom.
J. R. M. Hosking
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PLEASE do read the posting guide ht
package lmom.
J. R. M. Hosking
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
rtificial 1-site region whose
# frequency distribution is the one fitted to the actual data
sim <- regsimq(rfit$qfunc, nrec = rdat$n,
f = 1 - 1 / c(2,5,10,25,50,100))
# Compute error bounds for quantiles of the site's
# frequency distribution
sitequan
parameter Log Logistic distribution to this data?
Thanking in advance
Maithili
Log logistic is, after reparametrization, a subset of the generalized
logistic distribution used in package lmom, so function pelglo in that
package, together with a bit of algebra, should get you there.
J. R. M
g a normal distribution is grossly
inappropriate, they can be very far apart. Your data, which have
sample skewness 22 and sample L-skewness 0.93, fall into this category.
J. R. M. Hosking
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o clean up:
library(RDCOMClient)
xl <- COMCreate("Excel.Application")
wk <- xl$Workbooks()
sh<-wk$Open(normalizePath("sample_file.xls"))$Sheets()$Count()
wk$Close()
xl$Quit()
rm(sh)
rm(wk)
rm(xl)
gc()
J. R. M. Hosking
___
394 1.00195 1.14976 0.80008 1.11947 1.09484 0.81494 0.68696
+ 0.82364 0.84390 0.71402 0.80293 1.02873
+ "))
Read 39 items
>
> y <- (x-mean(x))/sd(x)
>
> library(lmom)
> pelln3(samlmu(y))
zeta mu sigma
-1.5362134 0.2554
(amounts),bound=1)
xialphak
1.0072.993343-0.838561
pelwei offers similar options for the Weibull distribution.
J. R. M. Hosking
Please help me.
Regards and thanking in advance
Maithili
e 3-parameter gamma.
For the relationship between the two distributions, see the help for
function cdfpe3 in package lmom.
J. R. M. Hosking
Please guide
Regards
Maithili
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n(x,para) exp(-exp(-(x - para[1])/para[2]))
J. R. M. Hosking
Maithili Shiva wrote:
Dera R Helpers,
I am re-posting my query.
Please guide me.
Maithili
--- On Fri, 3/13/09, Maithili Shiva wrote:
I am trying to fit the Gumbel distribution to a data. I am
using lmom package. I am gett
Maithili Shiva wrote:
Dear R helpers,
How do you estimate the (Location, Scale, Shape) parameters of Generalized
Extreme Value distribution using R?
...
Package lmom, function pelgev.
J. R. M. Hosking
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https
> I have enclosed the print operation in braces to avoid possible problems
> with it.
>
> Your advice?
>
> Tom Jones
What Messrs. Schwartz and Olshansky told you is valid, but will not cure
syntax errors.
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