How to estimate the parameter for many variable?
Dear Rui and Jim, Thank you very much for your feedback. Yes, now I get the output. And after this I will use this output as the marginal distribution to continue the analysis on spatial extremes. Thanks again. See you later.
On Fri, 9 Jul 2021 at 17:18, Rui Barradas <ruipbarradas at sapo.pt> wrote:
Hello,
With the condition for the location it can be estimated like the following.
fit_list2 <- gev_fit_list <- lapply(Ozone_weekly2, gev.fit, ydat = ti,
mul = c(2, 3), show = FALSE)
mle_params2 <- t(sapply(fit_list2, '[[', 'mle'))
# assign column names
colnames(mle_params2) <- c("location", "scale", "shape", "mul2", "mul3")
head(mle_params2)
Hope this helps,
Rui Barradas
?s 09:53 de 09/07/21, SITI AISYAH ZAKARIA escreveu:
Dear Rui ang Jim,
Thank you very much.
Thank you Rui Barradas, I already tried using your coding and I'm
grateful I got the answer.
ok now, I have some condition on the location parameter which is cyclic
condition.
So, I will add another 2 variables for the column.
and the condition for location is this one.
ti[,1] = seq(1, 888, 1)
ti[,2]=sin(2*pi*(ti[,1])/52)
ti[,3]=cos(2*pi*(ti[,1])/52)
fit0<-gev.fit(x[,i], ydat = ti, mul=c(2, 3))
Thank you again.
On Fri, 9 Jul 2021 at 14:38, Rui Barradas <ruipbarradas at sapo.pt
<mailto:ruipbarradas at sapo.pt>> wrote:
Hello,
The following lapply one-liner fits a GEV to each column vector,
there
is no need for the double for loop. There's also no need to create a
data set x.
library(ismev)
library(mgcv)
library(EnvStats)
Ozone_weekly2 <- read.table("~/tmp/Ozone_weekly2.txt", header = TRUE)
# fit a GEV to each column
gev_fit_list <- lapply(Ozone_weekly2, gev.fit, show = FALSE)
# extract the parameters MLE estimates
mle_params <- t(sapply(gev_fit_list, '[[', 'mle'))
# assign column names
colnames(mle_params) <- c("location", "scale", "shape")
# see first few rows
head(mle_params)
The OP doesn't ask for plots but, here they go.
y_vals <- function(x, params){
loc <- params[1]
scale <- params[2]
shape <- params[3]
EnvStats::dgevd(x, loc, scale, shape)
}
plot_fit <- function(data, vec, verbose = FALSE){
fit <- gev.fit(data[[vec]], show = verbose)
x <- sort(data[[vec]])
hist(x, freq = FALSE)
lines(x, y_vals(x, params = fit$mle))
}
# seems a good fit
plot_fit(Ozone_weekly2, 1) # column number
plot_fit(Ozone_weekly2, "CA01") # col name, equivalent
# the data seems gaussian, not a good fit
plot_fit(Ozone_weekly2, 4) # column number
plot_fit(Ozone_weekly2, "CA08") # col name, equivalent
Hope this helps,
Rui Barradas
?s 00:59 de 09/07/21, SITI AISYAH ZAKARIA escreveu:
> Dear all,
>
> Thank you very much for the feedback.
>
> Sorry for the lack of information about this problem.
>
> Here, I explain again.
>
> I use this package to run my coding.
>
> library(ismev)
> library(mgcv)
> library(nlme)
>
> The purpose of this is I want to get the value of parameter
estimation
> using MLE by applying the GEV distribution.
>
> x <- data.matrix(Ozone_weekly2) x refers to
my data
> that consists of 19 variables. I will attach the data together.
> x
> head(gev.fit)[1:4]
> ti = matrix(ncol = 3, nrow = 888)
> ti[,1] = seq(1, 888, 1)
> ti[,2]=sin(2*pi*(ti[,1])/52)
> ti[,3]=cos(2*pi*(ti[,1])/52)
>
> /for(i in 1:nrow(x))
> + { for(j in 1:ncol(x)) the problem
in
> here, i don't no to create the coding. i target my output will
come out
> in matrix that
> + {x[i,j] = 1}} show
the
> parameter estimation for 19 variable which have 19 row and 3
column/
> /
row --
> refer to variable (station) ; column -- refer to parameter
estimation
> for GEV distribution
>
> /thank you.
>
> On Thu, 8 Jul 2021 at 18:40, Rui Barradas <ruipbarradas at sapo.pt
<mailto:ruipbarradas at sapo.pt>
> <mailto:ruipbarradas at sapo.pt <mailto:ruipbarradas at sapo.pt>>>
wrote:
>
> Hello,
>
> Also, in the code
>
> x <- data.matrix(Ozone_weekly)
>
> [...omited...]
>
> for(i in 1:nrow(x))
> + { for(j in 1:ncol(x))
> + {x[i,j] = 1}}
>
> not only you rewrite x but the double for loop is equivalent
to
>
>
> x[] <- 1
>
>
> courtesy R's vectorised behavior. (The square parenthesis are
needed to
> keep the dimensions, the matrix form.)
> And, I'm not sure but isn't
>
> head(gev.fit)[1:4]
>
> equivalent to
>
> head(gev.fit, n = 4)
>
> ?
>
> Like Jim says, we need more information, can you post
Ozone_weekly2 and
> the code that produced gev.fit? But in the mean time you can
revise
> your
> code.
>
> Hope this helps,
>
> Rui Barradas
>
>
> ?s 11:08 de 08/07/21, Jim Lemon escreveu:
> > Hi Siti,
> > I think we need a bit more information to respond
helpfully. I
> have no
> > idea what "Ozone_weekly2" is and Google is also ignorant.
> "gev.fit" is
> > also unknown. The name suggests that it is the output of
some
> > regression or similar. What function produced it, and from
what
> > library? "ti" is known as you have defined it. However, I
don't know
> > what you want to do with it. Finally, as this is a text
mailing list,
> > we don't get any highlighting, so the text to which you
refer cannot
> > be identified. I can see you have a problem, but cannot
offer any
> help
> > right now.
> >
> > Jim
> >
> > On Thu, Jul 8, 2021 at 12:06 AM SITI AISYAH ZAKARIA
> > <aisyahzakaria at unimap.edu.my
<mailto:aisyahzakaria at unimap.edu.my>
> <mailto:aisyahzakaria at unimap.edu.my
<mailto:aisyahzakaria at unimap.edu.my>>> wrote:
> >>
> >> Dear all,
> >>
> >> Can I ask something about programming in marginal
distribution
> for spatial
> >> extreme?
> >> I really stuck on my coding to obtain the parameter
estimation for
> >> univariate or marginal distribution for new model in
spatial
> extreme.
> >>
> >> I want to run my data in order to get the parameter
estimation
> value for 25
> >> stations in one table. But I really didn't get the idea
of the
> correct
> >> coding. Here I attached my coding
> >>
> >> x <- data.matrix(Ozone_weekly2)
> >> x
> >> head(gev.fit)[1:4]
> >> ti = matrix(ncol = 3, nrow = 888)
> >> ti[,1] = seq(1, 888, 1)
> >> ti[,2]=sin(2*pi*(ti[,1])/52)
> >> ti[,3]=cos(2*pi*(ti[,1])/52)
> >> for(i in 1:nrow(x))
> >> + { for(j in 1:ncol(x))
> >> + {x[i,j] = 1}}
> >>
> >> My problem is highlighted in red color.
> >> And if are not hesitate to all. Can someone share with me
the
> procedure,
> >> how can I map my data using spatial extreme.
> >> For example:
> >> After I finish my marginal distribution, what the next
> procedure. It is I
> >> need to get the spatial independent value.
> >>
> >> That's all
> >> Thank you.
> >>
> >> --
> >>
> >>
> >>
> >>
> >>
> >> "..Millions of trees are used to make papers, only to be
thrown away
> >> after a couple of minutes reading from them. Our planet
is at
> stake. Please
> >> be considerate. THINK TWICE BEFORE PRINTING THIS.."
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> >>
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>
>
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stake.
> Please be considerate. THINK TWICE BEFORE PRINTING THIS.."
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"..Millions of trees are used to make papers, only to be thrown away
after a couple of minutes reading from them. Our planet is at stake. Please
be considerate. THINK TWICE BEFORE PRINTING THIS.."
DISCLAIMER:?This email \ and any files transmitte...{{dropped:24}}