On Sep 21, 2012, at 7:17 AM, MichelleNCSU wrote:
> Hello,
>
> First of all, let me apologize that my statistics background is modest at
> best.
>
> I am doing some extreme value analysis on model output (WRF) which have the
> following dimensions:
>
> speed(time,lat,lon)
How is this object structured? Are there multiple time layers where speed is
measured at lat-lon points are successive times?
>
> I am trying to fit the GPD (gpd.fit) to each point (time,lat,lon) to get a
> return level plot with values at each grid point. (Map with return level by
> location.)
I'm not really sure conducting extreme value analysis is a safe procedure when
your stats background is "modest at best".
>
> Here is some code I tried, following similar structure to languages I'm more
> familiar with, but it isn't working.
>
> Y = as.matrix(time,lat,lon)
This suggests to me that you come from a different computing universe where the
as.matrix() function allows multiple objects (presumably vectors) to be placed
"side-by-side" and have a matrix object returned. (In this computing universe
that role is played by the cbind function.) Perhaps:
Y = cbind(time,lat,lon) # "c" standing for column
>
> for (t in 1:67)
> + for (j in 1:106)
> + for (i in 1:193)
> + fit(t,j,i)<-gpd.fit(speed(t,j,i), threshold=17,ydat = Y)
>
> I receive errors at this point, and cant figure out how to get individual
> fits at each grid point.
It's a puzzle to me why you think that passing a single point to a regression
function will allow any solution. I did a "??" search and find that there is
`gpd.fit` function in the 'ismev' package, but it (like all other regression
functions of which I am aware) appears to take full data objects rather than
single points. You would not need to use for-loops to pass the object. Perhaps:
fit <-gpd.fit(Y, threshold=17,ydat = Y)
# Not sure where "speed" entered the picture.
# as noted before there is ambiguity in the problem statement
# Or did you do a prior differentiation operation?
# perhaps a 1/ first difference on time?
# perhaps ... if gpd.fit follows the usual R conventions
# return the first point
i=1,j=1,k=1
pred.ijk <- predict(fit, data,frame(time=i, lat,=j,lon=k) )
? expand.grid # to cover a range
Your placement of a functional form on the LHS of a formula also suggests
recent migration from another statistical universe where assignment is done
into functions, i.e. forms using parentheses, rather than the
extraction/insertion operators: "[" and "[<-", to put values into structures
with dimensions, like matrices and dataframes.
I think you really need to do some more self-study of the introductory R
material rather than making wild guesses at what "might" work based on
experience with Python, Perl, or (less likely in view of that effort to assign
into a function) Matlab.
You should also read the Posting Guide.
--
David Winsemius, MD
Alameda, CA, USA
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