On Fri, Sep 21, 2012 at 3:45 PM, David Winsemius [via R]
<ml-node+s789695n4643876h79@n4.nabble.com> wrote:
>
> 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?

The object is over space and time.  It is model output from the
atmospheric science modeling tool WRF
(http://www.mmm.ucar.edu/wrf/users/)

The data is constructed in 3-dimensions where the wind speed has the
dimensions (time, lat lat).
There are 67 times with 106 and 193 points lat and lon at each time.
This is why I was trying to index it to ensure I am able to perform
the task at each grid point.

>
>>
>> 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".

I have been consulting with a professor in the Statistics department
who is an expert in extreme value analysis.  What I am doing is
"safe", I just can't seem to get it working correctly.  I will also
run the procedure by her before I publish any research since I do not
have a statistics background.
>
>>
>> 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
>

I apologize, I was under the impression from previous help that the
as.matrix() was the correct way to do this.  I frequently use FORTRAN,
NCL, MATLAB etc.  R is still relatively new to me and most of what I
have worked with is 1-D data  in this program.

When I try the Y = cbind(time,lat,lon) I receive the following error:
Warning message:
In cbind(time, lat, lon) :
  number of rows of result is not a multiple of vector length (arg 1)

>>
>> 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

OK-- in other languages I have used (non-statistical), I have looped
through to perform operations on each individual point.  That is what
i am trying to do here.  There are numerous times at each lat/lon
poing.  The speed will vary greatly point-by-point as the terrain and
physical properties driving the speed are different by location.


> 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) )
>
This gives the following error:
Error in UseMethod("predict") :
  no applicable method for 'predict' applied to an object of class "gpd.fit"

>    ? 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.
>
My mistake.

> 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.
>

I have worked quite frequently with 1D data, and have studied books
that focus on the 1D data, but have yet to find helpful books on R
focusing on 3D data.  I have the Steward Coles book on Extreme Value
modeling, which I reference frequently.  If there is a book that
focuses on using R over time,lat,lon or other multi-dimensional data,
I would appreciate knowing and will immediately buy it.

I am not sure what else I can "study."  I have also read the posting
guide.  I know what I am trying to do statistically, and as mentioned
before, have gotten feedback from a statistician, it is just
performing the task that i do not understand.

Thanks,

Michelle

> --
> David Winsemius, MD
> Alameda, CA, USA
>
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-- 
Michelle Cipullo




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