Re: Calculation for pattern correlation

You have set opt=1. From the 'pattern_cor' documentation:
http://www.ncl.ucar.edu/Document/Functions/Contributed/pattern_cor.shtml

opt

opt=0 means compute the centered pattern correlation;
opt=1 means compute the uncentered pattern correlation.
=====
Hence the NCL 'pattern_cor' function which, under the hood'
uses the 'pattern_cor2' function, is going to calculate the
*uncentered* pattern correlation because you set opt=1

Your code is calculating the *centered* pattern correlation.

There is a difference.
=====
In your current code, change the last argument to 0.

=====

The best debugging is via print statements.

I have attached a test 'pattern_cor2' function that has print
statements. You can print the equivalent numbers from your computations.

=====

load "./tst_pattern_cor2.ncl"

r_x = tst_pattern_cor2(X,O,1.0,0)
r_y = tst_pattern_cor2(Y,O,1.0,0)

=====
Finally, you are use fortran-77 'do' [or C 'for'] style.
There is nothing wrong with this BUT in any interpreted language
using multiple do loops can be slow. NCL is an array language, like
fortran-90. Array operatons are much quicker but, at times' it can take
some thinking to make sure it is done correctly.

Another side point, if your X,Y,O arrays had missing values
(_FillValue), you would have to add and if statement

     if (.not.ismissing(X(i,j)) .or. .not.ismissing(Y(i,j)) .or. \
         .not.ismissing(O(i,j)) ) then
              :
              :
     end if

Good luck

On 10/18/11 10:06 PM, Ivanna Mo wrote:
> Hi,
> I use the built-in function pattern_cor to calculate the pattern
> correlation coefficient of my result. I find that the r is strange and
> different from the result I compute myself. Here is the script that I use:
>
> r_x = pattern_cor(X,O,1.0,1) ; r_x = 0.9998
> r_y = pattern_cor(Y,O,1.0,1) ; r_y= 0.99996
>
>
> Avg_X = avg(X)
> Avg_Y = avg(Y)
> Avg_O = avg(O)
>
> Sum_X = 0.0
> Sum_y = 0.0
> Sum_dev_X = 0.0
> Sum_dev_Y = 0.0
> Sum_dev_O = 0.0
>
> do i=0, 67
> do j=0, 67
>
> Sum_x = Sum_x + (X(i,j) - Avg_X)*(O(i,j) - Avg_O)
> Sum_y = Sum_y + (Y(i,j) - Avg_Y)*(O(i,j) - Avg_O)
> Sum_dev_X = Sum_dev_X + (X(i,j) - Avg_X)*(X(i,j) - Avg_X)
> Sum_dev_Y = Sum_dev_Y +(Y(i,j) - Avg_Y)*(Y(i,j) - Avg_Y)
> Sum_dev_O = Sum_dev_O +(O(i,j) - Avg_O)*(O(i,j) - Avg_O)
>
> end do
> end do
>
> R_x = Sum_x/(sqrt(Sum_dev_X)*sqrt(Sum_dev_O)) ; R_x = 0.70
>
> R_y = Sum_y/(sqrt(Sum_dev_Y)*sqrt(Sum_dev_O)) ; R_y = 0.98
>
>
>
>
>
>
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