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jbaldwin
Obsidian | Level 7

My long term question is how to duplicate a particular analysis in R but first I'm trying to understand the output from SAS.  Here is the analysis code:

 

* Blackcap example from the corrHLfit function in the R spaMM library;

* Input data;
  data test;
    input latitude longitude migStatus means pos;
    cards;
36.1291   -5.3469       0.0 161.4000   1
15.0522  -23.6010       0.0 162.2857   2
43.5226    4.7189       1.0 162.6111   3
28.6742  -17.7859       0.5 163.4400   4
32.6743  -16.9105       0.5 162.6667   5
28.1600  -17.1980       0.5 163.6667   6
28.4772  -16.4479       0.5 163.4000   7
41.5335    2.2991       1.0 162.6818   8
40.6653   -4.0871       1.5 162.3667   9
48.2850   16.9086       2.0 162.8800  10
-0.1671   37.0154       2.5 163.5000  11
47.8160    8.9887       2.0 163.7049  12
41.7425   12.4035       2.0 163.1667  13
55.7559   37.6197       2.5 163.8333  14
run;

* Run analysis;
  proc mixed data=test method=ml;
    model migStatus = means / solution;
    repeated / type=sp(matern)(latitude longitude);
  run;

/* Output:
 
    Covariance Parameter
        Estimates

 Cov Parm       Estimate

 SP(MATERN)       1.0000
 Smoothness       0.5000
 Residual         0.5263

*/

I don't understand the "Cov Parm" estimates.  Even when I change the data, I get the same estimates for SP(MATERN) and Smoothness.  And given that the values certainly don't look like typical estimates (1.0 and 0.5), I'm wondering if these are just set to a default value and not fitted using maximum likelihood.  Can someone explain this output to me?

 

 

Thanks,

 

Jim

1 ACCEPTED SOLUTION

Accepted Solutions
SteveDenham
Jade | Level 19

I would never have guessed this, but try

 

proc mixed data=test method=reml;
  
    model migStatus = means / solution;
    repeated /subject=intercept type=sp(matern)(latitude longitude);
  run;

This seems to solve both problems of moving off the initial values and getting rid of the non-positive definite Hessian output warning.  A subject= option is definitely needed, it was just lucky that I looked at the anisotropic spherical example above the Matern examples in the documentation.

 

Steve Denham

 

View solution in original post

4 REPLIES 4
SteveDenham
Jade | Level 19

Some quick testing seems to indicate that since all of the observations are "lumped" into the same subject, there is no movement away from the initial values.  I tried the following:

data test;
    input latitude longitude migStatus means pos;
	id=mod(pos,7);
    cards;
36.1291   -5.3469       0.0 61.4000   1
15.0522  -23.6010       0.0 162.2857   2
43.5226    4.7189       1.0 162.6111   3
28.6742  -17.7859       0.5 163.4400   4
32.6743  -16.9105       0.5 162.6667   5
28.1600  -17.1980       0.5 163.6667   6
28.4772  -16.4479       0.5 163.4000   7
41.5335    2.2991       1.0 162.6818   8
40.6653   -4.0871       1.5 162.3667   9
48.2850   16.9086       2.0 162.8800  10
-0.1671   37.0154       2.5 163.5000  11
47.8160    8.9887       2.0 163.7049  12
41.7425   12.4035       2.0 163.1667  13
55.7559   37.6197       2.5 163.8333  14
run;

* Run analysis;
  proc mixed data=test method=reml;
  class id;
    model migStatus = means / solution;
    repeated /subject=id type=sp(matern)(latitude longitude);
  run;

This introduces subject to subject variability (note that it is all made up at this point).  The results from this were:

 

Covariance Parameter Estimates

Cov Parm

Subject

Estimate

SP(MATERN)

id

0.9001

Smoothness

id

0.5211

Residual

 

0.7293

So it appears you need a subject= option in the REPEATED statement to move the estimates away from the initial values.
 
It still results in a non-positive Hessian warning in the output, as well.
 
And I have no idea why this is now centered, or how to get back to normal fomatting
Spoiler
 
Steve Denham 
SteveDenham
Jade | Level 19

I would never have guessed this, but try

 

proc mixed data=test method=reml;
  
    model migStatus = means / solution;
    repeated /subject=intercept type=sp(matern)(latitude longitude);
  run;

This seems to solve both problems of moving off the initial values and getting rid of the non-positive definite Hessian output warning.  A subject= option is definitely needed, it was just lucky that I looked at the anisotropic spherical example above the Matern examples in the documentation.

 

Steve Denham

 

jbaldwin
Obsidian | Level 7

Excellent!  Thank you!  That makes everything match up between SAS and R.

 

For others who might have this issue I offer the following Rosetta Stone:

 

    SAS parameter = corrHLfit parameter

    SP(MATERN) = 1/rho

    Smoothness = nu

    Residual = lambda

 

SAS counts the fixed effects and the covariance terms as parameters in the construction of the AIC statistic whereas the corrHLfit function in R only considers the fixed effects (i.e., adding in twice the number of parameters).

 

Thanks again!

 

Jim

 

lvm
Rhodochrosite | Level 12 lvm
Rhodochrosite | Level 12

With a repeated statement: If you do not specify a subject, then each observation is considered a subject. Thus, there could be no correlation structure. If you specify subject=intercept, the entire dataset is considered to be one large subject, so that every observation could be correlated (depending on the correlation structure parameters).

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