PARMS Statement
- PARMS (value-list) ...< / options > ;
The PARMS statement specifies initial values for the covariance
parameters, or it requests a grid search over several values of
these parameters. You must specify the values in the order in which
they appear in the "Covariance Parameter Estimates" table.
The value-list specification can take any of several forms:
- m
- a single value
- m1, m2, ... , mn
- several values
- m to n
- a sequence where m equals the starting
value, n equals the ending value, and
the increment equals 1
- m to n by i
- a sequence where m equals the starting
value, n equals the ending value, and
the increment equals i
- m1, m2 to m3
- mixed values and sequences
You can use the PARMS statement to input known parameters.
Referring to the split-plot example (Example 41.1),
suppose the three variance components are known
to be 60, 20, and 6. The SAS code to fix the variance components at
these values is as follows:
proc mixed data=sp noprofile;
class Block A B;
model Y = A B A*B;
random Block A*Block;
parms (60) (20) (6) / noiter;
run;
The NOPROFILE option requests PROC MIXED to refrain from profiling
the residual variance parameter during its calculations, thereby
enabling its value to be held at 6 as specified in the PARMS
statement. The NOITER option prevents any Newton-Raphson iterations
so that the subsequent results are based on the given variance
components. You can also specify known parameters of G using
the GDATA= option in the RANDOM statement.
If you specify more than one set of initial values, PROC MIXED
performs a grid search of the likelihood surface and uses the best
point on the grid for subsequent analysis. Specifying a large
number of grid points can result in long computing times. The grid
search feature is also useful for exploring the likelihood surface.
See Example 41.3.
The results from the PARMS statement are the values of the
parameters on the specified grid (denoted by CovP1 -CovP
n), the residual variance (possibly estimated) for models with a
residual variance parameter, and various functions of the
likelihood.
For ODS purposes, the label of the "Parameter Search" table is
"ParmSearch."
You can specify the following options in the PARMS statement
after a slash (/).
- HOLD=value-list
-
- EQCONS=value-list
-
specifies which parameter values PROC MIXED should hold to equal the
specified values. For example, the statement
parms (5) (3) (2) (3) / hold=1,3;
constrains the first and third covariance parameters to equal 5 and
2, respectively.
- LOGDETH
-
evaluates the log determinant of the Hessian matrix for each point specified
in the PARMS statement. A Log Det H column is added to the
"Parameter Search" table.
- LOWERB=value-list
-
enables you to specify lower boundary constraints on the covariance
parameters. The value-list specification is a list of numbers or
missing values (.) separated by commas. You must list the numbers
in the order that PROC MIXED uses for the covariance parameters, and each
number corresponds to the lower boundary constraint. A missing
value instructs PROC MIXED to use its default constraint, and if you
do not specify numbers for all of the covariance parameters, PROC
MIXED assumes the remaining ones are missing.
An example for which this option is useful is when you want
to constrain the G matrix to be positive definite in order
to avoid the more computationally intensive algorithms required
when G becomes singular. The corresponding code for a random
coefficients model is as follows:
proc mixed;
class person;
model y = time;
random int time / type=fa0(2) sub=person;
parms / lowerb=1e-4,.,1e-4;
run;
Here the FA0(2) structure is used in order to specify a Cholesky
root parameterization for the 2 ×2 unstructured blocks in
G. This parameterization ensures that the G matrix
is nonnegative definite, and the PARMS statement then ensures that
it is positive definite by constraining the two diagonal terms to be
greater than or equal to 1E-4.
- NOBOUND
-
requests the removal of boundary constraints on covariance
parameters. For example, variance components have a default lower
boundary constraint of 0, and the NOBOUND option allows their
estimates to be negative.
- NOITER
-
requests that no Newton-Raphson iterations be performed and that
PROC MIXED use the best value from the grid search to perform
inferences. By default, iterations begin at the best value from the
PARMS grid search.
- NOPROFILE
-
specifies a different computational method for the residual variance
during the grid search. By default, PROC MIXED estimates this
parameter using the profile likelihood when appropriate, and this
estimate is displayed in the Variance column of the
"Parameter Search" table. The NOPROFILE option suppresses the
profiling and uses the actual value of the specified variance in the
likelihood calculations.
- OLS
-
requests starting values corresponding to the usual general linear model.
Specifically, all variances and covariances are set to zero except for the
residual variance, which is set equal to its ordinary least-squares (OLS)
estimate. This option is useful when the default MIVQUE0 procedure produces
poor starting values for the optimization process.
- PARMSDATA=SAS-data-set
- PDATA=SAS-data-set
-
reads in covariance parameter values from a SAS data set.. The data
set should contain the EST or COVP1 -COVPn variables.
(Note: In releases prior to 6.12, the data set should contain EST or
COL1 -COLn variables.)
- RATIOS
-
indicates that ratios with the residual variance are specified instead
of the covariance parameters themselves. The default is to use the
individual covariance parameters.
- UPPERB=value-list
-
enables you to specify upper boundary constraints on the covariance
parameters. The value-list specification is a list of numbers or
missing values (.) separated by commas. You must list the numbers
in the order that PROC MIXED uses for the covariance parameters, and each
number corresponds to the upper boundary constraint. A missing
value instructs PROC MIXED to use its default constraint, and if you
do not specify numbers for all of the covariance parameters, PROC
MIXED assumes that the remaining ones are missing.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.