- PROFILE parms [ / [ ALPHA= values ] [ options ] ]
where parms := pnam_1 pnam_2 ... pnam_n
values := list of alpha values in (0,1)
options := additional options
The PROFILE statement
When computing the profile points or likelihood profile confidence
intervals, PROC NLP assumes that a maximization of
the log likelihood function is desired.
Each point of the profile and each endpoint of the
confidence interval is computed by solving corresponding
nonlinear optimization problems.
- writes the (x,y) coordinates of profile points for
each of the listed parameters to the OUTEST= data set
- displays, or writes to the OUTEST= data set, the profile
likelihood confidence limits (PL CL) for the listed
parameters for the specified values.
If the approximate standard errors are available, the
corresponding Wald confidence limits can be computed.
The keyword PROFILE must be followed by the names of
parameters for which the profile or the PL CLs should be
If the parameter name list is empty, the
profiles and PL CLs for all parameters are computed.
Then, optionally, the alpha values follow.
The list of values may contain TO and BY keywords.
Each element must satisfy . The following
is an example:
profile l11-l15 u1-u5 c /
alpha= .9 to .1 by -.1 .09 to .01 by -.01;
Duplicate values or values outside (0,1) are
automatically eliminated from the list.
A number of additional options can be specified.
- specifies the factor relating the discrepancy
function to the quantile. The default
value is r=2.
- FORCHI= F | CHI
defines the scale for the y values written to
the OUTEST= data set. For FORCHI=F, the y values are scaled
to the values of the log likelihood function ;for FORCHI=CHI, the y values are scaled so that
. The default value is FORCHI=F.
- specifies a factor of the Wald confidence limit
(or an approximation of it if standard errors are not computed)
defining an upper bound for the search for confidence limits.
In general, the range of x values in the profile graph is
between r=1 and r=2 of the size of the corresponding
Wald interval. For many examples, the quantiles
corresponding to small values define a y level
, which is too far away
from to be reached by y(x) for x within the
range of twice the Wald confidence limit. The search for an
intersection with such a y level at a practically infinite
value of x can be computationally expensive.
value for r can speed up computation time by restricting
the search for confidence limits to a region closer to .The default value of r=1000 practically disables the
- specifies that the complete set
of parameter estimates rather than only for each
confidence limit is written to the OUTEST= data set. This
output can be helpful for further analyses on how small changes
in affect the changes in the .
For some applications, it may be computationally less expensive
to compute the PL confidence limits for a few parameters than
to compute the approximate covariance matrix of many parameters,
whichis the basis for the Wald confidence limits. However, the
computation of the profile of the discrepancy function and the
corresponding CLs in general will be much more time consuming
than that of the Wald CLs.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.