The ESTIMATE statement computes estimates of functions of the parameters.

The ESTIMATE statement refers to the parameters estimated by the associated FIT statement (that is, to either the preceding FIT statement or, in the absence of a preceding FIT statement, to the following FIT statement). You can use any number of ESTIMATE statements.

Let denote the function of parameters that needs to be estimated. Let denote the unconstrained estimate of the parameter of interest, . Let be the estimate of the covariance matrix of . Denote

Then the standard error of the parameter function estimate is computed by obtaining the square root of . This is the same as the variance needed for a Wald type test statistic with null hypothesis .

If the expression of the function in the ESTIMATE statement includes a variable, then the value used in computing the function estimate is the last observation of the variable in the DATA= data set.

If you specify options on the ESTIMATE statement, a comma is required before the "/" character that separates the test expressions from the options, since the "/" character can also be used within test expressions to indicate division. Each item is written as an optional name followed by an expression,

< "name" > expression

where "name" is a string used to identify the estimate in the printed output and in the OUTEST= data set.

Expressions can be composed of parameter names, arithmetic operators, functions, and constants. Comparison operators (such as = or <) and logical operators (such as &) cannot be used in ESTIMATE statement expressions. Parameters named in ESTIMATE expressions must be among the parameters estimated by the associated FIT statement.

You can use the following options in the ESTIMATE statement:

The following statements are an example of the use of the ESTIMATE statement in a segmented model and produce the output shown in Figure 19.20:

data a;
   input y x @@;
datalines;
   .46 1  .47  2 .57  3 .61  4 .62  5 .68  6 .69  7
   .78 8  .70  9 .74 10 .77 11 .78 12 .74 13 .80 13
   .80 15 .78 16
;

title 'Segmented Model -- Quadratic with Plateau';
proc model data=a;

   x0 = -.5 * b / c;

   if x < x0 then y = a + b*x + c*x*x;
   else           y = a + b*x0 + c*x0*x0;

   fit y start=( a .45 b .5 c -.0025 );

   estimate 'Join point' x0 ,
            'plateau' a + b*x0 + c*x0**2 ;
run;