The HPNLMOD Procedure

MODEL Statement

  • MODEL dependent-variable ~ distribution;

The MODEL statement is the mechanism for either using a distribution specification to specify the distribution of the data or using the RESIDUAL distribution to specify a predicted value. You must specify a single dependent variable from the input data set, a tilde (~), and then a distribution along with its parameters. You can specify the following values for distribution:

RESIDUALleft-parenthesis sans-serif-italic m right-parenthesis or LSleft-parenthesis sans-serif-italic m right-parenthesis

specifies no particular distribution. Instead the sum of squares of the differences between sans-serif-italic m and the dependent variable is minimized.

NORMALleft-parenthesis sans-serif-italic m comma sans-serif-italic v right-parenthesis

specifies a normal (Gaussian) distribution that has mean sans-serif-italic m and variance sans-serif-italic v.

BINARYleft-parenthesis sans-serif-italic p right-parenthesis

specifies a binary (Bernoulli) distribution that has probability sans-serif-italic p.

BINOMIALleft-parenthesis sans-serif-italic n comma sans-serif-italic p right-parenthesis

specifies a binomial distribution that has count sans-serif-italic n and probability sans-serif-italic p.

GAMMAleft-parenthesis sans-serif-italic a comma sans-serif-italic b right-parenthesis

specifies a gamma distribution that has shape sans-serif-italic a and scale sans-serif-italic b.

NEGBINleft-parenthesis sans-serif-italic n comma sans-serif-italic p right-parenthesis

specifies a negative binomial distribution that has count sans-serif-italic n and probability sans-serif-italic p.

POISSONleft-parenthesis sans-serif-italic m right-parenthesis

specifies a Poisson distribution that has mean sans-serif-italic m.

GENERALleft-parenthesis sans-serif-italic ll right-parenthesis

specifies a general log-likelihood function that you construct by using SAS programming statements.

The MODEL statement must follow any SAS programming statements that you specify for computing parameters of the preceding distributions. For information about the built-in log-likelihood functions, see the section Built-In Log-Likelihood Functions .

Last updated: December 09, 2022