Modeling and Multivariate Methods - SAS

194 Fitting Generalized Linear Models Chapter 6
Platform Commands
Since the Prob>Chisq is less than 0.05, we have evidence that there is a difference in satellite attraction
based on color.
Inverse Prediction is used to predict an X value, given specific values for Y and the other X variables. This
can be used to predict continuous variables only. For more details about Inverse Prediction, see “Inverse
Prediction” on page 70 in the “Fitting Standard Least Squares Models” chapter.
Covariance of Estimates produces a covariance matrix for all the effects in a model. The estimated
covariance matrix of the parameter estimator is given by
Σ = -H -1
where H is the Hessian (or second derivative) matrix evaluated using the parameter estimates on the last
iteration. Note that the dispersion parameter, whether estimated or specified, is incorporated into H. Rows
and columns corresponding to aliased parameters are not included in Σ.
Correlation of Estimates produces a correlation matrix for all the effects in a model. The correlation matrix
is the normalized covariance matrix. That is, if σ ij is an element of Σ, then the corresponding element of the
correlation matrix is σ ij /σ i σ j , whereσ i
= σ ii
Profiler brings up the Profiler for examining prediction traces for each X variable. Details on the profiler are
found in “The Profiler” on page 93 in the “Fitting Standard Least Squares Models” chapter.
Contour Profiler brings up an interactive contour profiler. Details are found in “Contour Profiler” on
page 577 in the “Visualizing, Optimizing, and Simulating Response Surfaces” chapter.
Surface Profiler brings up a 3-D surface profiler. Details of Surface Plots are found in the “Plotting
Surfaces” chapter on page 535.