You can launch the Fit Model platform by selecting Analyze > Fit Model. Fit Model Launch Window shows an example of the launch window for the sample data table.
Fit Model Launch Window
Note: When you select Analyze > Fit Model in a data table that has a script named Model (or model), the launch window is automatically filled in based on the script.
Elements Common to Most Personalities describes the elements of the Fit Model launch window that are common to most personalities.
Specifies the fitting methodology. See Fit Model Launch Window Elements. Different options appear depending on the personality that you select.
Frequency variables, entered in the Freq text box, are an option for most Fit Model personalities. In general, a frequency is interpreted as follows. Suppose that a row has a frequency f. Then the computed results are identical to those for a data table containing f copies of that row, each having a frequency of one.
Creates interaction or polynomial effects. Select two or more variables in the Select Columns list and click Cross. Or, select one or more variables in the Select Columns list and one or more effects in the Construct Model Effects list and click Cross.
See Statistical Details, for a discussion of how crossed effects are parameterized and coded.
Creates nested effects. If the levels of one effect (B) occur only within a single level of another effect (A), then B is said to be nested within A. The notation B[A], which is read as “B nested within A,” is typically used. Note that nesting defines a hierarchical relationship. A is called the outside effect and B is called the inside effect.
Note: The nesting terms must be specified in order from outer to inner. For example, if B is nested within A, and C is nested within B, then the model is specified as: A, B[A], C[B,A] (or, equivalently, A, B[A], C[A,B]). You can construct effects that combine up to ten columns as crossed and nested.
Click Nest. This converts B to the effect B[A].
Click Nest. The converts C to the effect C[A, B].
Creates the same set of effects as the Full Factorial option but lists them in order of degree. All main effects are listed first, followed by all two-way interactions, then all three-way interactions, and so on.
Creates main effects, two-way interactions, and quadratic terms. The selected main effects are given the response surface attribute, denoted RS. When the RS attribute is applied to main effects and the Standard Least Squares personality is selected, a Response Surface report is provided. This report gives information about critical values and the shape of the response surface.
See also Response Surface Effect in Attributes and the Design of Experiments Guide.
Creates main effects and two-way interactions. Main effects have the response surface (RS) and mixture (Mixture) attributes. In the Standard Least Squares personality, the Mixture attribute causes a mixture model to be fit. The RS attribute creates a Response Surface report that is specific to mixture models.
See also Mixture Effect in Attributes and the Design of Experiments Guide.
Scheffé cubic terms are also included if you enter a 3 in the Degree box and then select the Mixture Response Surface macro command.
This produces the same results as does the GLIMMIX procedure in SAS/STAT if the following options are included in the RANDOM statement: TYPE=RSMOOTH, KNOTMETHOD=DATA.
Descriptions of the Attributes Options describes attributes that you can assign to an effect selected in the Construct Model Effects list.
Assigns the RS attribute to an effect. Note that the relevant model terms must be included in the Construct Model Effects list. The Response Surface option in the Macros list automatically generates these terms and assigns the RS attribute to the main effects. To obtain the Response Surface report, interaction and polynomial terms do not need to have the RS attribute assigned to them. You need only assign this attribute to main effects.
To include an effect in models for both the mean and variance of the response, you must specify the effect twice. In the tabbed interface, it must appear on both the Mean Effects and Variance Effects tabs. Otherwise, you can enter it twice on the Mean Effects tab, once without the LogVariance Effect attribute and once with the LogVariance Effect attribute.
Knotted splines are used to fit a response Y using a flexible function of a predictor. Consider the single predictor X. When the Knotted Spline Effect is assigned to X, and k knots are specified, then k-2 additional effects are implicitly added to the set of predictors. Each of these effects is a piecewise cubic polynomial spline whose segments are defined by the knots. See Stone and Koo (1985).
Description of Fitting Personalities briefly describes each personality and provides references to the chapters that describe each in detail.
You can also launch this personality by selecting Analyze > Reliability and Survival > Fit Proportional Hazards.
See the Reliability and Survival Methods book.
You can also launch this personality by selecting Analyze > Reliability and Survival > Fit Parametric Survival.
See the Reliability and Survival Methods book.
Fits models to one or more Ys using latent factors. This permits models to be fit when explanatory variables (Xs) are highly correlated, or when there are more Xs than observations.
You can also launch a partial least squares analysis by selecting Analyze > Multivariate Methods > Partial Least Squares.
See the Multivariate Methods book.
Note: This personality only allows continuous responses. Response Screening for individual factors is also available by selecting Analyze > Modeling > Response Screening. This platform supports categorical responses, and also provides equivalence tests and tests of practical significance.
See the Specialized Models book.