This section describes the options that you can use to facilitate entering effects into your model. Examples of how these options can be used to obtain specific types of models are given in Examples of Model Specifications and Their Model Fits.
Note: To remove an effect from the Construct Model Effects list, double-click the effect, or select it and click Remove or press the Backspace or Delete key.
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.
Suppose that a product coating requires a dye to be applied. Both Dye pH and Dye Concentration are suspected to have an effect on the coating color. To understand their effects, you design an experiment where Dye pH and Dye Concentration are each set at a high and low level. It is possible that the effect of Dye pH on the color is more pronounced at the high level of Dye Concentration than at its low level. This is known as an interaction. To model this possible interaction, you include the crossed term, Dye pH * Dye Concentration, in the Construct Model Effects list. This enables JMP to test for an interaction.
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. Nested terms must be categorical.
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.
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Click Nest. This converts B to the effect B[A].
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Click Nest. The converts C to the effect C[A, B].
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Creates all main effects and interactions for the columns selected in the Select Columns list. These are entered in an order that is based on the order in which the main effects are listed in the Select Columns list. For an alternate ordering, see Factorial Sorted, in this table.
Creates all main effects, but only interactions up to a specified degree (order). Specify the degree in the Degree box beneath the Macros button.
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 Response Surface Designs in 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 Response Surface Designs in the Design of Experiments Guide.
Creates main effects and polynomial terms up to a specified degree. Specify the degree in the Degree box beneath the Macros button.
Scheffé cubic terms are also included if you enter a 3 in the Degree box and then select the Mixture Response Surface macro command.
Grouped RegressorsAssigns the Random attribute to an effect. For details about random effects, see Specifying Random Effects and Fitting Method in Standard Least Squares Report and Options.
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.
Assigns the Knotted attribute to a continuous main effect. This implicitly adds cubic splines for the effect to the model specification. See Knotted Spline Effect.
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).
Note: You can also transform a column by right-clicking it in the Select Columns list and selecting Transform. A reference to the transformed column appears in the Select Columns list. You can then use the column in the Fit Model window as you would any data table column. See the Transform Columns in the Using JMP book for details.
Calculates the inverse of the logistic function for the selected column (where p is in the range of 0 to 1):
Calculates the logit as a percent for the selected column (where pct is a percent in the range of 0 to 100):
