BUILT-IN MATHEMATICA SYMBOL

DesignMatrix

constructs the design matrix for the linear model

.

MORE INFORMATION

DesignMatrix assumes data of the form . »

With data in the form , the number of coordinates , , ... should equal the number of variables .

The design matrix m is formed from the values of basis functions at data points in the form

DesignMatrix takes the following options:

IncludeConstantBasis	True	whether to include a constant basis function
NominalVariables	None	variables considered as nominal or categorical
WorkingPrecision	Automatic	precision used in internal computations

With the setting IncludeConstantBasis->False, the design matrix for a model of form is constructed. »

EXAMPLES

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Basic Examples (3)

Design matrix for a linear model:

Add a quadratic term:

Leave out the constant term:

Design matrix with two predictor variables:

Include a product term:

Assume predictor values

,

, ...:

Design matrix for a linear model:

In[1]:=

Out[1]=

In[2]:=

Out[2]//MatrixForm=

Add a quadratic term:

In[3]:=

Out[3]//MatrixForm=

Leave out the constant term:

In[4]:=

Out[4]//MatrixForm=

Design matrix with two predictor variables:

In[1]:=

Out[1]=

In[2]:=

Out[2]//MatrixForm=

Include a product term:

In[3]:=

Out[3]//MatrixForm=

Assume predictor values

,

, ...:

In[1]:=

Out[1]//MatrixForm=

Scope (2)

Use any numeric functions of the predictors:

Get the design matrix using exact arithmetic:

Use machine arithmetic:

Use arbitrary-precision arithmetic:

Use fixed 24-digit precision arithmetic:

Options (3)

A constant term is included by default:

Construct a design matrix without a constant term:

Treat x as a numeric variable:

Treat x as nominal:

Use nominal variables that are not numeric:

Treat only x as nominal:

Treat all predictors as nominal:

Properties & Relations (1)

DesignMatrix constructs the design matrix used by LinearModelFit:

The matrix is the same for GeneralizedLinearModelFit: