Convert predictor matrix to design matrix

`D = x2fx(X,`

* model*)

D = x2fx(X,

`model`

D = x2fx(X,

`model`

`D = x2fx(X,`

converts
a matrix of predictors * model*)

`X`

to a design matrix `D`

for
regression analysis. Distinct predictor variables should appear in
different columns of `X`

.The optional input * model* controls
the regression model. By default,

`x2fx`

returns
the design matrix for a linear additive model with a constant term. `model`

`'linear'`

— Constant and linear terms. This is the default.`'interaction'`

— Constant, linear, and interaction terms`'quadratic'`

— Constant, linear, interaction, and squared terms`'purequadratic'`

— Constant, linear, and squared terms

If `X`

has *n* columns,
the order of the columns of `D`

for a full quadratic
model is:

The constant term

The linear terms (the columns of

`X`

, in order 1, 2, ...,*n*)The interaction terms (pairwise products of the columns of

`X`

, in order (1, 2), (1, 3), ..., (1,*n*), (2, 3), ..., (*n*–1,*n*))The squared terms (in order 1, 2, ...,

*n*)

Other models use a subset of these terms, in the same order.

Alternatively, * model* can be a matrix
specifying polynomial terms of arbitrary order. In this case,

`model`

`X`

and one row
for each term in the model. The entries in any row of `model`

`X`

. For
example, if `X`

has columns `X1`

, `X2`

,
and `X3`

, then a row `[0 1 2]`

in `model`

`(X1.^0).*(X2.^1).*(X3.^2)`

. A row of all
zeros in `model`

`D = x2fx(X,`

treats
columns with numbers listed in the vector * model*,categ)

`categ`

as
categorical variables. Terms involving categorical variables produce
dummy variable columns in `D`

. Dummy variables are
computed under the assumption that possible categorical levels are
completely enumerated by the unique values that appear in the corresponding
column of `X`

.`D = x2fx(X,`

accepts
a vector * model*,categ,catlevels)

`catlevels`

the same length as `categ`

,
specifying the number of levels in each categorical variable. In
this case, values in the corresponding column of `X`

must
be integers in the range from 1 to the specified number of levels.
Not all of the levels need to appear in `X`

.The following converts 2 predictors `X1`

and `X2`

(the
columns of `X`

) into a design matrix for a full quadratic
model with terms `constant`

, `X1`

, `X2`

, `X1.*X2`

, `X1.^2`

,
and `X2.^2`

.

X = [1 10 2 20 3 10 4 20 5 15 6 15]; D = x2fx(X,'quadratic') D = 1 1 10 10 1 100 1 2 20 40 4 400 1 3 10 30 9 100 1 4 20 80 16 400 1 5 15 75 25 225 1 6 15 90 36 225

The following converts 2 predictors `X1`

and `X2`

(the
columns of `X`

) into a design matrix for a quadratic
model with terms `constant`

, `X1`

, `X2`

, `X1.*X2`

,
and `X1.^2`

.

X = [1 10 2 20 3 10 4 20 5 15 6 15]; model = [0 0 1 0 0 1 1 1 2 0]; D = x2fx(X,model) D = 1 1 10 10 1 1 2 20 40 4 1 3 10 30 9 1 4 20 80 16 1 5 15 75 25 1 6 15 90 36

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