Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set

Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set

Usage

make_matrices(formula, data, knots = NULL)

Arguments

formula

right side of a formula as used in mgcv

data

data frame containing the variables in the formula

knots

optional list containing user specified knot values to be used for basis construction

For most bases the user simply supplies the knots to be used, which must match up with the k value supplied (note that the number of knots is not always just k). See mgcv documentation for more details.

Value

a list containing the design matrix Z, a (potentially nested) list of penalty matrices S, the formula, the data, the knots, and the original mod object returned by mgcv. Note that for tensorproduct smooths, the corresponding list entry is itself a list, containing the d marginal penalty matrices if d is the dimension of the tensor product.

Examples

data = data.frame(x = runif(100), 
                  y = runif(100),
                  g = factor(rep(1:10, each = 10)))

# unvariate thin plate regression spline
modmat = make_matrices(~ s(x), data)
# univariate P-spline
modmat = make_matrices(~ s(x, bs = "ps"), data)
# adding random intercept
modmat = make_matrices(~ s(g, bs = "re") + s(x, bs = "ps"), data)
# tensorproduct of x and y
modmat = make_matrices(~ s(x) + s(y) + ti(x,y), data)