make_matrices_old: Build the design and the penalty matrix for models involving...

In LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

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

Description

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

Usage

make_matrices_old(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)

LaMa documentation built on Nov. 5, 2025, 6:42 p.m.