make_matrices: 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(formula, data, knots = NULL)

Arguments

formula	formula as used in mgcv. Formulas can be right-side only, or contain a response variable, which is just extracted for naming. Can also be a list of formulas, which are then processed separately. In that case, both a named list of right-side only formulas or a list of formulas with response variables can be provided.
data	data frame containing all the variables on the right side of the formula(s)
knots	optional list containing user specified knot values for each covariate 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. If formula is a list, this needs to be a named (based on the response variables) list over such lists.

Value

a list of class LaMa_matrices containing:

Z	design matrix (or list of such matrices if formula is a list))
S	list of penalty matrices (with names based on the response terms of the formulas as well as the smooth terms and covariates). For tensorproduct smooths, corresponding entries are themselves lists, containing the d marginal penalty matrices if d is the dimension of the tensor product)
pardim	list of parameter dimensions (fixed and penalised separately) for each formula, for ease of setting up initial parameters
coef	list of coefficient vectors filled with zeros of the correct length for each formula, for ease of setting up initial parameters
data	the data frame used for the model(s)
gam	unfitted mgcv::gam object used for construction of Z and S (or list of such objects if formula is a list)
gam0	fitted mgcv::gam which is used internally for to create prediction design matrices (or list of such objects if formula is a list)
knots	knot list used in the basis construction (or named list over such lists if formula is a list

See Also

predict.LaMa_matrices for prediction design matrix construction based on the model matrices object created by this function.

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)
# multiple formulas at once
modmat = make_matrices(list(mu ~ s(x) + y, sigma ~ s(g, bs = "re")), data = data)

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