This function is the workhorse for fitting a gamlasso model. Not recommended
to call directly. It is slightly more efficient than gamlasso.default since
it doesn't perform any quality checks. Only use if the data has been cleaned
and no errors are expected to occur.
gamlassoFit(
data,
formula = NULL,
response.name = NULL,
linear.name = NULL,
smooth.name = NULL,
family = "gaussian",
linear.penalty = 0,
smooth.penalty = 2,
offset.name = NULL,
weights.name = NULL,
num.knots = 5,
num.iter = 100,
interactions = F,
tolerance = 1e-04,
seed = .Random.seed[1],
verbose = TRUE
)The training data for fitting the model
A formula describing the model to be fitted
The name of the response variable. Vector of two if
family = "binomial"
The names of the variables to be used as linear predictors
The names of the variables to be used as smoothers
The family describing the error distribution and link function
to be used in the model. A character string which can only be
"gaussian" (default), "binomial", "poisson" or
"cox". For family = "binomial", response can be
a vector of two and for family="cox", weights must
be provided (see details below).
The penalty used on the linear predictors. Can be 0, 1 or 2
The penalty used on the smoothers. Can be 1 or 2
The name of the offset variable. NULL (default) if not provided
The name of the weights variable. NULL (default)
if not provided. See Details of gamlasso.
Number of knots for each smoothers. Can be a single integer (recycled for each smoother variable) or a vector of integers the same length as the number of smoothers.
Number of iterations for the gamlasso loop
logical. Should interactions be included.
Tolerance for covergence of the gamlasso loop
The random seed can be specified for reproducibility. This is used for fitting the gam and lasso models, or fixed before each loop of gamlasso.
logical. Should there be "progress reports" printed to the console while fitting the model.
See gamlasso
# NOT RUN {
## Not recommended to use directly. Please see examples of gamlasso
# }
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