... |
Variables for which to generate multivariate interaction basis
function where the variables can be found in a matrix X in a parent
environment/frame. Note, just like standard formula objects, the
variables should not be characters (e.g. do h(W1,W2) not h("W1", "W2"))
h(W1,W2,W3) will generate three-way HAL basis functions between W1, W2, and
W3. It will not generate the lower dimensional basis functions.
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k |
The number of knots for each univariate basis function used to
generate the tensor product basis functions. If a single value then this
value is used for the univariate basis functions for each variable.
Otherwise, this should be a variable named list that specifies for each
variable how many knots points should be used.
h(W1,W2,W3, k = list(W1 = 3, W2 = 2, W3=1)) is equivalent to first
binning the variables W1, W2 and W3 into 3, 2 and 1 unique
values and then calling h(W1,W2,W3). This coarsening of the data ensures
that fewer basis functions are generated, which can lead to substantial
computational speed-ups. If not provided and the variable num_knots
is in the parent environment, then s will be set to
num_knots'.
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s |
The smoothness_orders for the basis functions. The possible
values are 0 for piece-wise constant zero-order splines or 1 for
piece-wise linear first-order splines. If not provided and the variable
smoothness_orders is in the parent environment, then s will
be set to smoothness_orders.
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pf |
A penalty.factor value the generated basis functions that is
used by glmnet in the LASSO penalization procedure. pf = 1
(default) is the standard penalization factor used by glmnet and
pf = 0 means the generated basis functions are unpenalized.
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monotone |
Whether the basis functions should enforce monotonicity of
the interaction term. If \code{s} = 0, this is monotonicity of the
function, and, if \code{s} = 1, this is monotonicity of its derivative
(e.g., enforcing a convex fit). Set "none" for no constraints, "i" for
a monotone increasing constraint, and "d" for a monotone decreasing
constraint. Using "i" constrains the basis functions to have positive
coefficients in the fit, and "d" constrains the basis functions to have
negative coefficients.
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. |
Just like with formula, . as in h(.) or h(.,.) is
treated as a wildcard variable that generates terms using all variables in
the data. The argument . should be a character vector of variable
names that . iterates over. Specifically,
h(., k=1, . = c("W1", "W2", "W3")) is equivalent to
h(W1, k=1) + h(W2, k=1) + h(W3, k=1), and
h(., ., k=1, . = c("W1", "W2", "W3")) is equivalent to
h(W1,W2, k=1) + h(W2,W3, k=1) + h(W1, W3, k=1)
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dot_args_as_string |
Whether the arguments ... are characters or
character vectors and should thus be evaluated directly. When TRUE, the
expression h("W1", "W2") can be used.
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X |
An optional design matrix where the variables given in ...
can be found. Otherwise, X is taken from the parent environment.
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