penalty_control {deepregression} | R Documentation |
Options for penalty setup in the pre-processing
penalty_control(
defaultSmoothing = NULL,
df = 10,
null_space_penalty = FALSE,
absorb_cons = FALSE,
anisotropic = TRUE,
zero_constraint_for_smooths = TRUE,
no_linear_trend_for_smooths = FALSE,
hat1 = FALSE,
sp_scale = function(x) ifelse(is.list(x) | is.data.frame(x), 1/NROW(x[[1]]),
1/NROW(x))
)
defaultSmoothing |
function applied to all s-terms, per default (NULL)
the minimum df of all possible terms is used. Must be a function the smooth term
from mgcv's smoothCon and an argument |
df |
degrees of freedom for all non-linear structural terms (default = 7);
either one common value or a list of the same length as number of parameters;
if different df values need to be assigned to different smooth terms,
use df as an argument for |
null_space_penalty |
logical value;
if TRUE, the null space will also be penalized for smooth effects.
Per default, this is equal to the value give in |
absorb_cons |
logical; adds identifiability constraint to the basis.
See |
anisotropic |
whether or not use anisotropic smoothing (default is TRUE) |
zero_constraint_for_smooths |
logical; the same as absorb_cons,
but done explicitly. If true a constraint is put on each smooth to have zero mean. Can
be a vector of |
no_linear_trend_for_smooths |
logical; see |
hat1 |
logical; if TRUE, the smoothing parameter is defined by the trace of the hat matrix sum(diag(H)), else sum(diag(2*H-HH)) |
sp_scale |
function of response; for scaling the penalty (1/n per default) |
Returns a list with options