Description Usage Arguments Value Note Examples
This function checks if the arguments entered for fitting a gamlasso model
are compatible with each other. Not recommended to call directly. Only use
if cleaning data prior to fitting gamlassoFit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | gamlassoChecks(
data,
response.name,
linear.name,
smooth.name,
family,
linear.penalty,
smooth.penalty,
offset.name,
weights.name,
num.knots,
num.iter,
tolerance,
seed,
prompts
)
|
data |
The training data for fitting the model |
response.name |
The name of the response variable. Vector of two if
|
linear.name |
The names of the variables to be used as linear predictors |
smooth.name |
The names of the variables to be used as smoothers |
family |
The family describing the error distribution and link function
to be used in the model. A character string which can only be
|
linear.penalty |
The penalty used on the linear predictors. Can be 0, 1 or 2 |
smooth.penalty |
The penalty used on the smoothers. Can be 1 or 2 |
offset.name |
The name of the offset variable. |
weights.name |
The name of the weights variable. |
num.knots |
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. |
num.iter |
Number of iterations for the gamlasso loop |
tolerance |
Tolerance for covergence of the gamlasso loop |
seed |
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. |
prompts |
logical. Should |
gamlassoChecks
produces a series of logical values:
allcheck
indicating if the arguments passed all the checks,
fit.smoothgam
indicating if there aren't any linear predictors and
a model with only smoothers should be fitted, fit.glmnet
is the counterpart for smooth predictors. It also returns the cleaned
(if needed) arguments as a list named cleandata
who's elements are:
train.data | The training data with unnecessary columns deleted |
linear.name , smooth.name , num.knots | The changed variable names and number of knots |
linear.penalty , smooth.penalty | The changed penalties for linear and smooth terms. Reset to their default values only in the rare case of too few predictors |
The arguments offset.name
, num.iter
, tolerance
and seed
are not currently not being used in testing.
1 | ## Usage similar to gamlassoFit
|
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