CrossValidate FCnet model — cv

This function is a wrapper around glmnetUtils::cva.glmnet(), which is itself built around glmnet::cv.glmnet(). For extended documentation, the readers are encouraged to consult the respective pages. cv_FCnet() requires two objects at minimum: y is a vector or data.frame with exactly one column, corresponding to the (behavioral) score to predict; x is a data.frame or a list of lists with an entry named "Weights", which includes the independent variables. x can be - and is meant to be - one object created by reduce_featuresFC(), but this is not strictly necessary. The crossvalidated lambda and alpha parameters are returned. For the model, use the FCnetLOO() function to avoid overfitting. Differently from glmnet::cv.glmnet(), here the mean absolute error is minimized by default; you can change this parameter directly in the function or by running optionsFCnet(optionsFCnet= "mse").

cv_FCnet(
  y,
  x,
  alpha = seq(0, 1, by = 0.1),
  lambda = rev(10^seq(-5, 5, length.out = 200)),
  parallelLOO = F,
  cv_Ncomp = NULL,
  cv_Ncomp_method = c("order", "R"),
  family = optionsFCnet("family"),
  type.measure = optionsFCnet("cv.type.measure"),
  intercept = optionsFCnet("intercept"),
  standardize = optionsFCnet("standardize"),
  thresh = optionsFCnet("thresh"),
  ...
)

Arguments

y	The dependent variable, typically behavioral scores to predict. This can be a vector or a single data.frame column.
x	The independent variables, typically neural measures that have been already summarised through data reduction techniques (e.g. ICA, PCA): an object created by `reduce_featuresFC()` will do. If such an object is passed to this function, the "Weights" slot is taken as x. A list can be passed to this function: in this case the function needs an entry named "Weights". Otherwise, a data.frame can be passed to x.
alpha	Value(s) that bias the elastic net toward ridge regression (alpha== 0) or LASSO regression (alpha== 1). If a vector of alpha values is supplied, the value is optimized through crossvalidation. It defaults to a vector ranging from 0 to 1 with steps of 0.1. The crossvalidated alpha is returned.
lambda	Regularization parameter for the regression, see `glmnet::glmnet()`. Lambda must be a vector with length>1. When a vector of lambda values is supplied, the value of lambda is optimized through internal crossvalidation. It defaults to a vector ranging from 10^-5 to 10^5 with 200 values in logarithmic steps. The crossvalidated optimal lambda is returned.
parallelLOO	If TRUE - recommended, but not the default - uses `future.apply::future_lapply()` for the outer loops: `future.apply` must be installed, the machine should have multiple cores available for use, and threads should be defined explicitly beforehand by the user (e.g. by calling `plan(multisession)`).
cv_Ncomp	Whether to crossvalidate the number of components or not. It defaults to NULL, but a vector can be supplied specifing the number (range) of components to test in the inner loops.
cv_Ncomp_method	Whether the number of components to optimize means components are ordered (e.g. according to the explained variance of neuroimaging data) or - somehow experimental - whether to use the N best components ranked according to their relationship (pearson's R) with y.
family	Defaults to "gaussian." Experimental support for "binomial" on the way.
intercept	whether to fit (TRUE) or not (FALSE) an intercept to the model.
standardize	Whether x must be standardized internally to glmnet.
thresh	Threshold for glmnet to stop converging to the solution.
...	Other parameters passed to `glmnetUtils::cva.glmnet()`.
cv.type.measure	The measure to minimize in crossvalidation inner loops. Differently from `glmnetUtils::cva.glmnet()` the deafult is the mean absolute error.

Value

The crossvalidated alpha and lambda parameters with the associated error.