cv_FCnet: CrossValidate FCnet model

In EBlini/FCnet: Analysis of Functional Connectivity matrices through elastic NETs

CrossValidate FCnet model

Description

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").

Usage

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 (in the context of a univariate (g)lm) 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.

EBlini/FCnet documentation built on April 13, 2022, 10:23 p.m.