Description Usage Arguments Value Author(s) References Examples
Runs cvr.glmnet giving different penalty factors to predictors from different blocks.
1 2 | cvr.ipflasso(X, Y, family, type.measure, standardize=TRUE, alpha=1, blocks, pf, nfolds,
ncv)
|
X |
a (nxp) matrix of predictors with observations in rows and predictors in columns |
Y |
n-vector giving the value of the response (either continuous, numeric-binary 0/1, or |
family |
should be "gaussian" for continuous |
type.measure |
The accuracy/error measure computed in cross-validation. If not specified, type.measure is "class" (classification error) if |
standardize |
whether the predictors should be standardized or not. Default is TRUE. |
alpha |
the elastic net mixing parameter: |
blocks |
a list of length M the format |
pf |
a vector of length equal to the number of blocks M. Each entry contains the penalty factor to be applied to the predictors of the corresponding block. Example: if |
nfolds |
the number of folds of CV procedure. |
ncv |
the number of repetitions of CV. Not to be confused with |
A list with the following arguments:
coeff |
the matrix of coefficients with predictors corresponding to rows and lambda values corresponding to columns. The first rows contains the intercept of the model (for all families other than |
ind.bestlambda |
the index of the best lambda according to CV. |
lambda |
the lambda sequence. |
cvm |
the CV estimate of the measure specified by |
nzero |
the number of non-zero coefficients in the selected model. |
family |
See arguments. |
Anne-Laure Boulesteix (https://www.en.ibe.med.uni-muenchen.de/mitarbeiter/professoren/boulesteix/index.html)
Boulesteix AL, De Bin R, Jiang X, Fuchs M, 2017. IPF-lasso: integrative L1-penalized regression with penalty factors for prediction based on multi-omics data. Comput Math Methods Med 2017:7691937.
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