nested.cvSGL: Nested Cross-validation for Sparse Group Lasso

Description Usage Arguments Value Author(s)

View source: R/nested.cvSGL.R

Description

Accurate evaluation of the predictive performance of SGL

Usage

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nested.cvSGL(ents, rels, x, y, type = c("linear", "logit"), alphas = seq(0, 1, 0.1), nlam = 20, 
	standardize = c("train", "self", "all", "no"), nfold = 10, measure = c("ll", "auc"), maxit = 1000, 
	thresh = 0.001, min.frac = 0.05, gamma = 0.8, step = 1, reset = 10, cre.sig = 0.01, de.sig = 0.01, 
	ncores = 1, lambdas = NULL, verbose = TRUE)

Arguments

ents

data frame of KB entries

rels

data frame of KB relations

x

matrix of predictors (gene expression levels)

y

response vector (numeric for linear regression, binary for logistic regression)

type

type of model: linear or logistic

alphas

Vector of mixing parameters for lasso/group lasso penalty (0 = pure group lasso, 1 = pure lasso)

nlam

number of lambda values in the regularization path

standardize

type of data standardization to be performed

nfold

number of folds in the inner cross-validation

measure

performance measure used to select best lambdas and alphas: log-likelihood or area under curve

maxit

Maximum number of iterations to convergence

thresh

Convergence threshold for change in beta

min.frac

Minimum value of the penalty parameter, as a fraction of the maximum value

gamma

Fitting parameter used for tuning backtracking (between 0 and 1)

step

Fitting parameter used for initial backtracking step size (between 0 and 1)

reset

Fitting parameter used for taking advantage of local strong convexity in Nesterov momentum (number of iterations before momentum term is reset)

cre.sig
de.sig
ncores

Number of computer cores used in calculations

lambdas

Optional sequence of lambda values for fitting. We recommend leaving this NULL and letting SGL self-select values

verbose

logical for verbosity of display

Value

A list with components

best.lambdas

Best lambda value for each outer fold

best.alphas

Best alpha value for each outer fold

pred

predicted responses (=probabilities for logistic regression)

accuracy

Classification rates

ROC

ROC curve and AUC

Author(s)

David Degras and Kourosh Zarringhalam


kouroshz/creNet documentation built on Feb. 25, 2018, 12:41 p.m.