Description Usage Arguments Value Author(s)
Accurate evaluation of the predictive performance of SGL
1 2 3 4  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)

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 crossvalidation 
measure 
performance measure used to select best lambdas and alphas: loglikelihood 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 selfselect values 
verbose 
logical for verbosity of display 
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 
David Degras and Kourosh Zarringhalam
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