tune_network: tune two network

In glmaag: Adaptive LASSO and Network Regularized Generalized Linear Models

Description

Tune two network for better prediction.

Usage

tune_network(y, x, L1, L2, adaptl2 = T, nfolds = 5, cvwhich, foldseed,
  stratify = T, lam0, bets, intercept = T, standardize = T,
  fam = "Gaussian", type1se = T, measdev = T, maxiter = 10000,
  cri = 0.001, parallel = F)

Arguments

y	outcome
x	predictors matrix
L1	Laplacian matrix for the first network
L2	Laplacian matrix for the second network
adaptl2	whether to adapt the sign for quadratic penalty, default to be TRUE
nfolds	number of folds used in cross validation, default to be five
cvwhich	fold assignment, start from zero, if missing do random cross validation
foldseed	the random seed for cross validation design
stratify	whether to do stratified cross validation for Logistic or Cox model, default to be TRUE
lam0	The tuning parameters for quadratic penalty. If not defined, tuned by default
bets	The candidate weight for the first network, must be between 0 and 1, default to be 0, 0.1,..., 1
intercept	whether to include intercept. Ignore for Cox regression
standardize	whether to standardize predictors
fam	family for the outcome, can be "Gaussian", "Logistic", and "Cox"
type1se	whether to use one standard error or maximum rule, default to be one standard error rule
measdev	Whether to use deviance to tune, default to be deviance. If not, use mean absolue error, area under ROC curve, or concordance index for Gaussian, Logistic, and Cox
maxiter	maximum number of iterations, default to be 500
cri	stoppint criterion, default to be 0.001
parallel	whether to do parallel computing at each fold

Value

est	estimated mixed Laplacian matrix
weight	weights for the two Laplacian matrix

Examples

data(sampledata)
data(L0)
data(L1)
y <- sampledata$Y_Gau
x <- sampledata[, -(1:3)]
Ltune <- tune_network(y, x, L0, L1, adaptl2 = FALSE)
weight <- Ltune@weight
Lest <- Ltune@est

glmaag documentation built on May 10, 2019, 9:04 a.m.