glmaag: Fit glmaag model

In glmaag: Adaptive LASSO and Network Regularized Generalized Linear Models

Description

Fit the glmaag model with given tuning parameters without cross validation or stability selection

Usage

glmaag(y, x, L, tune = F, est = T, gam = 1, lam1, lam2, nfolds = 5,
  dfmax, w0, adaptl1 = T, adaptl2 = T, pind, intercept = T,
  standardize = T, maxiter = 10000, cri = 0.001, fam = "Gaussian",
  measdev = T, type1se = T, parallel = F)

Arguments

y	outcome
x	predictors matrix
L	Laplacian matrix for the network
tune	whether to tune with an estimated network, default to be FALSE
est	whether to estimate a network from the data
gam	the parameter for l1 adaptive weight, default to be ones
lam1	The tuning parameters for L1 penalty. If not defined, searched by default
lam2	The tuning parameters for quadratic penalty. If not defined, searched by default
nfolds	number of folds used in cross validation to obatin network sign estimate and l1 weight estimate, default to be five
dfmax	maximum number of parameters allowed in the model, default to be p/2
w0	Weights for l1 penalty. If not defined, estimated via quadratic penalyzed regression
adaptl1	whether to adapt the l1 penalty, default to be TRUE
adaptl2	whether to adapt the sign for quadratic penalty, default to be TRUE
pind	indicator vector whether to put L1 penalty on the feature, 1 means penalyzed while 0 means not penalyzed, default to be all ones (all penalyzed)
intercept	whether to include intercept. Ignore for Cox regression
standardize	whether to standardize predictors
maxiter	maximum number of iterations, default to be 500
cri	stoppint criterion, default to be 0.001
fam	family for the outcome, can be "Gaussian", "Logistic", and "Cox"
measdev	Whether to use deviance to tune when estimate l1 weight and network sign, default to be deviance. If not, use mean absolue error, area under ROC curve, or concordance index for Gaussian, Logistic, and Cox
type1se	whether to use one standard error or maximum rule when estimate network sign and l1 weight, default to be one standard error rule
parallel	whether to do parallel computing at each lambda2, need to set up parallel first, default to be FALSE

Value

input	input predictors
lambda1	l1 penalty parameter search sequence
lambda2	quadratic penalty parameter search sequence
ns	number of parameters selected given provided tuning parameter
coefs	coefficients estimated
intercept	intercepts estimated
loglik	log likelihood estimated
fam	family of the outcome

Examples

data(sampledata)
data(L0)
y <- sampledata$Y_Gau
x <- sampledata[, -(1:3)]
mod <- glmaag(y, x, L0)

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