cv_glmaag: Cross validation for glmaag

In glmaag: Adaptive LASSO and Network Regularized Generalized Linear Models

Description

Do k-fold cross-validation for glmaag

Usage

cv_glmaag(y, x, L, nfolds = 5, cvwhich, foldseed, stratify = T,
  gam = 1, tune = F, est = T, lam1, lam2, 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 first network
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
gam	The power of weights of L1 penalty, default to be ones
tune	whether to tune the input network with estimated network or identity matrix, ignored if no input network
est	when there is no input network whether to use estimated network or identiy matrix (elastic net) or mixed the network with estimated network or identity matrix, default to be estimated network
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
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, 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, default to be one standard error rule
parallel	whether to do parallel computing at each fold, need to set up parallel first, default to be FALSE

Value

input	input predictor matrix
inputweight	estimated weights if mixing network
lambda1	lambda1 path that has been searched
lambda1	lambda1 path that has been searched
lambda1_max	selected lambda1 based on maximum rule
lambda2_max	selected lambda2 based on maximum rule
lambda1_1se	selected lambda1 based on one standard error rule
lambda2_1se	selected lambda2 based on one standard error rule
cvm	the mean cross validation accuracy
cv1se	the standard error of cross validation accuracy
cvn	the mean number of parameter estimated among folds
n_max	number of selected features based on maximum rule
n_1se	number of selected features based on one standard error rule
intercept_max	estimated intercept based on maximum rule
intercept_1se	estimated intercept based on one standard error rule
coef_max	estimated coefficients based on maximum rule
coef_1se	estimated coefficients based on one standard error rule
fam	family of outcome
measure	measure in cross validation

Examples

data(sampledata)
data(L0)
y <- sampledata$Y_Gau
x <- sampledata[, -(1:3)]
cvwhich <- sample(rep(0:4, length.out = length(y)))
mod <- cv_glmaag(y, x, L0, cvwhich = cvwhich)

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