ss_glmaag: Stability selection for glmaag

In glmaag: Adaptive LASSO and Network Regularized Generalized Linear Models

Description

Do stability selection for glmaag

Usage

ss_glmaag(y, x, L, nfolds = 5, subn, nsam = 100, beta = 0.15,
  gam = 1, tune = F, est = T, lam1, lam2, 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 to estimate the l1 weights or network tuning, default to be five
subn	number of samples in each subset, default to be n/2 if n<400 and 10sqrt(10) if n>400
nsam	number of subsets, default to be 100
beta	the cut off for instability score
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
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 for l1 weight estimation and network sign, default to be one standard error rule
parallel	whether to do parallel computing at each subset, need to set up parallel first, default to be FALSE

Value

input	input matrix for predictors
lambda1	searching sequence for l1 penalty parameters
lamdba2	searching sequence for quadratic penalty parameters
lambda1_ss	optimal l1 parameter
lambda2_ss	optimal quadratic parameter
n_ss	number of parameters obtained by the optimal model
ssm	instability score paths
ssf	selection probability paths
intercept_ss	intercept estimated by the optimal model
coef_ss	coefficients estimated by the optimal model
fam	the family of the outcome

References

Meinshausen, N., & B"uhlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417-473.

Liu, H., Roeder, K., & Wasserman, L. (2010). Stability approach to regularization selection (stars) for high dimensional graphical models. In Advances in neural information processing systems (pp. 1432-1440).

Examples

data(sampledata)
data(L0)
y <- sampledata$Y_Gau
x <- sampledata[, 4:6]
mod <- ss_glmaag(y, x, L0[seq_len(3), seq_len(3)], nsam = 3)

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