GLMvanISISscad {SIS} | R Documentation |
These functions implement the iterative sure independence screening with GLMvanISISscad
for vanilla ISIS and GLMvarISISscad
for variant ISIS in Generalized Linear Models.
GLMvanISISscad(x, y, nsis=NULL, family=binomial(), folds=folds, rank.method="obj", eps0=1e-3, inittype='NoPen', tune.method="AIC", ISIStypeCumulative=FALSE, DOISIS=TRUE, maxloop=5) GLMvarISISscad(x, y, nsis=NULL, family=binomial(), folds=folds, rank.method="obj", tune.method="AIC", vartype="First", eps0=1e-3, inittype='NoPen', ISIStypeCumulative=FALSE, DOISIS=TRUE, maxloop=5)
x |
an (n * p) matrix of features. |
y |
an (n) vector of response. |
nsis |
number of pedictors recuited by (I)SIS. |
family |
a description of the error distribution and link function to be used in the model. |
folds |
fold information for cross validation. |
rank.method |
the criterion for ranking predictor variables in (I)SIS. It can be either obj or coeff .
|
tune.method |
method for tuning regularization parameter. |
inittype |
inittype specifies the type of initial solution for the one-step SCAD. It can be either NoPen or L1 .
|
vartype |
vartype specifies variant (I)SIS of first type or second type.
|
ISIStypeCumulative |
ISIStypeCumulative specifies whether to allow variable deletion in each step of ISIS. (ISIStypeCumulative = FALSE allows variable deletion)
|
DOISIS |
DOISIS specifies whether to do iterative SIS.
|
maxloop |
maximum number of loops in iterative SIS. |
eps0 |
an effective zero. |
Returns an object with
initRANKorder |
initial predictor ranking order for vanilla SIS. |
detail.pickind, detail.ISISind |
details of each loop of ISIS. |
normal.exit |
indicator of normal exit. |
SISind |
the vector of indices selected by SIS. |
ISISind |
the vector of indices selected by ISIS. |
initRANKorder1, initRANKorder2 |
initial predictor ranking order for variant SIS. |
Jianqing Fan, Yang Feng, Richard Samworth, and Yichao Wu
Jianqing Fan and Jinchi Lv (2008) Sure independence screening for ultra-high dimensional feature space (with discussion) Journal of Royal Statistical Society B, 36, 849-911.
Jianqing Fan, Richard Samworth, and Yichao Wu (2009) Ultrahigh dimensional variable selection: beyond the linear model Journal of Machine Learning Research, to appear.
Jianqing Fan and Rui Song (2009) Sure Independence Screening in Generalized Linear Models with NP-Dimensionality, technical report.
set.seed(0) b <- c(4,4,4,-6*sqrt(2)) n=150 p=200 truerho=0.5 corrmat=diag(rep(1-truerho, p))+matrix(truerho, p, p) corrmat[,4]=sqrt(truerho) corrmat[4, ]=sqrt(truerho) corrmat[4,4]=1 cholmat=chol(corrmat) x=matrix(rnorm(n*p, mean=0, sd=1), n, p) x=x%*%cholmat feta=x[, 1:4]%*%b fprob=exp(feta)/(1+exp(feta)) y=rbinom(n, 1, fprob) nsis=floor(n/log(n)/4) binom.van.sis=GLMvanISISscad(x, y, nsis, family=binomial(), tune.method='BIC') binom.var.sis=GLMvarISISscad(x, y, nsis, family=binomial(), vartype='Second', tune.method='BIC') #####compare the result binom.van.sis$SIS binom.van.sis$ISIS binom.var.sis$SIS binom.var.sis$ISIS