ahazisis {ahaz}  R Documentation 
Fast and scalable model selection for the semiparametric additive hazards model via univariate screening combined with penalized regression.
ahazisis(surv, X, weights, standardize=TRUE,
nsis=floor(nobs/1.5/log(nobs)), do.isis=TRUE,
maxloop=5, penalty=sscad.control(), tune=cv.control(),
rank=c("FAST","coef","z","crit"))
surv 
Response in the form of a survival object, as returned by the
function 
X 
Design matrix. Missing values are not supported. 
weights 
Optional vector of observation weights. Default is 1 for each observation. 
standardize 
Logical flag for variable standardization, prior to
model fitting. Estimates are always returned on
the original scale. Default is 
nsis 
Number of covariates to recruit initially. If

.
do.isis 
Perform iterated independent screening? 
maxloop 
Maximal number of iterations of the algorithm if 
rank 
Method to use for (re)recruitment of variables. See details. 
penalty 
A description of the penalty function to be used for
the variable selection part. This can be a character string naming a penalty
function (currently 
tune 
A description of the tuning method to be used for the
variable selection part. This can be
a character string naming a tuning control
function (currently 
The function is a basic implementation of the iterated sure independent screening method described in GorstRasmussen & Scheike (2011). Briefly, the algorithm does the following:
Recruits the nsis
most relevant covariates by ranking them according to the univariate ranking
method described by rank
.
Selects, using ahazpen
with penalty function described
in penalty
, a model among the
top two thirds of the nsis
most relevant covariates. Call the
size of this model m
.
Recruits 'nsis
minus m
' new covariates among the nonselected
covariates by ranking their relevance according to the univariate
ranking method described in rank
, adjusted for the already
selected variables (using an unpenalized semiparametric additive
hazards model).
Steps 23 are iterated for maxloop
times, or until nsis
covariates has been recruited, or until the
set of selected covariate is stable between two iterations; whichever
comes first.
The following choices of ranking method exist:
rank="FAST"
corresponds to ranking, in the initial
recruitment step only, by the basic FAST statistic
described in GorstRasmussen & Scheike (2011). If do.isis=TRUE
then the algorithm sets rank="z"
for subsequent rankings.
rank="coef"
corresponds to ranking by absolute value of
(univariate) regression coefficients, obtained via ahaz
rank="z"
corresponds to ranking by the Z
statistic of
the (univariate) regression coefficients, obtained via ahaz
rank="crit"
corresponds to ranking by the size
of the decrease in
the (univariate) natural loss function used for estimation by ahaz
.
An object with S3 class "ahazisis"
.
call 
The call that produced this object. 
initRANKorder 
The initial ranking order. 
detail.pickind 
List (of length at most 
detail.ISISind 
List (of length at most 
detail.ISIScoef 
List (of length at most 
SISind 
Indices of covariates selected in the initial recruitment step. 
ISISind 
Indices of the final set of covariates selected by the iterated algorithm. 
ISIScoef 
Vector of the penalized regression coefficients of the
covariates in 
nsis 
The argument 
do.isis 
The argument 
maxloop 
The argument 
GorstRasmussen, A. & Scheike, T. H. (2011). Independent screening for singleindex hazard rate models with ultrahigh dimensional features. Technical report R201106, Department of Mathematical Sciences, Aalborg University.
print.ahazisis
, ahazpen
, ahaz.adjust
data(sorlie)
# Break ties
set.seed(10101)
time < sorlie$time+runif(nrow(sorlie))*1e2
# Survival data + covariates
surv < Surv(time,sorlie$status)
X < as.matrix(sorlie[,3:ncol(sorlie)])
# Basic ISIS/SIS with a single step
set.seed(10101)
m1 < ahazisis(surv,X,maxloop=1,rank="coef")
m1
# Indices of the variables from the initial recruitment step
m1$SISind
# Indices of selected variables
m1$ISISind
# Check fit
score < X[,m1$ISISind]%*%m1$ISIScoef
plot(survfit(surv~I(score>median(score))))