tune.ahazpen {ahaz}  R Documentation 
Tuning of penalty parameters for the penalized semiparametric additive hazards model via crossvalidation  or via nonstochastic procedures, akin to BIC for likelihoodbased models.
tune.ahazpen(surv, X, weights, standardize=TRUE, penalty=lasso.control(), tune=cv.control(), dfmax=nvars, lambda, ...)
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. Parameter estimates are always returned on
the original scale. Default is 
penalty 
A description of the penalty function to be used for
model fitting. This can be a character string naming a penalty
function (currently 
dfmax 
Limit the maximum number of covariates included in the
model. Default is 
lambda 
An optional user supplied sequence of penalty parameters. Typical usage
is to have the
program compute its own 
tune 
A description of the tuning method to be used. This can be
a character string naming a tuning control
function (currently 
... 
Additional arguments to be passed to 
The function performs an initial penalized fit based on the
penalty supplied in penalty
to obtain a sequence of
penalty parameters. Subsequently, it selects among these an optimal penalty parameter based on
the tuning control function described in tune
, see ahaz.tune.control
.
An object with S3 class "tune.ahazpen"
.
call 
The call that produced this object. 
lambda 
The actual sequence of 
tunem 
The tuning score for each value of 
tunesd 
Estimate of the crossvalidated standard error, if 
tunelo 
Lower curve = 
tuneup 
Upper curve = 
lambda.min 
Value of 
df 
Number of nonzero coefficients at each value of

tune 
The selected 
penalty 
The selected 
foldsused 
Folds actually used, if 
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.
ahaz.tune.control
, plot.tune.ahazpen
, ahazpen
.
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)]) # Training/test data set.seed(20202) train < sample(1:nrow(sorlie),76) test < setdiff(1:nrow(sorlie),train) # Run cross validation on training data set.seed(10101) cv.las < tune.ahazpen(surv[train,], X[train,],dfmax=30) plot(cv.las) # Check fit on the test data testrisk < predict(cv.las,X[test,],type="lp") plot(survfit(surv[test,]~I(testrisk<median(testrisk))),main="Low versus high risk") # Advanced example, crossvalidation of onestep SCAD # with initial solution derived from univariate models. # Since init.sol is specified as a function, it is # automatically crossvalidated as well scadfun<function(surv,X,weights){coef(ahaz(surv,X,univariate=TRUE))} set.seed(10101) cv.ssc<tune.ahazpen(surv[train,],X[train,], penalty=sscad.control(init.sol=scadfun), tune=cv.control(rep=5),dfmax=30) # Check fit on test data testrisk < predict(cv.ssc,X[test,],type="lp") plot(survfit(surv[test,]~I(testrisk<median(testrisk))),main="Low versus high risk")