ahaz.adjust {ahaz}  R Documentation 
Adjusted univariate association measures from ahaz
Description
Fast calculation of univariate association measures in the semiparametric additive risk model, adjusted for userspecified covariates
Usage
ahaz.adjust(surv, X, weights, idx, method=c("coef", "z", "crit"))
Arguments
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. 
idx 
Vector specifying the indices of the covariates to adjust for. 
method 
The type of adjusted association measure to calculate. See details. 
Details
The function is intended mainly for programming use and
screening purposes, when a very large number of covariates are considered and direct application of
ahaz
is unfeasible.
Running this function is equivalent to running ahaz
with
design matrix cbind(X[,i],X[,idx])
for each column X[,i]
in
X
. By utilizing basic matrix identities, ahaz.adjust
runs many times faster.
The following univariate association measures are currently implemented:

method="z"
,Z
statistics, obtained from a fittedahaz
model. 
method="coef"
, regression coefficients, obtained from a fittedahaz
model. 
method="crit"
, the increase in the natural loss function of the semiparametric additive hazards model when the covariate is included in the model.
Value
A list containing the following elements:
call 
The call that produced this object. 
idx 
A copy of the argument 
adj 
Adjusted association statistic, as specified by 
See Also
Examples
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)])
# Adjust for first 10 covariates
idx < 1:10
a < ahaz.adjust(surv,X,idx=idx)
# Compare with (slower) solution
b < apply(X[,idx],2,function(x){coef(ahaz(surv,cbind(x,X[,idx])))[1]})
plot(b,a$adj[idx])