The function globalboosttest
implements a permutationbased testing procedure to globally test the (additional) predictive value of a large set of predictors given that a small set of predictors is already available.
1 
X 
A n x p matrix or data frame with observations in rows and variables in columns, whose additional predictive value has to be tested. 
Y 
Either a nvector of type factor (if the prediction outcome is binary), or a numeric vector of length n (if the prediction outcome is numeric and uncensored), or a 
Z 
A n x q matrix or data frame with observations in rows and variables in columns, on which we want to condition. Note that q should be smaller than n. If 
nperm 
The number of permutations used to derived the pvalue. 
mstop 
A numeric vector giving the number(s) of boosting steps at which the pvalue has to be calculated. 
mstopAIC 
If 
pvalueonly 
Should the function return only the permutation pvalue or also the risk for all numbers of boosting steps and all permutations? 
plot 
If 
... 
Further arguments to be passed to the 
See Boulesteix and Hothorn (2009) for details on the methodology.
If mstopAIC=TRUE
, the number of boosting steps is chosen from 1 to max(mstop)
independently of the specific values
included in the vector mstop
.
A list with the following arguments
riskreal 
A numeric vector of length 
riskperm 
A 
mstopAIC 
The number of boosting steps selected using the AICbased procedure (if 
pvalue 
A numeric vector of length 
AnneLaure Boulesteix (http://www.ibe.med.unimuenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/eng.html),
Torsten Hothorn (http://www.statistik.lmu.de/~hothorn/)
A. L. Boulesteix and Torsten Hothorn (2010). Testing the additional predictive value of highdimensional data. BMC Bioinformatics 10:78.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  # load globalboosttest library
library(globalboosttest)
# load the simulated data with binary outcome
data(simdatabin)
attach(simdatabin)
# Test with 25 permutations
test<globalboosttest(X=X,Y=Y,Z=Z,nperm=25,mstop=c(100,500,1000))
# load the simulated data with survival outcome
data(simdatasurv)
attach(simdatasurv)
# Test with 25 permutations
test<globalboosttest(X=X,Y=Surv(time,status),Z=NULL,nperm=25,mstop=c(100,500,1000),mstopAIC=FALSE)

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