Description Usage Arguments Details Value Author(s) References See Also Examples
Performs permutationbased pvalue estimation for the optional covariates in a fit from GAMBoost
or GAMBoost
. Currently binary response models with linear effects are supported, and the components have to be selected with criterion="score"
1 2 
object 
fit object obtained from 
x 

y 
response vector. This has to be the same that was used in the call to 
permute.n 
number of permutations employed for obtaining a null distribution. 
per.covariate 
logical value indicating whether a separate null distribution should be considered for each covariate. A larger number of permutations will be needed if this is wanted. 
parallel 
logical value indicating whether computations for obtaining a null distribution via permutation should be performed in parallel on a compute cluster. Parallelization is performed via the package 
multicore 
indicates whether computations in the permuted data sets should be performed in parallel, using package 
trace 
logical value indicating whether progress in estimation should be indicated by printing the number of the permutation that is currently being evaluated. 
... 
miscellaneous parameters for the calls to 
As pvalue estimates are based on permutations, random numbers are drawn for determining permutation indices. Therfore, the results depend on the state of the random number generator. This can be used to explore the variability due to random variation and help to determine an adequate value for permute.n
. A value of 100 should be sufficient, but this can be quite slow. If there is a considerable number of covariates, e.g., larger than 100, a much smaller number of permutations, e.g., 10, might already work well. The estimates might also be negatively affected, if only a small number of boosting steps (say <50) was employed for the original fit.
Vector with pvalue estimates, one value for each optional covariate with linear effect specificed in the original call to GAMBoost
or GLMBoost
.
Harald Binder [email protected]
Binder, H., Porzelius, C. and Schumacher, M. (2009). Rankbased pvalues for sparse highdimensional risk prediction models fitted by componentwise boosting. FDMPreprint Nr. 101, University of Freiburg, Germany.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Not run:
## Generate some data
x < matrix(runif(100*8,min=1,max=1),100,8)
eta < 0.5 + 2*x[,1] + 4*x[,3]
y < rbinom(100,1,binomial()$linkinv(eta))
## Fit a model with only linear components
gb1 < GLMBoost(x,y,penalty=100,stepno=100,trace=TRUE,family=binomial(),criterion="score")
# estimate pvalues
p1 < estimPVal(gb1,x,y,permute.n=10)
# get a second vector of estimates for checking how large
# random variation is
p2 < estimPVal(gb1,x,y,permute.n=10)
plot(p1,p2,xlim=c(0,1),ylim=c(0,1),xlab="permute 1",ylab="permute 2")
## End(Not run)

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