## 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 p-values
#
# 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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