# NOT RUN {
data(sorlie)
set.seed(10101)
# Break ties
time <- sorlie$time+runif(nrow(sorlie))*1e-2
# Survival data + covariates
surv <- Surv(time,sorlie$status)
X <- as.matrix(sorlie[,3:ncol(sorlie)])
# Fit additive hazards regression model w/lasso penalty
fit <- ahazpen(surv, X, dfmax=100)
# Coefficients
beta <- predict(fit,X,lambda=0.08,type="coef")
barplot(as.numeric(beta))
# Linear predictions
linpred <- predict(fit,X,lambda=0.1,type="lp")
riskgrp <- factor(linpred < median(linpred))
plot(survfit(surv~riskgrp))
# Residuals
resid <- predict(fit, X, lambda=0.1, type = "residuals")
par(mfrow = c(1,2))
hist(resid[,1],main=colnames(resid)[1])
hist(resid[,2],main=colnames(resid)[2])
# Cumulative hazard
cumhaz <- predict(fit,X,lambda=0.1,type="cumhaz")
plot(cumhaz)
# }
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