#--------------linear model--------------#
# Generate simulated data
n <- 500
p <- 20
K <-10
sigma <- 1
rho <- 0.2
data <- gen.data(n, p, family = "gaussian", K, rho, sigma)
# Best subset selection
fit1 <- bess.one(data$x, data$y, s = 10, family = "gaussian", normalize = TRUE)
#coef(fit1,sparse=TRUE)
bestmodel <- fit1$bestmodel
#summary(bestmodel)
## Not run:
#--------------logistic model--------------#
# Generate simulated data
data <- gen.data(n, p, family = "binomial", K, rho, sigma)
# Best subset selection
fit2 <- bess.one(data$x, data$y, family = "binomial", s = 10, normalize = TRUE)
bestmodel <- fit2$bestmodel
#summary(bestmodel)
#--------------cox model--------------#
# Generate simulated data
data <- gen.data(n, p, K, rho, sigma, c=10, family="cox", scal=10)
# Best subset selection
fit3 <- bess.one(data$x, data$y, s = 10, family = "cox", normalize = TRUE)
bestmodel <- fit3$bestmodel
#summary(bestmodel)
#----------------------High dimensional linear models--------------------#
p <- 1000
data <- gen.data(n, p, family = "gaussian", K, rho, sigma)
# Best subset selection
fit <- bess.one(data$x, data$y, s=10, family = "gaussian", normalize = TRUE)
#---------------prostate---------------#
data("prostate")
x = prostate[,-9]
y = prostate[,9]
fit.ungroup = bess.one(x, y, s=5)
fit.group = bess.one(x, y, s=5, factor = c("gleason"))
#---------------SAheart---------------#
data(SAheart)
y = SAheart[,5]
x = SAheart[,-5]
x$ldl[x$ldl<5] = 1
x$ldl[x$ldl>=5&x$ldl<10] = 2
x$ldl[x$ldl>=10] = 3
fit.ungroup = bess.one(x, y, s=5, family = "binomial")
fit.group = bess.one(x, y, s=5, factor = c("ldl"), family = "binomial")
## End(Not run)
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