Nothing
## Gen data
n <- 40
p <- 10
X <- matrix(rnorm(n*p), ncol=p)
b <- c(2, -2, 1, -1, rep(0, p-4))
y <- rnorm(n, mean=X%*%b, sd=2)
####################################################
# perm.ncvreg works for linear regression
####################################################
pmfit <- perm.ncvreg(X, y)
par(mfrow=c(2,2))
plot(pmfit)
plot(pmfit, type="EF")
plot(pmfit$fit)
######################################################
# perm.ncvreg works for logistic regression
######################################################
pmfit <- perm.ncvreg(X, y > 0, family='binomial')
par(mfrow=c(2,2))
plot(pmfit)
plot(pmfit, type="EF")
plot(pmfit$fit)
plot(pmfit$fit, log=TRUE)
#####################################################
# perm.ncvreg works for Poisson regression
#####################################################
pmfit <- perm.ncvreg(X, rank(y), family='poisson')
par(mfrow=c(2,2))
plot(pmfit)
plot(pmfit, type="EF")
plot(pmfit$fit)
plot(pmfit$fit, log=TRUE)
#############################################################
# permute='residuals' option for perm.ncvreg works
#############################################################
pmfit <- perm.ncvreg(X, y, permute="residuals", N=25)
par(mfrow=c(2,2))
plot(pmfit)
plot(pmfit, type="EF")
plot(pmfit$fit)
plot(pmfit$fit)
# Setting seeds works
pmfit <- perm.ncvreg(X, y, permute="residuals", N=5, seed=1)
pmfit <- perm.ncvreg(X, y, seed=1)
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