man/examples/cirls.control.R

In cirls: Constrained Iteratively Reweighted Least Squares

# Simulate predictors and response with some negative coefficients
set.seed(111)
n <- 100
p <- 10
betas <- rep_len(c(1, -1), p)
x <- matrix(rnorm(n * p), nrow = n)
y <- x %*% betas + rnorm(n)

# Define constraint matrix (includes intercept)
# By default, bounds are 0 and +Inf
Cmat <- cbind(0, diag(p))

# Fit GLM by CIRLS
res1 <- glm(y ~ x, method = cirls.fit, Cmat = Cmat)
coef(res1)

# Same as passing Cmat through the control argument
res2 <- glm(y ~ x, method = cirls.fit, control = list(Cmat = Cmat))
identical(coef(res1), coef(res2))