require(rms)
## Hauck-Donner effect
set.seed(3)
n <- 200
x1 <- sample(0:1, n, TRUE)
x2 <- sample(0:1, n, TRUE)
L <- 0.4 * x1 + 25 * x2
y <- ifelse(runif(n) <= plogis(L), 1, 0)
f <- lrm(y ~ x1 + x2, x=TRUE, y=TRUE)
coef(f)
g <- Glm(y ~ x1 + x2, family=binomial, x=TRUE, y=TRUE)
rbind(coef(f), coef(g))
anova(g)
anova(g, test='LR')
x1 <- runif(n)
x2 <- runif(n)
x3 <- runif(n)
y <- round(10*(x1+x2+x3) + 6*rnorm(n))
f <- y ~ x1 + pol(x2, 2) * pol(x3, 2)
s <- Surv(exp(y)) ~ x1 + pol(x2, 2) * pol(x3, 2)
g <- lrm(f, x=TRUE, y=TRUE)
w <- function(fit) {
print(system.time(a1 <- anova(fit)))
print(system.time(a2 <- anova(fit, test='LR')))
print(a1)
print(a2)
invisible()
}
w(g)
g <- orm(f, x=TRUE, y=TRUE)
w(g)
g <- cph(s, x=TRUE, y=TRUE)
w(g)
# survreg.fit2 <- rms:::survreg.fit2
g <- psm(s, x=TRUE, y=TRUE)
w(g)
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