```
# NOT RUN {
options(knots=4, poly.degree=2)
# To get the old behavior of rcspline.eval knot placement (which didnt' handle
# clumping at the lowest or highest value of the predictor very well):
# options(fractied = 1.0) # see rcspline.eval for details
country <- factor(country.codes)
blood.pressure <- cbind(sbp=systolic.bp, dbp=diastolic.bp)
fit <- lrm(Y ~ sqrt(x1)*rcs(x2) + rcs(x3,c(5,10,15)) +
lsp(x4,c(10,20)) + country + blood.pressure + poly(age,2))
# sqrt(x1) is an implicit asis variable, but limits of x1, not sqrt(x1)
# are used for later plotting and effect estimation
# x2 fitted with restricted cubic spline with 4 default knots
# x3 fitted with r.c.s. with 3 specified knots
# x4 fitted with linear spline with 2 specified knots
# country is an implied catg variable
# blood.pressure is an implied matrx variable
# since poly is not an rms function (pol is), it creates a
# matrx type variable with no automatic linearity testing
# or plotting
f1 <- lrm(y ~ rcs(x1) + rcs(x2) + rcs(x1) %ia% rcs(x2))
# %ia% restricts interactions. Here it removes terms nonlinear in
# both x1 and x2
f2 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 %ia% rcs(x2))
# interaction linear in x1
f3 <- lrm(y ~ rcs(x1) + rcs(x2) + x1 %ia% x2)
# simple product interaction (doubly linear)
# Use x1 %ia% x2 instead of x1:x2 because x1 %ia% x2 triggers
# anova to pool x1*x2 term into x1 terms to test total effect
# of x1
#
# Examples of gTrans
#
# Linear relationship with a discontinuity at zero:
ldisc <- function(x) {z <- cbind(x == 0, x); attr(z, 'nonlinear') <- 1; z}
gTrans(x, ldisc)
# Duplicate pol(x, 2):
pol2 <- function(x) {z <- cbind(x, x^2); attr(z, 'nonlinear') <- 2; z}
gTrans(x, pol2)
# Linear spline with a knot at x=10 with the new slope taking effect
# until x=20 and the spline turning flat at that point but with a
# discontinuous vertical shift
dspl <- function(x) {
z <- cbind(x, pmax(pmin(x, 20) - 10, 0), x > 20)
attr(z, 'nonlinear') <- 2:3
z }
gTrans(x, dspl)
# }
```

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