```
# NOT RUN {
n <- 1000 # define sample size
set.seed(17) # so can reproduce the results
age <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
cholesterol <- rnorm(n, 200, 25)
sex <- factor(sample(c('female','male'), n,TRUE))
label(age) <- 'Age' # label is in Hmisc
label(cholesterol) <- 'Total Cholesterol'
label(blood.pressure) <- 'Systolic Blood Pressure'
label(sex) <- 'Sex'
units(cholesterol) <- 'mg/dl' # uses units.default in Hmisc
units(blood.pressure) <- 'mmHg'
# Specify population model for log odds that Y=1
L <- .4*(sex=='male') + .045*(age-50) +
(log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))
# Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)]
y <- ifelse(runif(n) < plogis(L), 1, 0)
ddist <- datadist(age, blood.pressure, cholesterol, sex)
options(datadist='ddist')
fit <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)))
Predict(fit, age, cholesterol, np=4)
Predict(fit, age=seq(20,80,by=10), sex, conf.int=FALSE)
Predict(fit, age=seq(20,80,by=10), sex='male') # works if datadist not used
# Get simultaneous confidence limits accounting for making 7 estimates
# Predict(fit, age=seq(20,80,by=10), sex='male', conf.type='simult')
# (this needs the multcomp package)
ddist$limits$age[2] <- 30 # make 30 the reference value for age
# Could also do: ddist$limits["Adjust to","age"] <- 30
fit <- update(fit) # make new reference value take effect
Predict(fit, age, ref.zero=TRUE, fun=exp)
# Make two curves, and plot the predicted curves as two trellis panels
w <- Predict(fit, age, sex)
require(lattice)
xyplot(yhat ~ age | sex, data=w, type='l')
# To add confidence bands we need to use the Hmisc xYplot function in
# place of xyplot
xYplot(Cbind(yhat,lower,upper) ~ age | sex, data=w,
method='filled bands', type='l', col.fill=gray(.95))
# If non-displayed variables were in the model, add a subtitle to show
# their settings using title(sub=paste('Adjusted to',attr(w,'info')$adjust),adj=0)
# Easier: feed w into plot.Predict, ggplot.Predict, plotp.Predict
# }
# NOT RUN {
# Predictions form a parametric survival model
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n,
rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
t <- -log(runif(n))/h
label(t) <- 'Follow-up Time'
e <- ifelse(t<=cens,1,0)
t <- pmin(t, cens)
units(t) <- "Year"
ddist <- datadist(age, sex)
Srv <- Surv(t,e)
# Fit log-normal survival model and plot median survival time vs. age
f <- psm(Srv ~ rcs(age), dist='lognormal')
med <- Quantile(f) # Creates function to compute quantiles
# (median by default)
Predict(f, age, fun=function(x)med(lp=x))
# Note: This works because med() expects the linear predictor (X*beta)
# as an argument. Would not work if use
# ref.zero=TRUE or adj.zero=TRUE.
# Also, confidence intervals from this method are approximate since
# they don't take into account estimation of scale parameter
# Fit an ols model to log(y) and plot the relationship between x1
# and the predicted mean(y) on the original scale without assuming
# normality of residuals; use the smearing estimator. Before doing
# that, show confidence intervals for mean and individual log(y),
# and for the latter, also show bootstrap percentile nonparametric
# pointwise confidence limits
set.seed(1)
x1 <- runif(300)
x2 <- runif(300)
ddist <- datadist(x1,x2); options(datadist='ddist')
y <- exp(x1+ x2 - 1 + rnorm(300))
f <- ols(log(y) ~ pol(x1,2) + x2, x=TRUE, y=TRUE) # x y for bootcov
fb <- bootcov(f, B=100)
pb <- Predict(fb, x1, x2=c(.25,.75))
p1 <- Predict(f, x1, x2=c(.25,.75))
p <- rbind(normal=p1, boot=pb)
plot(p)
p1 <- Predict(f, x1, conf.type='mean')
p2 <- Predict(f, x1, conf.type='individual')
p <- rbind(mean=p1, individual=p2)
plot(p, label.curve=FALSE) # uses superposition
plot(p, ~x1 | .set.) # 2 panels
r <- resid(f)
smean <- function(yhat)smearingEst(yhat, exp, res, statistic='mean')
formals(smean) <- list(yhat=numeric(0), res=r[!is.na(r)])
#smean$res <- r[!is.na(r)] # define default res argument to function
Predict(f, x1, fun=smean)
## Example using offset
g <- Glm(Y ~ offset(log(N)) + x1 + x2, family=poisson)
Predict(g, offset=list(N=100))
# }
# NOT RUN {
options(datadist=NULL)
# }
```

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