Plot the odds ratios (ORs) for a specific covariate from different logistic regression models

library(Hmisc)
library(Design)

x<-read.table("samplefile.txt", header=TRUE)
x<-upData(x,
   labels=c(age="Age", race="Race", sex="Sex",
      weight="Weight", visits="No. of Visits",
      tx="Treatment"),
   levels=list(sex=c("Female", "Male"),
      race=c("Black", "Caucasian", "Other"),
      tx=c("Drug X", "Placebo")),
   units=c(age="years", weight="lbs."))
contents(x)

# Generate an outcome variable
x<-upData(x,
   # Specify population model for log odds that Y=1
   L = .4*(sex=='Male') + .045*(age-50) +
      (log(weight - 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(x) ; options(datadist='ddist')

fit1 <- lrm(y ~ visits + sex * (age + rcs(weight,4)), data = x, x=TRUE, y=TRUE)
fit2 <- lrm(y ~ visits + tx + sex * (age + rcs(weight,4)), data = x, x=TRUE, y=TRUE)
fit3 <- lrm(y ~ sex * (age + rcs(weight,4)), data = x, x=TRUE, y=TRUE)

summfit1 <- summary(fit1, sex = NA, est.all = FALSE)[2, 
   c("Effect", "Lower 0.95", "Upper 0.95")]
summfit2 <- summary(fit2, sex = NA, est.all = FALSE)[2, 
   c("Effect", "Lower 0.95", "Upper 0.95")]
summfit3 <- summary(fit3, sex = NA, est.all = FALSE)[2, 
   c("Effect", "Lower 0.95", "Upper 0.95")]
plotinfo <- data.frame(rbind(summfit1, summfit2, summfit3), model = 1:3)

with(plotinfo, {
   plot(Effect ~ model, axes = FALSE, xlab = "Model", ylab = "OR", 
      ylim = c(min(Lower.0.95), max(Upper.0.95)))
   axis(side = 2)
   axis(side = 1, at = 1:3, labels = paste("Model", 1:3))
   box()
   arrows(x0 = 1:3, x1 = 1:3, y0 = Lower.0.95, y1 = Upper.0.95, code = 3, angle = 90) 
})