列线图，让逻辑回归更加贴近临床

n <- 1000 # define sample size set.seed(17) # so can reproduce the results d <- data.frame(age = rnorm(n, 50, 10), blood.pressure = rnorm(n, 120, 15), cholesterol = rnorm(n, 200, 25), sex = factor(sample(c('female','male'), n,TRUE))) # Specify population model for log odds that Y=1 # Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)] d <- upData(d, L = .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male')), y = ifelse(runif(n) < plogis(L), 1, 0)) ddist <- datadist(d); options(datadist='ddist') f <- lrm(y ~ lsp(age,50) + sex * rcs(cholesterol, 4) + blood.pressure, data=d) nom <- nomogram(f, fun=function(x)1/(1+exp(-x)), # or fun=plogis fun.at=c(.001,.01,.05,seq(.1,.9,by=.1),.95,.99,.999), funlabel="Risk of Death") #Instead of fun.at, could have specified fun.lp.at=logit of #sequence above - faster and slightly more accurate plot(nom, xfrac=.45) print(nom)