Pre0 <-rms::Predict(fit,Hb,fun=exp,type="predictions",ref.zero=TRUE,conf.int = 0.95,digits=2);
##Where fun is the conversion function
ggplot(Pre0)
View(Pre0)
ggplot()+
geom_line(data=Pre0,
aes(Hb,yhat,colour=gender),alpha=0.7)+
scale_color_nejm()+ ##Using England in the ggsci package, other colors are also available
geom_ribbon(data=Pre0,
aes(Hb, ymin=lower,ymax=upper,fill=gender),alpha=0.1)+
scale_fill_nejm()+
geom_hline(yintercept=1,linetype=2,size=0.75)
as.factor
converts the sex variable into a factor;
Pre0 <-rms::Predict(fit,Hb,fun=exp,type="predictions",ref.zero=TRUE,conf.int = 0.95,digits=2);
##Where fun is the conversion function
ggplot(Pre0)
View(Pre0)
ggplot()+
geom_line(data=Pre0,
aes(age,yhat,colour=as.factor(sex)),alpha=0.7)+
scale_color_nejm()+ ##Using England in the ggsci package, other colors are also available
geom_ribbon(data=Pre0,
aes(age, ymin=lower,ymax=upper,fill=as.factor(sex)),alpha=0.1)+
scale_fill_nejm()+
geom_hline(yintercept=1,linetype=2,size=0.75)
ggplot(mtcars, aes(x=wt, y=mpg, color=as.factor(cyl), shape=as.factor(cyl))) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
scale_shape_manual(values=c(3, 16, 17))+
scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
theme(legend.position="top")