In cardiomoon/autoReg: Automatic Linear and Logistic Regression and Survival Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = NA
)

You can make a bootstrap simulation for model prediction. When writing bootPredict function and this vignette, I was inspired from package finalfit by Ewen Harrison. For example, you can predict survival after diagnosis of breast cancer.

library(autoReg)
library(dplyr)  # for use `%>%`
data(GBSG2,package="TH.data")
head(GBSG2)

Data GBGS2 in TH.data package is a data frame containing the observations from the German Breast Cancer Study Group 2. In this data, the survival status of patients is coded as 0 or 1 in the variable cens. Whether the patient receive the hormonal therapy or not is recorded as 'no' or 'yes' in variable horTh. The number of positive lymph nodes are recoded in pnodes. You can make a logistic regression model with the following R code.

GBSG2$cens.factor=factor(GBSG2$cens,labels=c("Alive","Died"))
fit=glm(cens.factor~horTh+pnodes+menostat,data=GBSG2,family="binomial")
summary(fit)

You can make a publication-ready table with the following R command.

autoReg(fit) %>% myft()

You can draw a plot summarizing the model.

modelPlot(fit)

For bootstrapping simulation, you can make new data with the following R code.

newdata=expand.grid(horTh=factor(c(1,2),labels=c("no","yes")),
                    pnodes=1:51,
                    menostat=factor(c(1,2),labels=c("Pre","Post")))

You can make a bootstrapping simulation with bootPredict() function. You can set the number of simulation by adjusting R argument.

df=bootPredict(fit,newdata,R=500)
head(df)

With this result, you can draw a plot showing bootstrapping prediction of breast cancer.

library(ggplot2)
ggplot(df,aes(x=pnodes,y=estimate))+
  geom_line(aes(color=horTh))+
  geom_ribbon(aes(ymin=lower,ymax=upper,fill=horTh),alpha=0.2)+
  facet_wrap(~menostat)+
  theme_bw()+
  labs(x="Number of positive lymph nodes", y="Probability of death")

cardiomoon/autoReg documentation built on Dec. 10, 2023, 1:59 a.m.