rcode

cci <- read.csv("cci.csv", sep=",", header = T)
dim(cci)
head(cci)  
str(cci)

library(moonBook)
table1 <- mytable(death_1yr~.-patientsID, show.total = T, method = 3, max.ylev = 20, data=cci)
table1

library(car)

fit <- glm(data = cci, death_1yr~.-patientsID-DM2)
sqrt(vif(fit))>1.4

fit <- glm(data = cci, death_1yr~.-patientsID-DM2, family = 'binomial')
fit.od <- glm(data = cci, death_1yr~.-patientsID-DM2, family = 'quasibinomial')
pchisq(summary(fit.od)$dispersion * fit$df.residual, fit$df.residual, lower = F)

result_glm <- glm(data = cci, death_1yr~.-patientsID-DM2)
summary(result_glm)

result_glm <- step(result_glm, directon ="backward")
summary(result_glm)

names(cci)

full.model <-glm(death_1yr~.-patientsID-DM2, data = cci)
reduced.model <- step(full.model, direction = "backward")

min.model <- glm(death_1yr~1, data = cci)
fwd.model <- step(min.model, direction = "forward", 
                  scope = (death_1yr ~ sex+agegroup+DM1+PAD+CHF+OMI+COPD+
                             liver1+CKD+CVD+rheuma+ulcer+dementia+malig+
                             meta+hemi+HIV))
summary(reduced.model)
summary(fwd.model)

library(leaps)

leaps <- regsubsets(death_1yr ~ sex+agegroup+DM1+PAD+CHF+OMI+COPD+
                             liver1+CKD+CVD+rheuma+ulcer+dementia+malig+
                             meta+hemi+HIV, data = cci, nbest = 10)
plot(leaps, scale='adjr2', main='Adjusted R^2')

result_1 <- glm(data = cci, death_1yr~agegroup+OMI+dementia+malig+meta)
summary(result_1)

ORtable=function(x,digits=2){
    suppressMessages(a<-confint(x))
    result=data.frame(exp(coef(x)),exp(a))
    result=round(result,digits)
    result=cbind(result,round(summary(x)$coefficient[,4],3))
    colnames(result)=c("OR","2.5%","97.5%","p")
    result
}
exp(result_1$coefficients)
ORtable(result_1)
ORplot(result_1,type=2,show.OR=T,show.CI=T)

result_2 <- glm(data = cci, death_1yr~malig*meta+agegroup+OMI+dementia+malig+meta)
summary(result_2)