# Cox PH
#install.packages("survival", dependencies=TRUE)
# Now load the dataset into R with the following command:

mydataframe <- read.delim("http://www.robin-beaumont.co.uk/virtualclassroom/book2data/survival_collett_p290.dat", header=TRUE)
library(survival)
names(mydataframe)
attach(mydataframe)
mydataframe
# Now create the cox regression model:
my_coxph<- coxph(Surv(time,cens) ~ treat + age, data=mydataframe)
summary(my_coxph)

###################
# alternatively if you have carried out the previous chapters r code you
# will have a Surv object called mysurv from the line: 
# mysurv <- Surv(time, cens)
# so the above formula could be changed to:
# my_coxph<- coxph(mysurv ~ treat + age, data=mydataframe)
###################
###	Log Likelihood ratio test
my_coxphnull <- coxph(formula = mysurv ~ 1, mydataframe)
summary(my_coxphnull)
my_coxphnull$loglik
-2*my_coxphnull$loglik

anova(my_coxph)
### showing iteration details
fit0 <- coxph(formula = mysurv ~ treat + age, data=mydataframe, iter=0)
fit0
# This indicates that the initial values for both betas are set to 0.
# To find the beta values for the first iteration we use the iter=1 setting:
fit1 <- coxph(formula = mysurv ~ treat + age, data=mydataframe, iter=1)
fit1
fit2 <- coxph(formula = mysurv ~ treat + age, data=mydataframe, iter=1, init=fit1$coef)
fit2
fit3 <- coxph(formula = mysurv ~ treat + age, data=mydataframe, iter.max = 50)
fit3
## with iteration instead, explained in the chapter:
fit_test <- fit0
for (i in 0:my_coxph$iter) {	
fit_test2 <- coxph(formula = mysurv ~ treat + age, data=mydataframe, iter=1, init=fit_test$coef)
cat(fit_test2$loglik[2],"   ", fit_test2$loglik[1],"   ",fit_test2$loglik[2] - fit_test2$loglik[1],"   ", "\n") 
fit_test <- fit_test2
}