#COX REGRESSION

cox=read.table("C:\\Users\\Pam\\Google Drive\\Binghamton\\Classes\\Spring 2014\\Biostats with R\\Projects\\scoxreg.txt",header=T)
attach(cox)
#must install a package before you begin
  #survival
  #and splines

#factor the variables in question
Ftreat=factor(cox$Treat)
Fblock=factor(cox$block)
#this tells R that both treatment and block are factors

#Assign the rest of the variables
Day=cox$Day
Dead=cox$Dead
SampleSize=cox$SS

coxreg=coxph(Surv(Day,Dead)~Fblock+Ftreat+SampleSize)
coxreg
#create a model with y=surv, based on day and number dead flies. The variables that are being compared are block, treatment, and sample size
#creating this model compares all treatments against the control treatment (treat 1)
#it also compares block 1 against block 2
#in addition, it compares samplesize 1 against sample size 2
#because the p values of all these parts of the model are less tha 0.05, this suggests that they all contribute to the model and should be kept in
#if this was not the case and there was a variable with a p value higher than 0.05, this means that the model can probably be run without that variable

summary(coxreg)
#the summary functions shows pairwise t-tests of the different variables, saying which are significantly different from the controls in each case