I have this model I want to run that has no main treatment effect because I subsetted the data just to look at this one treatment (For example I have 1200 points from three treatments: control, shelter and water, and this is only shelter).
stdsheltercox <- coxph(Surv(Time, Event, type = c('right')) ~ st.Area+st.Growth_Rate+st.SLA+st.VLA+st.Thickness+PD10, data = SMeans) Warning messages: 1: In coxph(Y ~ X[, assign <= alevels[i]]) : X matrix deemed to be singular; variable 5 2: In coxph(Y ~ X[, assign <= alevels[i]]) : X matrix deemed to be singular; variable 5 6 3: In coxph(Y ~ X[, assign <= alevels[i]]) : X matrix deemed to be singular; variable 5 6 7
It doesn't matter what I do, if I put more than five covariates the following covariates are always deemed singular
I am wondering how I can see the effect of covariates if I have no main treatment for all covariates? I have already run a Pearson's correlation to reduce colinearity - but either way I can't put in over. I read that was because these later variables that are deemed singular have no effect - but that can't be true because if I run them by themselves they work and are significant
You can see what I mean with the plots above
The dataset is below but it exceeded the character limit so I uploaded it to this link: dataset