R help - Apr 2012 - How does survreg ordered factors vs not ordered factors?

Consider the following generic code for a survival model

survobj<-Surv(data$Time,data$Satisfactory)
survmodel<-survreg(survobj~x1+x2+x3+x4+x5+x6, data=data,
dist="weibull")
survsum<-summary(survmodel)
survsum

My question: Does anyone know what exactly survreg() does differently if

x1<-factor(data$x1, ordered=TRUE)
x2<-factor(data$x2, ordered=TRUE)

vs. 

x1<-factor(data$x1)
x2<-factor(data$x2)

Thanks,

William

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On Apr 23, 2012, at 8:29 AM, wwreith wrote:
> Consider the following generic code for a survival model
> 
> survobj<-Surv(data$Time,data$Satisfactory)
> survmodel<-survreg(survobj~x1+x2+x3+x4+x5+x6, data=data,
dist="weibull")
> survsum<-summary(survmodel)
> survsum
> 
> My question: Does anyone know what exactly survreg() does differently if
> 
> x1<-factor(data$x1, ordered=TRUE)
> x2<-factor(data$x2, ordered=TRUE)
> 
> vs. 
> 
> x1<-factor(data$x1)
> x2<-factor(data$x2)
> 
> Thanks,
> 
> William

You might want to Google search for "Orthogonal Polynomial Contrasts",
which is what you get by default in R for ordered factors and that will apply
not just for survreg, but for all typical modeling functions in R (lm, glm,
etc.). There is a page here that might be helpful:

  http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm

and it is also covered, albeit briefly, in An Introduction to R:

  http://cran.r-project.org/doc/manuals/R-intro.html#Contrasts

as well as ?contr.poly. Briefly, it allows for an analysis/exploration of linear
and higher order polynomial trends in the factor in relation to the response
variable, which would be more typical for an ordinal, as compared to a nominal,
independent variable.

For unordered factors, the default in R is to use what are called treatment
contrasts, which compares each level of the factor with the base or reference
level. Depending upon the nature of the analysis you are conducting and your
underlying hypotheses, treatment contrasts are very commonly used for ordinal
variables as well.

Regards,

Marc Schwartz