It looks like the first part of the SAS code is generating an ARIMA model, which you can do in R with the arima function. The arima function has an order argument, which is a vector containing the AR, I, and MA (autoregressive, integration, and moving average) portions of the ARIMA model specification. arima also has a seasonal argument for specifying the seasonal portion of the model. Run ?arima to bring up the help file, which has detailed information on using the arima function.
It looks like the second part of the code is providing information on the model coefficients and goodness of fit statistics. The object output by the arima function will have the model coefficients and some fit statistics as well, but I don't know if it will have the specific output you're looking for. Try generating an ARIMA model with a built-in data set and you can see what's available. For example, with the built in lh time series data set:
# Create model
x = arima(lh, order = c(3,0,2))
# Print to the console a summary of the model output
x
# Get model coefficients
coef(x)
# Look at structure of model object returned by arima function
str(x)
There are almost certainly additional R functions for generating additional model diagnostics, and you can also run tsdiag(x), which will output some diagnostic plots of the model residuals.
I'm not familiar with the Roll model and have only basic knowledge of time series analysis, but here and here are an ARIMA tutorial and a free online book about forecasting, respectively, that use R and will hopefully help you get started.