I've seen several websites say that the function rq() from the package
quantreg can be used to do least absolute deviation regression. How do
you go about doing this and what's the connection between quantile
regression and LAD? (I'm very new to the former topic.)
Thanks,
Jane
--
-
The short answer is that what you seem to want is the rq() default with
tau not specified. (Default is tau=.5).
In general rq() minimizes a sum of weighted absolute residuals. The
weights depend on tau (the conditional quantile of
interest), and turn out to be equal with tau = 0.5, i.e.,
If this isn't already answered:
I don't quite understand the question: what do you mean by do a
complete data set from an object in R? What do you mean by the
subsetting is dangerous ... as you need to specify the levels for all
your factors again?
(What do your 3000 columns of data
Dear List,
Earlier this year on an (undoubtedly ill-advised) lark I coded up
an R version of TWINSPAN. It's far from a polished package at this
point, but the code does run. One of the interesting features is that
you can partition a PCO or NMDS in addition to the traditional CA. To be
Thank you very much Ben.
I was doing an analysis of indicator species with the subset data and
the other levels were still in my subset data and the analysis was
considering them in the analysis.
My 3000 columns are plant species presence/absence type of data.
Best,
Manuel
On 26/04/2011
On 27/04/11 00:40 AM, Dave Roberts dvr...@ecology.msu.montana.edu wrote:
Earlier this year on an (undoubtedly ill-advised) lark I coded up
an R version of TWINSPAN. It's far from a polished package at this
point, but the code does run. One of the interesting features is that
you can
