On Thu, Aug 6, 2009 at 17:02, Pierre GM<pgmdevlist@gmail.com> wrote:
On Aug 6, 2009, at 5:49 PM, Robert Kern wrote:
On Thu, Aug 6, 2009 at 16:43, Pierre GM<pgmdevlist@gmail.com> wrote:
Even if the scale is simply discarded already, using a location will probably NOT give the expected result
It depends on what your expectations are. For the discrete distributions, all the loc parameter means is this, as documented:
pmf(x; loc) -> pmf(x-loc)
That's it. I don't know why you would expect anything else.
Because using a location parameter, you change the support domain. Back to the example of a Poisson distribution with loc=1, the support domain is now x>=1, which amounts to truncating the zeroes.
I don't understand why you go through all of these contortions. It does not amount to truncation at all. It just shifts the distribution.
The mean of a zero-truncated Poisson with parameter pr should be pr/(1-exp(- pr)), but we end up with pr+1. Not the expected result.
Because you are expecting that the operation is equivalent to something that it is not. pmf(x; loc) -> pmf(x-loc) Nothing more. It is definitely *not* the same thing as setting all x<loc to 0 and renormalizing. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco