density vs. mass for discrete probability functions
Thank you Peter and Spencer. That clears things up. Also since no one responded the second part of my question, I'm still wondering if it was noted that there is a hyperlink in the dbinom help file (?dbinom) that isn't directing correctly? Stefan
On Fri, Mar 15, 2019, 07:37 peter dalgaard, <pdalgd at gmail.com> wrote:
Mathematically, you can bring discrete and continuous distributions on a
common footing by defining probability functions as densities wrt. counting
measure. You don't really need Radon-Nikodym derivatives to understand the
idea, just the fact that sums can be interpreted as integrals wrt counting
measure, hence sum_{x in A} f(x) and int_A f(x) dx are essentially the same
concept.
-pd
On 15 Mar 2019, at 01:43 , Stefan Schreiber <sschreib at ualberta.ca>
wrote:
Dear R users, While experimenting with the dbinom() function and reading its documentation (?dbinom) it reads that "dbinom gives the density" but shouldn't it be called "mass" instead of "density"? I assume that it has something to do with keeping the function for "density" consistent across discrete and continuous probability functions - but I am not sure and was hoping someone could clarify? Furthermore the help file for dbinom() function references a link (http://www.herine.net/stat/software/dbinom.html) but it doesn't seem to land where it should. Maybe this could be updated? Thank you, Stefan
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