I have two frequency tables, one representing observed data and one representing modeled data.
I am looking for a Goodness of Fit measure, checking whether the model data fits the observed data.
Problem is, my counts are rather small (most are smaller than 5), and so Pearson Goodness of Fit fails to operate on these data sets (I am using R).
R reports the following:
X-squared = Inf, df = 534, p-value < 2.2e-16
Should I use another Goodness of Fit measure? Any suggestions?
EDIT (providing some more info):
The model contains frequencies of "topics" that are associated with "documents" read by users in the last month.
The observed data contains "topics" that are associated with "documents" read by users in the last day.
(If "documents" are Music CDs, "topics" are musical Genres, for example)
I am trying to understand if the model (data over time) fits the observed data (data of last day). If the model fits the observed data, it should mean that users stay around the same "topics" (keep listening to the same musical genres).