Hello,
From the documentation, help('glm'):
Details
A typical predictor has the form|response ~ terms|where|response|is the
(numeric) response vector and|terms|is a series of terms which specifies
a linear predictor for|response|.
For|binomial|and|quasibinomial|families the response can also be
specified as a|factor
<http://127.0.0.1:11611/library/stats/help/factor>|(when the first level
denotes failure and all others success) or as a two-column matrix with
the columns giving the numbers of successes and failures. A terms
specification of the form|first + second|indicates all the terms
in|first|together with all the terms in|second|with any duplicates removed.
There is no need for the response to be a factor, it is optional, the
wording is very clear,
"For|binomial|and|quasibinomial|families the response *can* also be
specified as a|factor <http://127.0.0.1:11611/library/stats/help/factor>"|
And with binary, numeric responses I cannot reproduce the warning
message, the models fit silently.
Hope this helps,
Rui Barradas
Às 18:39 de 01/08/2020, Paul Bernal escreveu:
Dear friends,
Hope you are doing great. I want to fit a logistic regression in R, where
the dependent variable is the covid status (I used 1 for covid positives,
and 0 for covid negatives), but when I ran the glm, R complains that I
should make the dependent variable a factor.
What would be more advisable, to keep the dependent variable with 1s and
0s, or code it as yes/no and then make it a factor?
Any guidance will be greatly appreciated,
Best regards,
Paul
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