That's a bit harsh.
Isn't the best advice here, to post a reproducible example...
Which I believe has been mentioned.
Also, I'd strongly encourage people to use package+function name, for
this sort of thing.
stats::glm
As there are many R functions for GLMs...
On Sun, Aug 2, 2020 at 12:47 PM Rolf Turner <r.tur...@auckland.ac.nz> wrote:
>
>
> On 2/08/20 5:39 am, Paul Bernal wrote:
>
> > 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,
>
>
> There have been many responses to this post, the majority of them being
> confusing and off the point.
>
> BOTTOM LINE: R/glm() does *NOT* complain that one "should make the
> dependent variable a factor". This is bovine faecal output.
>
> As Rui Barradas has pointed out (alternatively: RTFM!) when you fit a
> Bernoulli model using glm(), your response/dependent variable is allowed
> to be
>
> * a numeric variable with values 0 or 1
> * a logical variable
> * a factor with two levels
>
> The OP presumably fed glm() a *character* vector with values "0" and
> "1". Doing *this* will cause glm() to whinge.
>
> I reiterate: RTFM!!! (And perhaps learn to distinguish between
> character vectors and factors.)
>
> cheers,
>
> Rolf Turner
>
> --
> Honorary Research Fellow
> Department of Statistics
> University of Auckland
> Phone: +64-9-373-7599 ext. 88276
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
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