[R] Names of variables needed in newdata for predict.glm

[David Winsemius]

> On Mar 31, 2018, at 8:48 AM, Bendix Carstensen <bendix.carstensen at regionh.dk> wrote:
> 
> all.vars works fine, EXCEPT, it give a bit too much.
> I only want the regression variables, but in the following example I also get "k" the variable holding the chosen knots. Any machinery to find only "real" regression variables?
> cheers, Bendix
> 
> library( splines )
> y <- rnorm(100)
> x <- rnorm(100)
> k <- -1:1
> ml <-  lm( y ~ bs(x,knots=k) )
> mg <- glm( y ~ bs(x,knots=k) )
> all.vars(ml$terms)
> all.vars(mg$terms)
> all.vars(mg$formula)

If you allowed a requirement that "real" regression variables have been passed in a data argument, then this might succeed:

> ml <-  lm( y ~ bs(x,knots=k), data=dat )
> all.vars(ml$terms)
[1] "y" "x" "k"
> all.vars(ml$formula)
character(0)
> all.vars(ml$terms)[ all.vars(ml$terms) %in% names(dat)]
[1] "y" "x"

-- 
David.
> 

> ________________________________________
> Fra: Marc Girondot <marc_grt at yahoo.fr>
> Sendt: 8. marts 2018 06:26
> Til: Bendix Carstensen; r-help at r-project.org
> Emne: Re: [R] Names of variables needed in newdata for predict.glm
> 
> Hi,
> 
> Some try:
>> names(mi$xlevels)
> [1] "f"
>> all.vars(mi$formula)
> [1] "D" "x" "f" "Y"
>> names(mx$xlevels)
> [1] "f"
>> all.vars(mx$formula)
> [1] "D" "x" "f"
> 
> When offset is indicated out of the formula, it does not work...
> 
> Marc
> 
> Le 07/03/2018 à 06:20, Bendix Carstensen a écrit :
>> I would like to extract the names, modes [numeric/factor] and levels
>> of variables needed in a data frame supplied as newdata= argument to
>> predict.glm()
>> 
>> Here is a small example illustrating my troubles; what I want from
>> (both of) the glm objects is the vector c("x","f","Y") and an
>> indication that f is a factor:
>> 
>> library( splines )
>> dd <- data.frame( D = sample(0:1,200,rep=T),
>>                   x = abs(rnorm(200)),
>>                   f = factor(sample(letters[1:4],200,rep=T)),
>>                   Y = runif(200,0.5,10) )
>> mx <- glm( D ~ ns(x,knots=1:2,Bo=c(0,5)) + f:I(x^2) , offset=log(Y) , family=poisson, data=dd)
>> mi <- glm( D ~ ns(x,knots=1:2,Bo=c(0,5)) + f:I(x^2) + offset(log(Y)), family=poisson, data=dd)
>> 
>> attr(mx$terms,"dataClasses")
>> attr(mi$terms,"dataClasses")
>> mi$xlevels
>> mx$xlevels
>> 
>> ...so far not quite there.
>> 
>> Regards,
>> 
>> Bendix Carstensen
>> 
>> Senior Statistician
>> Steno Diabetes Center
>> Clinical Epidemiology
>> Niels Steensens Vej 2-4
>> DK-2820 Gentofte, Denmark
>> b at bxc.dk
>> bendix.carstensen at regionh.dk
>> http://BendixCarstensen.com
>> 
>> ________________________________
>> 
>> 
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>> 
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>> 
> 
> 
> ________________________________
> 
> 
> Denne e-mail indeholder fortrolig information. Hvis du ikke er den rette modtager af denne e-mail eller hvis du modtager den ved en fejltagelse, beder vi dig venligst informere afsender om fejlen ved at bruge svarfunktionen. Samtidig bedes du slette e-mailen med det samme uden at videresende eller kopiere den.
> 
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

David Winsemius
Alameda, CA, USA

'Any technology distinguishable from magic is insufficiently advanced.'   -Gehm's Corollary to Clarke's Third Law