[R] GAM model with interactions between continuous variables and factors

[Joshua Wiley]

Hi Antonio,

If wealth is a factor variable, you should include the main effect in
the model, as the smooths will be centered.

Cheers,

Josh



On Mon, Mar 25, 2013 at 6:09 PM, Antonio P. Ramos
<ramos.grad.student at gmail.com> wrote:
> Hi all,
>
> I am not sure how to handle interactions with categorical predictors in the
> GAM models. For example what is the different between these bellow two
> models. Tests are indicating that they are different but their predictions
> are essentially the same.
>
> Thanks a bunch,
>
>> gam.1 <- gam(mortality.under.2~ maternal_age_c+ I(maternal_age_c^2)+
> +                s(birth_year,by=wealth) +
> +                + wealth + sex +
> +                residence+ maternal_educ + birth_order,
> +              ,data=rwanda2,family="binomial")
>>
>> gam.2 <- gam(mortality.under.2~ maternal_age_c+ I(maternal_age_c^2)+
> +                s(birth_year,by=wealth) +
> +                 + sex +
> +                residence+ maternal_educ + birth_order,
> +              ,data=rwanda2,family="binomial")
>>
>> anova(gam.1,gam.2,test="Chi")
> Analysis of Deviance Table
>
> Model 1: mortality.under.2 ~ maternal_age_c + I(maternal_age_c^2) +
> s(birth_year,
>     by = wealth) + +wealth + sex + residence + maternal_educ +
>     birth_order
> Model 2: mortality.under.2 ~ maternal_age_c + I(maternal_age_c^2) +
> s(birth_year,
>     by = wealth) + +sex + residence + maternal_educ + birth_order
>   Resid. Df Resid. Dev      Df Deviance  Pr(>Chi)
> 1     28986      24175
> 2     28989      24196 -3.6952  -21.378 0.0001938 ***
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>> str(rwanda2)
> 'data.frame': 29027 obs. of  18 variables:
>  $ CASEID            : Factor w/ 10718 levels "        1  5  2",..: 289
> 2243 7475 9982 6689 10137 7426 428 8415 10426 ...
>  $ mortality.under.2 : int  0 1 0 0 0 0 0 0 1 0 ...
>  $ maternal_age_disct: Factor w/ 3 levels "-25","+35","25-35": 1 1 1 1 1 1
> 3 1 3 1 ...
>  $ maternal_age      : int  18 21 21 23 21 22 26 18 27 21 ...
>  $ time              : int  3 3 3 3 3 3 3 3 3 3 ...
>  $ child_mortality   : num  0.232 0.232 0.232 0.232 0.232 ...
>  $ democracy         : Factor w/ 1 level "dictatorship": 1 1 1 1 1 1 1 1 1
> 1 ...
>  $ wealth            : Factor w/ 5 levels "Lowest quintile",..: 2 4 1 4 5 1
> 4 1 4 5 ...
>  $ birth_year        : int  1970 1970 1970 1970 1970 1970 1970 1970 1970
> 1970 ...
>  $ residence         : Factor w/ 2 levels "Rural","Urban": 1 1 1 1 2 1 1 1
> 1 2 ...
>  $ birth_order       : int  1 2 2 5 1 1 3 1 2 2 ...
>  $ maternal_educ     : Factor w/ 4 levels "Higher","No education",..: 3 2 2
> 3 4 2 3 2 2 2 ...
>  $ sex               : Factor w/ 2 levels "Female","Male": 1 1 2 2 1 1 2 2
> 2 2 ...
>  $ quinquennium      : Factor w/ 7 levels "00-5's","70-4",..: 2 2 2 2 2 2 2
> 2 2 2 ...
>  $ time.1            : int  3 3 3 3 3 3 3 3 3 3 ...
>  $ new_time          : int  0 0 0 0 0 0 0 0 0 0 ...
>  $ maternal_age_c    : num  -6.12 -3.12 -3.12 -1.12 -3.12 ...
>  $ birth_year_c      : num  -14.8 -14.8 -14.8 -14.8 -14.8 ...
>
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>
>
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>



-- 
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com