[R] factors in probit regression
Daniel Malter
daniel at umd.edu
Fri Oct 7 07:32:56 CEST 2011
I need to quote David Winsemius on this one again: "The advancement of
science would be safer if you knew what you were doing."
Note that the whole model screams at you that it is wrongly modeled. You are
running a fully interacted model with factor variables. Thus, you have 19
regressors plus the baseline for 150 observations. Note that all your
coefficients are insignificant with a z-value of 0 and a p-value of 1. This
indicates that something is severely wrong with your model. And it is not
difficult to tell what. If you look at the residual deviance, it is
effectively zero. This means that you are overfitting the model. Your model
explains fully (with no error), whether the dependent variable is a zero or
a one. This may be meaningful in a descriptive but not in an inferential
sense.
Also, there are no "Control" coefficients or interactions because modeling
three factor levels only requires two dummy variables. The other one becomes
the omitted baseline that is absorbed in the intercept. That is, the
intercept and the "plain" interaction terms capture that group. Please pick
up an introductory econometrics book before continue.
Best,
Daniel
garciap wrote:
>
> Hi to all of you,
>
> I'm fitting an full factorial probit model from an experiment, and I've
> the independent variables as factors. The model is as follows:
>
>
> fit16<-glm(Sube ~ as.factor(CE)*as.factor(CEBO)*as.factor(Luz),
> family=binomial(link="probit"), data=experimento)
>
> but, when I took a look to the results I've obtained the following:
>
> glm(formula = Sube ~ CE * CEBO * Luz, family = binomial(link = "probit"),
> data = experimento)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -1.651e-06 -1.651e-06 1.651e-06 1.651e-06 1.651e-06
>
> Coefficients: (3 not defined because of singularities)
> Estimate Std. Error z value
> Pr(>|z|)
> (Intercept) 6.991e+00 3.699e+04 0
> 1
> CEexperimental 5.357e-09 4.775e+04 0
> 1
> CENO -1.398e+01 4.320e+04 0
> 1
> CEBOcombinado 4.948e-26 4.637e+04 0
> 1
> CEBOolor 1.183e-25 4.446e+04 0
> 1
> CEBOvisual 7.842e-26 5.650e+04 0
> 1
> Luzoscuridad 3.383e-26 4.637e+04 0
> 1
> CEexperimental:CEBOcombinado -6.227e-26 6.656e+04 0
> 1
> CENO:CEBOcombinado -3.758e-26 5.540e+04 0
> 1
> CEexperimental:CEBOolor -2.611e-25 6.865e+04 0
> 1
> CENO:CEBOolor -5.252e-26 5.620e+04 0
> 1
> CEexperimental:CEBOvisual -2.786e-09 7.700e+04 0
> 1
> CENO:CEBOvisual 8.169e-15 6.334e+04 0
> 1
> CEexperimental:Luzoscuridad -1.703e-25 6.304e+04 0
> 1
> CENO:Luzoscuridad -1.672e-28 6.117e+04 0
> 1
> CEBOcombinado:Luzoscuridad 1.028e-26 5.950e+04 0
> 1
> CEBOolor:Luzoscuridad 9.212e-27 6.207e+04 0
> 1
> CEBOvisual:Luzoscuridad NA NA NA
> NA
> CEexperimental:CEBOcombinado:Luzoscuridad 9.783e-26 8.744e+04 0
> 1
> CENO:CEBOcombinado:Luzoscuridad -2.948e-26 7.959e+04 0
> 1
> CEexperimental:CEBOolor:Luzoscuridad 1.573e-25 9.005e+04 0
> 1
> CENO:CEBOolor:Luzoscuridad -2.111e-26 8.208e+04 0
> 1
> CEexperimental:CEBOvisual:Luzoscuridad NA NA NA
> NA
> CENO:CEBOvisual:Luzoscuridad NA NA NA
> NA
>
> (Dispersion parameter for binomial family taken to be 1)
>
> Null deviance: 2.0853e+02 on 150 degrees of freedom
> Residual deviance: 4.1146e-10 on 130 degrees of freedom
> AIC: 42
>
>
> Well, there are too many levels of the original factors lacking in this
> table. As an example, the factor CE has three levels (Undefined, Control,
> Experimental), but in the table there are only two of them (NO=undefined,
> Experimental=Experimental). I need to check the complete result, how can I
> obtain the effects for the remaining levels of the factors?
>
> Thanks,
>
> Pablo
>
Hi to all of you,
I'm fitting an full factorial probit model from an experiment, and I've the
independent variables as factors. The model is as follows:
fit16<-glm(Sube ~ as.factor(CE)*as.factor(CEBO)*as.factor(Luz),
family=binomial(link="probit"), data=experimento)
but, when I took a look to the results I've obtained the following:
glm(formula = Sube ~ CE * CEBO * Luz, family = binomial(link = "probit"),
data = experimento)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.651e-06 -1.651e-06 1.651e-06 1.651e-06 1.651e-06
Coefficients: (3 not defined because of singularities)
Estimate Std. Error z value
Pr(>|z|)
(Intercept) 6.991e+00 3.699e+04 0
1
CEexperimental 5.357e-09 4.775e+04 0
1
CENO -1.398e+01 4.320e+04 0
1
CEBOcombinado 4.948e-26 4.637e+04 0
1
CEBOolor 1.183e-25 4.446e+04 0
1
CEBOvisual 7.842e-26 5.650e+04 0
1
Luzoscuridad 3.383e-26 4.637e+04 0
1
CEexperimental:CEBOcombinado -6.227e-26 6.656e+04 0
1
CENO:CEBOcombinado -3.758e-26 5.540e+04 0
1
CEexperimental:CEBOolor -2.611e-25 6.865e+04 0
1
CENO:CEBOolor -5.252e-26 5.620e+04 0
1
CEexperimental:CEBOvisual -2.786e-09 7.700e+04 0
1
CENO:CEBOvisual 8.169e-15 6.334e+04 0
1
CEexperimental:Luzoscuridad -1.703e-25 6.304e+04 0
1
CENO:Luzoscuridad -1.672e-28 6.117e+04 0
1
CEBOcombinado:Luzoscuridad 1.028e-26 5.950e+04 0
1
CEBOolor:Luzoscuridad 9.212e-27 6.207e+04 0
1
CEBOvisual:Luzoscuridad NA NA NA
NA
CEexperimental:CEBOcombinado:Luzoscuridad 9.783e-26 8.744e+04 0
1
CENO:CEBOcombinado:Luzoscuridad -2.948e-26 7.959e+04 0
1
CEexperimental:CEBOolor:Luzoscuridad 1.573e-25 9.005e+04 0
1
CENO:CEBOolor:Luzoscuridad -2.111e-26 8.208e+04 0
1
CEexperimental:CEBOvisual:Luzoscuridad NA NA NA
NA
CENO:CEBOvisual:Luzoscuridad NA NA NA
NA
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2.0853e+02 on 150 degrees of freedom
Residual deviance: 4.1146e-10 on 130 degrees of freedom
AIC: 42
Well, there are too many levels of the original factors lacking in this
table. As an example, the factor CE has three levels (Undefined, Control,
Experimental), but in the table there are only two of them (NO=undefined,
Experimental=Experimental). I need to check the complete result, how can I
obtain the effects for the remaining levels of the factors?
Thanks,
Pablo
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