[R-sig-ME] P-value associated to explanatory from glmer binomial family

[Mario Garrido]

 Dear Thierry,
thanks so much for the clarification. After I run the LRT I get those
results

gm1 <- glmer(Mycoplasma~ type+ (1|donor.number), family=binomial)

anova(gm1)

Analysis of Variance Table

     Df Sum Sq Mean Sq F value

type  1 3.2124  3.2124  3.2124

gm0<-  glmer(Mycoplasma~ 1+ (1|donor.number), family=binomial)

anova(gm1, gm0)

Data: NULL

Models:

gm0: Mycoplasma ~ 1 + (1 | donor.number)

gm1: Mycoplasma ~ type + (1 | donor.number)

         Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)

gm0  2 46.492 49.869 -21.246   42.492
gm1  3 44.901 49.968 -19.451   38.901 3.5905      1    0.05811 .


The F-value associated to  type, my only explanatory variable, is 3.124, as
anova(gm1) shows above

1. So which values should I take to calculate the P-value associated to the
variable type? 2 and 3 as shows  anova(gm1, gm0)
Is like that then?

1-pf(3.2124,2,3)

[1] 0.1795865

or

1-pf(3.2124,3,2)

[1] 0.246378


2.   anova(gm1, gm0) give a P-value associated of 0.058, since I have only
one explanatory variable, is not this value the defining the significance
of this variable (the ine that makes the difference between the 2 models)


3. What is in this case the F-value and df provided by anova(gm1)?



Srry, I am a little confused with the results. Thanks!


2018-05-24 11:55 GMT+03:00 Thierry Onkelinx <thierry.onkelinx using inbo.be>:

> Dear Mario,
>
> Calculating the degrees of freedom of a mixed model is not straightforward.
>
> A workaround would be to use a likelihoodratio test between two nested
> models: one with and one without the variable. See the example below.
>
> library(lme4)
> gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
>               data = cbpp, family = binomial)
> anova(gm1)
> gm0 <- glmer(cbind(incidence, size - incidence) ~ (1 | herd),
>               data = cbpp, family = binomial)
> anova(gm1, gm0)
>
>
> Best regards,
>
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
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> 2018-05-23 13:14 GMT+02:00 Mario Garrido <gaiarrido using gmail.com>:
>
>> Dear lme4-users,
>> I am trying to get the P-value associated with a glmer model from the
>> binomial family.
>> My model is the following:
>> glmer(Infection.status~origin+ (1|donationID), family=binomial)->q7H
>>
>> where Infection status is a dummy variable with two levels, infected and
>> uninfected
>> I tried to get the P-value associated to the the explanatory variable
>> origin
>> but I get only the F-value and the degrees of freedom
>>
>> (aov <- anova(q7H))
>> Analysis of Variance Table
>>          Df Sum Sq Mean Sq F value
>> origin   2 5.3061  2.6531  2.6531
>>
>> I have 2 different questions
>> 1. Am I doing correctly or am I using an incorrect command?
>>
>> 2. with the F-value I get and the df, should I go to test the significance
>> to a F or Chi-squared table? I guess I should go to the latest since I am
>> running a binomial test, right?
>> In case I have to go to an F table, how can I know the numerator and
>> denominator degrees of freedom?
>>
>> Thanks in advance
>>
>>         [[alternative HTML version deleted]]
>>
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>
>

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