Message-ID: <dd92a18a-92d0-49c4-f37c-c185b97af299@gmail.com>
Date: 2019-05-03T02:53:24Z
From: Ben Bolker
Subject: What p value should I report here?
In-Reply-To: <CAGP99JHO51FpxRDDbH+d1tpg65YBdzPZtXc8eH7NuzM40spE8w@mail.gmail.com>
What we'd like to see is the *results* of summary(Vert_effect) and
summary(model.frame(glmm_Vert_effect)) ... for example, if I was running
the first example in ?lmer, the desired output would look something like
this (here, the two outputs are identical because there are no NA values
in the input).
> library(lme4)
Loading required package: Matrix
> fm1 <- lmer(Reaction~Days+(1|Subject), sleepstudy)
> summary(sleepstudy)
Reaction Days Subject
Min. :194.3 Min. :0.0 308 : 10
1st Qu.:255.4 1st Qu.:2.0 309 : 10
Median :288.7 Median :4.5 310 : 10
Mean :298.5 Mean :4.5 330 : 10
3rd Qu.:336.8 3rd Qu.:7.0 331 : 10
Max. :466.4 Max. :9.0 332 : 10
(Other):120
> summary(model.frame(fm1))
Reaction Days Subject
Min. :194.3 Min. :0.0 308 : 10
1st Qu.:255.4 1st Qu.:2.0 309 : 10
Median :288.7 Median :4.5 310 : 10
Mean :298.5 Mean :4.5 330 : 10
3rd Qu.:336.8 3rd Qu.:7.0 331 : 10
Max. :466.4 Max. :9.0 332 : 10
(Other):120
On 2019-05-02 10:51 p.m., DESPINA MICHAILIDOU wrote:
> The code is
>
> glmm_Vert_effect?<- glmer(Vert_effect?~ P-Diz-today?+ (1|
> ID/SCAN_DATE/Side), data=GCA_data, family=binomial(link= "logit"))
>
> summary(Vert_effect).
>
>
> That is your question?
>
>
> I am sorry i am very new to R.
>
>
> Thank you for your interest and help. Really appreciate it.
>
>
> Despina
>
>
> ???? ???, 2 ??? 2019 ???? 10:40 ?.?., ?/? Ben Bolker <bbolker at gmail.com
> <mailto:bbolker at gmail.com>> ??????:
>
>
> ? We will be able to help much better if you can provide a reproducible
> example, or at least the results of the summary() commands requested
> below ...
>
> On 2019-05-02 10:29 p.m., DESPINA MICHAILIDOU wrote:
> > Yes you are correct. I have a 0 or 1 scoring outcome. I do have some
> > blanks in my observations.? Thank you for your response.
> >
> > Despina
> >
> > ???? ???, 2 ??? 2019 ???? 10:19 ?.?., ?/? Ben Bolker
> <bbolker at gmail.com <mailto:bbolker at gmail.com>
> > <mailto:bbolker at gmail.com <mailto:bbolker at gmail.com>>> ??????:
> >
> >
> >? ? ?? Can you show us summary(GCA_data) and
> >? ? ?summary(model.frame(fitted_model)) please? It looks like for
> some reason
> >? ? ?(maybe because of observations dropped due to NA values?) you
> have no
> >? ? ?variation in your predictor variable (P_Diz_today).
> >
> >? ? ?? It's also potentially problematic that you have an
> observation-level
> >? ? ?random effect for a Bernoulli outcome (i.e., you're fitting a
> binomial
> >? ? ?model with a single-column value as the response and no weights=
> >? ? ?argument, which implies you have a 0/1 outcome; you have the
> same number
> >? ? ?of groups in your fully nested [ID:Scan:Side] random effect as
> >? ? ?observations), but I don't think this would lead to the
> dropping of the
> >? ? ?P_Diz_today predictor ...
> >
> >? ? ?? cheers
> >? ? ?? ? Ben Bolker
> >
> >? ? ?On 2019-05-02 3:30 p.m., DESPINA MICHAILIDOU wrote:
> >? ? ?> Hi everyone,
> >? ? ?>
> >? ? ?>
> >? ? ?> I am running this regression analysis model and I get the
> >? ? ?following output.
> >? ? ?> What P value should I report for my variable P-Dizz today?What
> >? ? ?does it mean
> >? ? ?> that fixed-effect model matrix is rank deficient so dropping 1
> >? ? ?column /
> >? ? ?> coefficient? Can anyone help me with the interpretation of
> those data?
> >? ? ?>
> >? ? ?>
> >? ? ?> Thank you in advance.
> >? ? ?>
> >? ? ?>
> >? ? ?> Despina
> >? ? ?>
> >? ? ?>
> >? ? ?> Generalized linear mixed model fit by maximum likelihood
> (Laplace
> >? ? ?> Approximation) ['glmerMod']
> >? ? ?>
> >? ? ?>? Family: binomial? ( logit )
> >? ? ?>
> >? ? ?> Formula: Vert_effect ~ P_Diz_today + (1 | ID/SCAN_DATE/Side)
> >? ? ?>
> >? ? ?>? ? Data: GCA_data
> >? ? ?>
> >? ? ?>
> >? ? ?>
> >? ? ?>? ? ? AIC? ? ? BIC? ?logLik deviance df.resid
> >? ? ?>
> >? ? ?>? ? ?80.3? ? ?94.5? ? -36.1? ? ?72.3? ? ? 254
> >? ? ?>
> >? ? ?>
> >? ? ?>
> >? ? ?> Scaled residuals:
> >? ? ?>
> >? ? ?>? ? ? ?Min? ? ? ? 1Q? ? Median? ? ? ? 3Q? ? ? ?Max
> >? ? ?>
> >? ? ?> -0.012501 -0.000639 -0.000639 -0.000639? 0.105723
> >? ? ?>
> >? ? ?>
> >? ? ?>
> >? ? ?> Random effects:
> >? ? ?>
> >? ? ?>? Groups? ? ? ? ? ? ? Name? ? ? ? Variance Std.Dev.
> >? ? ?>
> >? ? ?>? Side:(SCAN_DATE:ID) (Intercept) 1502.7? ?38.76
> >? ? ?>
> >? ? ?>? SCAN_DATE:ID? ? ? ? (Intercept)? ? 0.0? ? 0.00
> >? ? ?>
> >? ? ?>? ID? ? ? ? ? ? ? ? ? (Intercept)? 235.1? ?15.33
> >? ? ?>
> >? ? ?> Number of obs: 258, groups:? Side:(SCAN_DATE:ID), 258;
> >? ? ?SCAN_DATE:ID, 130;
> >? ? ?> ID, 52
> >? ? ?>
> >? ? ?>
> >? ? ?>
> >? ? ?> Fixed effects:
> >? ? ?>
> >? ? ?>? ? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
> >? ? ?>
> >? ? ?> (Intercept)? -14.711? ? ? 3.646? -4.035 5.47e-05 ***
> >? ? ?>
> >? ? ?> ---
> >? ? ?>
> >? ? ?> Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
> >? ? ?>
> >? ? ?> fit warnings:
> >? ? ?>
> >? ? ?> fixed-effect model matrix is rank deficient so dropping 1
> column /
> >? ? ?> coefficient
> >? ? ?>
> >? ? ?> convergence code: 0
> >? ? ?>
> >? ? ?> boundary (singular) fit: see ?isSingular
> >? ? ?>
> >? ? ?>? ? ? ?[[alternative HTML version deleted]]
> >? ? ?>
> >? ? ?> _______________________________________________
> >? ? ?> R-sig-mixed-models at r-project.org
> <mailto:R-sig-mixed-models at r-project.org>
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> >? ? ?>
> >
> >? ? ?_______________________________________________
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> >
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