[R-sig-ME] Imputation methods mixed model analysis

[Thierry Onkelinx]

Dear Sam,

Don't use the data.frame$ notation in your formula. Just use the code below.

preCORT_ICC <- lmer(Cortisol_pre ~ 1 + (1 | ID), data = Data_F_long)

Which values are missing: Cortisol_pre or ID?

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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www.inbo.be

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Op do 16 apr. 2020 om 15:34 schreef Breugelmans, S. (Sara) <
S.Breugelmans using student.ru.nl>:

> Dear colleagues,
>
>
> For my thesis I am conducting a mixed model analysis on longitudinal data.
> However, when I tried to run an intercept-only model I got the following
> error:
>
>
> preCORT_ICC <- lmer(Data_F_long$Cortisol_pre ~ 1 + (1 | Data_F_long$ID),
> data = Data_F_long)
> Error in KhatriRao(sm, t(mm)) : (p <- ncol(X)) == ncol(Y) is not TRUE
>
> ?
>
> When I searched this error I saw that it might have to do something with
> the number of NA's. So then I thought it would be better to use some kind
> of imputation strategy.
>
> I was wondering if there is a build-in function in lmer() to do this. Or
> is it better to manually impute the data before analysing.
>
>
> I already found a function called mice(). Does anyone of you have
> experience with mice() and would you recommend using it?
>
>
> Of course it would be more convenient if I could use some kind of build-in
> function in lmer().
>
>
> Thank you for your response!
>
>
> Kind regards
>
>
> Sam
>
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>
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