[Lme4-authors] LME4 and Lmer model
The BY statement in SAS has nothing to do with BLUP, it simply runs an analysis separately for each group defined by BY. The empirical BLUP predicts the values of the random effect for each group taking into consideration all the data. There is a command in SAS for extracting the random effect predictions which I would hope gives the same results as ranef in R. Ken
On 30 September 2014 07:19, Zhanyou Xu <zxuiowa at iastate.edu> wrote:
Dear Sir/Madam, We are running lmer to calculate Best Linear Unbiased prediction (BLUP), and we have 10 groups of data, each group has groupID and 42 individual. We want to get BLUP values for each individual material within each group. In SAS, there is a BY statement and we can get BLUP value for each individuals within a group. We are wondering whether there is a similar R code that we can calculate the BLUPs for each group similar like BY statement in SAS? Below is the R code we pare testing for your reference. Thank you very much in advance! Zhanyou ---------- Forwarded message ---------- From: Ben Bolker <bbolker at gmail.com> Date: Mon, Sep 29, 2014 at 2:05 PM Subject: Re: [Lme4-authors] LME4 and Lmer model To: zhanyou.xu at syngenta.com, "lme4-authors at lists.r-forge.r-project.org" <lme4-authors at lists.r-forge.r-project.org>, marcia.almeida_de_macedo at syngenta.com, zxuiowa at iastate.edu, lme4-authors at r-forge.wu-wien.ac.at On 14-09-29 11:30 AM, zhanyou.xu at syngenta.com wrote:
Dear Ben and all LME4 authors, We are running lmer to calculate Best Linear Unbiased prediction (BLUP), and we have 10 groups of data, each group has groupID and 42 individual. We want to get BLUP values for each individual material within each group. In SAS, there is a BY statement and we can get BLUP value for each individuals within a group. We are wondering whether there is a similar R code that we can calculate the BLUPs for each group similar like by statement in SAS? Below is the R code we pare testing for your reference. Thank you very much in advance!
Take a look at ranef() and coef(). If you have further questions,
could you please send them to r-sig-mixed-models at r-project.org ?
I can believe that "%in%" works as a nesting indicator, but it is more
typical/I am more familiar with REPNO:HOSEID ... (I'm also curious why
you need to ignore the greater-than-1-level check ...)
thanks
Ben Bolker
Zhanyou
Milkvarcomp = lmer(YGSMN~ (1|ANIMALID) + (1|HOSEID) +
(1|REPNO%in%HOSEID) + (1|MINRNG%in%HOSEID) +
(1|MINROW%in%HOSEID)+
(1|ANIMALID:HOSEID),control=lmerControl(check.nlev.gtr.1="ignore"),
data=Stage3Data)
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*Ken Beath* Lecturer Statistics Department MACQUARIE UNIVERSITY NSW 2109, Australia Phone: +61 (0)2 9850 8516 Building E4A, room 526 http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/ CRICOS Provider No 00002J This message is intended for the addressee named and may...{{dropped:9}}