Hello, I am trying to recreate a BLUP animal model from Tempelman, R. J. and Rosa, G. J. M. Empirical Bayes Approaches to Mixed Model Inference in Quantitative Genetics. In: Genetic Analysis of Complex Traits Using SAS. Saxton, A. (Editor). Cary, NC: SAS Institute Inc., 2004.   They used a pedigree of 306 animals, out of which 282 animals have phenotypic records (y). The model has one fixed effect (generation) and one random effect (animal) with a covariance structure given by a relationship matrix (306x306). The most important bit of the code is: proc mixed data=meyer noprofile; class generation animal; model y = generation /solution noint covb; random animal /type=lin(1) LDATA=L2data solution; *L2data is an adjusted output from proc inbreed; parms (40) (50) /noiter; run; The code works, but the problem is that I am getting only 282 solutions (breeding values), whereas the original contains 306 solutions.    Original dimensions: Covariance Parameters 2 Columns in X 2 Columns in Z 306 Subjects 1 Max Obs Per Subject 306 Observations Used 282 Observations Not Used 24 My dimensions: Covariance Parameters 2 Columns in X 2 Columns in Z 282 Subjects 1 Max Obs Per Subject 282 Number of Observations Read 306 Number of Observations Used 282 Number of Observations Not Used 24 So, the real question is if I can make SAS to create the Z matrix with all 306 columns. Also, is this a version issue? I am using SAS 9.4, but I guess the authors created the code about 25 years ago.   Thank you!     ... View more

Hello, is there a way to handle properly a mixed model with uneven number of measurements per subject and (more importantly) with uneven time intervals between measurements which are taken at different time points (the dataset contains observations during several years)?   I have data from artificial insemination stations about quality of ejaculate of boars. The goal is to determine if a particular mutation (snp) affects quality of ejaculate. Sample data:   boar_id   breed   year    snp   age(days)   y(quality)    measurement_order          1             A          2020   AA     250             330            1         1             A          2020   AA     265             290            2      2             B          2016   AA     330             400           1  2             B          2016   AA     350             385           2  2             B          2017   AA     365             360           3   The biggest problem I see is that measurement 1 for boar 1 is a completely different time point (date) than measurement 1 for boar 2.    I wanted to try something like this:     proc mixed data=have; class boar_id breed year snp measurement; model y = age interval breed year snp measurement; repeated measurement / subject=boar_id(snp) type=SP(POW); run;      where interval would mean number of days from the last measurement. But I am not sure if it can fix the problem above.    I also considered giving every unique date of observation its "serial number" (so instead of measurement_order I would use a time point from 1 to n), but then I end up having thousands of levels for fixed effect of time...   So is there a solution?     ... View more

Hello, I am trying to determine if animal's SNP (genotype) and diet affect quantity of a compound. There were 25 animals in the experiment measured in the course of 3 weeks. After the first measurement, half of the animals were fed with different diet.    Dataset looks like this: ID   week   SNP   diet   compound 1     1         AA      a       6.6    1     2         AA      a       6.1 1     3         AA      a       5.5 2     1         GG     b       7.3 2     2         GG     b       7.1 2     3         GG     b       7.0 ...   Q1 - How does the equivalent to lmer look like? Initally, I used lmer function in R for a model with random slope + random intercept (because I saw very similar example in a lecture and it was pretty straightforward). It is as follows:  model <- lmer(compound ~ diet + snp + diet*snp + week + (week|id), data)   So I wanted to see how the notation would look like in SAS using PROC MIXED. However, despite reading many different articles I am quite lost. I tried this, but the result is way different from lmer: proc mixed data=have;    class         id diet snp week;    model        y = diet snp diet*snp week;    random     id;    repeated   week / subject=id; run;   So what would be the correct form?   Q2 - Is there a better option? Feel free to suggest a different model if you think this one is not good enough.    Note I also tried random intercept only model: lmer(compound ~ diet + snp + diet*snp + week + (1|id), data), which is equivalent to  proc mixed data=have;     class          id diet snp week;    model        y = diet snp diet*snp week;    repeated    week / subject=id; run;   ... View more