Hello, I’m trying to figure out the best procedure to analyze ‘repeated measures for proportions scores’. In this case I counted the number of grazed or ungrazed components (grass) of two pastures (treatments) samples or patches throughout the day, when the area was grazed by cows. The objective is to see any difference in the grazing pattern in these two kind of swards. I did these measures in 10 patches (subject), four times a day, for two consecutive days and three seasons of the year (spring, summer and autumn). My data set looks like this: Season Day Treatment Patch HOUR GRAZED UNGRAZED GrazedProp Spring 1 1 1 1 6 15 0.285714 Spring 1 1 1 4 19 0 1 Spring 1 1 1 11 17 0 1 Spring 1 1 1 23 21 2 0.913043 Spring 1 1 2 1 15 61 0.197368 Spring 1 1 2 4 25 1 0.961538 Spring 1 1 2 11 61 0 1 Spring 1 1 2 23 36 0 1 Spring 1 1 3 1 13 43 0.232143 Spring 1 1 3 4 17 1 0.944444 Spring 1 1 3 11 23 0 1 Spring 1 1 3 23 13 0 1 Spring 1 1 4 1 16 33 0.326531 Spring 1 1 4 4 15 33 0.3125 Spring 1 1 4 11 62 0 1 Spring 1 1 4 23 60 0 1 Spring 1 2 1 1 14 81 0.147368 Spring 1 2 2 1 21 62 0.253012 Spring 1 2 3 1 6 53 0.101695 Spring 1 2 4 1 14 34 0.291667 Etc… I was thinking to analyze the data set with the MIXED procedure, but the data set is not normal, as at the beginning of the day a most of the grasses are ‘ungrazed’ and, as the day goes on, the cows graze most of the grasses so I will have almost all of the grasses grazed by hour 11 or 23. proc mixed data=Grazedungrazed; class season day HOUR Treatment Patch; model GrazedProp = season|Treatment|hour /DDFM = satterth; repeated day(HOUR) / Subject = Treatment*date*patch Type=ar(1); random patch; LSmeans season|Treatment|hour / pdiff = all adjust = tukey; ods output LSmeans = Grazmeans Diffs = GrazPdiffs; title ' Grazedprop'; run; However, a friend of mine suggested that GLIMMIX will be a better procedure for this kind of analysis and I am a little bit doubtful now… especially because I'm not really that experienced in statistics, so any help is appreciated. Thanks
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