/********************************************************************* * STAT 7030 - Categorical Data Analysis * Peng Zeng (Auburn University) * 2025-04-06 *********************************************************************/ data foot_mv; input yr b1 b2 b3 k1 k2 k3; sire = _n_; datalines; 1 1 0 0 52 25 0 1 1 0 0 49 17 1 1 1 0 0 50 13 1 1 1 0 0 42 9 0 1 1 0 0 74 15 0 1 1 0 0 54 8 0 1 1 0 0 96 12 0 1 -1 1 0 57 52 9 1 -1 1 0 55 27 5 1 -1 1 0 70 36 4 1 -1 1 0 70 37 3 1 -1 1 0 82 21 1 1 -1 1 0 75 19 0 1 -1 -1 0 17 12 10 1 -1 -1 0 13 23 3 1 -1 -1 0 21 17 3 -1 0 0 1 37 41 23 -1 0 0 1 47 24 12 -1 0 0 1 46 25 9 -1 0 0 1 79 32 11 -1 0 0 1 50 23 5 -1 0 0 1 63 18 8 -1 0 0 -1 30 20 9 -1 0 0 -1 31 33 3 -1 0 0 -1 28 18 4 -1 0 0 -1 42 27 4 -1 0 0 -1 35 22 2 -1 0 0 -1 33 18 3 -1 0 0 -1 35 17 4 -1 0 0 -1 26 13 2 -1 0 0 -1 37 15 2 -1 0 0 -1 36 14 1 -1 0 0 -1 63 20 3 -1 0 0 -1 41 8 1 ; data footshape; set foot_mv; array k{3}; do Shape = 1 to 3; count = k{Shape}; output; end; drop k:; run; proc print data = footshape; run; proc glimmix data=footshape method = quad; class sire; model Shape = yr b1 b2 b3 / s link = cumprobit dist = multinomial; random intercept / sub = sire s cl; freq count; covtest GLM; run; /********************************************************************* * THE END *********************************************************************/