Choices study and availability difference

Question

I have few experiments Type and four products type (p,c,s,m). We express preference with binary variables p_f, p_c, p_s andp_m. (1) is the choice of the product and (0) mean that this product was not choosen. stock_ex is the interval percent stock exhaustion of all product. I want to analyse product preference. This is my dataset

res=structure(list(Type = c("900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v1", "900-v1", "900-v1", "900-v1", "900-v1", "900-v1", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "900-v2", 
"900-v2", "900-v2", "900-v2", "900-v2", "900-v2", "8800-v1", 
"8800-v1", "8800-v1", "8800-v1", "8800-v1", "8800-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v1", "88-v1", "88-v1", "88-v1", "88-v1", "88-v1", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2", "88-v2", "88-v2", "88-v2", "88-v2", 
"88-v2", "88-v2"), stock_ex = c("(0,10]", "(0,10]", 
"(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(10,20]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", "(40,50]", 
"(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", 
"(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(80,90]", "(80,90]", "(80,90]", "(90,100]", "(90,100]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", 
"(90,100]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", 
"(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", 
"(0,10]", "(0,10]", "(0,10]", "(0,10]", "(10,20]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(0,10]", 
"(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", 
"(0,10]", "(0,10]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(70,80]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(0,10]", 
"(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", 
"(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", "(0,10]", 
"(0,10]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", 
"(10,20]", "(10,20]", "(10,20]", "(10,20]", "(10,20]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", "(20,30]", 
"(20,30]", "(20,30]", "(20,30]", "(20,30]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", "(30,40]", 
"(30,40]", "(30,40]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", "(40,50]", 
"(40,50]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", 
"(50,60]", "(50,60]", "(50,60]", "(50,60]", "(50,60]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", "(60,70]", 
"(60,70]", "(60,70]", "(60,70]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", "(70,80]", 
"(70,80]", "(70,80]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", "(80,90]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]", 
"(90,100]", "(90,100]", "(90,100]", "(90,100]", "(90,100]"), 
   p_f = c(1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 
    0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 
    1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
    1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 
    0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 
    0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 
    0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 
    1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 
    1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 
    1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 
    0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 
    1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 
    0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 
    0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 
    0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 
    1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 
    1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 
    1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 
    0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 
    1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 
    1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 
    1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 
    1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0), p_m = c(0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 
    0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 
    0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), p_c = c(0, 
    0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 
    1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 
    0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 
    1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 
    0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 
    0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
    0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 
    1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 
    1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
    1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 
    1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 
    1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 
    0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 
    0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 
    0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 
    1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 
    0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 
    1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 
    1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 
    1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 
    0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 
    1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 
    0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 
    1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0), p_s = c(0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 
    1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Type", "stock_ex", 
"p_f", "p_m", "p_c", "p_s"), row.names = c(8L, 12L, 
14L, 16L, 27L, 33L, 35L, 36L, 39L, 41L, 42L, 48L, 50L, 51L, 53L, 
61L, 68L, 71L, 81L, 87L, 90L, 92L, 95L, 98L, 100L, 101L, 102L, 
111L, 117L, 123L, 126L, 132L, 135L, 136L, 140L, 146L, 152L, 154L, 
159L, 161L, 162L, 164L, 169L, 173L, 175L, 180L, 183L, 189L, 191L, 
192L, 194L, 196L, 200L, 201L, 204L, 206L, 209L, 211L, 217L, 226L, 
227L, 242L, 245L, 248L, 249L, 250L, 255L, 264L, 268L, 274L, 276L, 
278L, 280L, 281L, 299L, 308L, 314L, 319L, 336L, 345L, 348L, 391L, 
3L, 7L, 17L, 23L, 26L, 28L, 30L, 31L, 37L, 46L, 64L, 65L, 67L, 
73L, 75L, 77L, 79L, 80L, 83L, 86L, 96L, 97L, 103L, 105L, 107L, 
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148L, 150L, 153L, 156L, 157L, 160L, 163L, 165L, 168L, 172L, 176L, 
177L, 178L, 181L, 187L, 188L, 195L, 199L, 202L, 203L, 207L, 213L, 
219L, 221L, 224L, 225L, 230L, 232L, 251L, 252L, 254L, 258L, 262L, 
273L, 275L, 285L, 290L, 297L, 303L, 305L, 311L, 313L, 316L, 322L, 
324L, 325L, 332L, 342L, 343L, 346L, 347L, 353L, 354L, 358L, 359L, 
360L, 361L, 362L, 367L, 376L, 378L, 389L, 390L, 392L, 393L, 403L, 
405L, 406L, 408L, 409L, 420L, 421L, 424L, 425L, 431L, 432L, 433L, 
439L, 449L, 451L, 453L, 455L, 459L, 465L, 466L, 467L, 470L, 474L, 
475L, 476L, 477L, 480L, 483L, 486L, 491L, 492L, 493L, 500L, 501L, 
502L, 503L, 505L, 506L, 509L, 511L, 515L, 519L, 520L, 524L, 526L, 
527L, 529L, 532L, 533L, 540L, 542L, 546L, 547L, 548L, 551L, 555L, 
556L, 557L, 559L, 560L, 563L, 564L, 565L, 566L, 568L, 573L, 1L, 
2L, 4L, 9L, 10L, 11L, 13L, 15L, 19L, 22L, 24L, 29L, 34L, 49L, 
54L, 57L, 60L, 62L, 70L, 76L, 93L, 104L, 106L, 108L, 112L, 113L, 
127L, 130L, 138L, 139L, 147L, 155L, 166L, 171L, 182L, 193L, 198L, 
212L, 220L, 222L, 228L, 231L, 234L, 235L, 236L, 237L, 238L, 241L, 
243L, 244L, 246L, 265L, 269L, 270L, 286L, 294L, 296L, 300L, 301L, 
307L, 317L, 323L, 329L, 330L, 331L, 338L, 339L, 341L, 351L, 366L, 
368L, 370L, 374L, 377L, 379L, 381L, 382L, 385L, 394L, 397L, 404L, 
414L, 415L, 418L, 423L, 428L, 434L, 438L, 441L, 444L, 446L, 452L, 
460L, 461L, 462L, 469L, 481L, 485L, 599L, 601L, 604L, 605L, 608L, 
5L, 6L, 18L, 20L, 25L, 32L, 38L, 43L, 44L, 45L, 55L, 56L, 58L, 
63L, 66L, 69L, 72L, 74L, 78L, 82L, 84L, 85L, 88L, 89L, 91L, 94L, 
118L, 120L, 125L, 129L, 134L, 137L, 141L, 143L, 144L, 145L, 149L, 
151L, 158L, 170L, 174L, 179L, 184L, 185L, 186L, 197L, 205L, 208L, 
210L, 214L, 215L, 216L, 223L, 229L, 233L, 239L, 240L, 247L, 253L, 
259L, 260L, 261L, 263L, 266L, 267L, 271L, 282L, 283L, 288L, 289L, 
291L, 292L, 293L, 295L, 302L, 304L, 306L, 310L, 312L, 315L, 318L, 
321L, 326L, 327L, 333L, 334L, 335L, 337L, 340L, 344L, 350L, 355L, 
356L, 357L, 364L, 365L, 369L, 371L, 372L, 373L, 375L, 387L, 388L, 
395L, 398L, 399L, 407L, 411L, 412L, 416L, 419L, 426L, 430L, 436L, 
442L, 443L, 445L, 447L, 448L, 450L, 456L, 457L, 468L, 471L, 484L, 
487L, 495L, 496L, 497L, 498L, 499L, 504L, 507L, 510L, 512L, 513L, 
518L, 521L, 522L, 523L, 528L, 530L, 531L, 534L, 535L, 538L, 539L, 
541L, 544L, 549L, 553L, 558L, 561L, 562L, 569L, 570L, 574L, 575L, 
576L, 579L, 581L, 588L, 595L, 597L), class = "data.frame")

I tried GLM.

a=anova(glm(p_f ~ stock_ex  , family=binomial),test="Chisq")
a=anova(glm(p_s ~ stock_ex  , family=binomial),test="Chisq")
a=anova(glm(p_c ~ stock_ex  , family=binomial),test="Chisq")

The problem is that the number of availibilty of product is different globally, and more than that different per type experiment.

> sum(res$p_f) [1] 222
> sum(res$p_c) [1] 225
> sum(res$p_s) [1] 22

Firstly, is my approach is ok ? secondly, how to study choices under this restriction ? and finally, how to be aware of that in my analyses ? I'm sorry if my question could be seen as basic.

Edit 1: DV : p_f, p_c, p_s andp_m ID : stock_ex and type

Instructions unclear, what is your DV and what are you IV's? Are the p_'s your DV's? If so you should aggregate these into 1 variable and perform multinomial logistic regression — user2974951, Sep 12 '18 at 08:18
Thanks for replying, please see edit 1. I have 4 binary DV that express choice toward one product (f,c,s,m respectively p_f,p_c,p_s,p_m)) and want to express them according stock_ex (which is exhaustion level of all product) and experiment type. (1) I don't know how to do with 4DV in one GLM. How to aggregate it ? (2) Is difference in number could be taken in account in this analyses ? (s= 22 products available and f = 222 products available) – ranell 6 mins ago — ranell, Sep 12 '18 at 08:32
If you have only one possible outcome for each row (one out of the 4 p_'s), that is if the outcome is either p_f, p_c, p_s or p_m, then you can combine simply by creating a new vector with values 1, 2, 3, 4 and perform multinomial. If this is not the case and multiple outcomes are possible for every row, then your approach is justified. Regarding unbalanced data / classes, this is not a trivial problem to solve, have a look at https://stats.stackexchange.com/questions/6067/does-an-unbalanced-sample-matter-when-doing-logistic-regression — user2974951, Sep 12 '18 at 08:38
Thanks a lot. I'm looking on the link and aboutunbalanced data. In the other hands, does proportion could be interesting in such case ? Example : transform p_s= p_s/ 22 ( I don't know also ifI could continue to use binomial GLM. — ranell, Sep 12 '18 at 09:26
Transforming as per your example will not help, the problem lies in the actual number of cases, which is fixed. You would need to gather more data from the smaller classes, or remove some cases from the larger classes. There are alternatives, some are mentioned in the link. — user2974951, Sep 12 '18 at 09:30

Choices study and availability difference

0 Answers0