clear all
set obs 200
gen x1 = runiform(-5,5)
gen x2 = runiform(0,5)
generate y = -2 + x1 - 0.5*x2 + rnormal()

regress y x*
est sto lin0
predict y0
regress y x1
est sto lin1
predict y1
regress y x2
est sto lin2
predict y2

est tab lin0 lin1 lin2, se stats(r2)

graph matrix y0 y1 y2, name(linear)

generate ypr = invlogit(-2 + x1 - 0.5*x2)
//histogram ypr  // beta distribution
generate ylogit = runiform() > ypr
logit ylogit x1 x2
est sto logt0
predict yl0
logit ylogit x1
est sto logt1
predict yl1
logit ylogit x2
est sto logt2
predict yl2
est tab logt0 logt1 logt2, se stats(ll)
graph matrix yl0 yl1 yl2, name(logit)


drop ypr yl*
generate ypr = invlogit(x1 + x2)
//histogram ypr  // beta distribution
generate ylogit = runiform() < ypr
logit ylogit x1 x2
predict yl0
predict ylxb0, xb
logit ylogit x1
predict yl1
predict ylxb1, xb
logit ylogit x2
predict yl2
predict ylxb2, xb
graph matrix yl0 yl1 yl2, name(equal)
graph matrix ylxb0 ylxb1 ylxb2