program define gr_olog
* make parameters for equation
display "usage: gr_olog <a> <or> <cut1> <cut2> <xunits"
local a = `1'
local or = `2'
local cut1 = `3'
local cut2 = `4'
local xunits = `5'

local b1 = ln(`or')


* make probs from parameters
clear
set obs `xunits'
generate x = _n - ((`xunits'/2))
generate score = x * `b1'

generate pgty1 = 1 - 1/(1 + exp(score - `cut1'))
generate pgty2 = 1 - 1/(1 + exp(score - `cut2'))

* Graph "logistic regression"
graph pgty1 pgty2 x, c(ll)

more

* Graph "logit regression"
generate logit1 = log( pgty1 / ( 1 - pgty1))
generate logit2 = log( pgty2 / ( 1 - pgty2))

* graph "logit" regression
graph logit1 logit2 x, c(ll)
more

* graph of predicted probabilities of each category
generate peqy1 = 1 - pgty1
generate peqy2 = pgty1 - pgty2
generate peqy3 = pgty2

graph peqy1 peqy2 peqy3 x, c(lll) 
more

* make summary counts from summary percentages
gen county1 = round(300*peqy1,1)
gen county2 = round(300*peqy2,1)
gen county3 = round(300*peqy3,1)

* make odds and odds ratios, and confirm them
gen odds1 = county3 / (county1 + county2)
gen odds2 = county3+county2 / (county1)
* odds ratios
gen oddsr1 = odds1[_n] / odds1[_n-1]
gen oddsr2 = odds2[_n] / odds2[_n-1]
* confirm the oddds ratio
listblck x county1 county2 county3 odds1 oddsr1 odds2 oddsr2
more

* make the individual data
reshape long county, i(x) j(y)
expand county

* show the tabulations of the individual data
tab  x y
more

* run the ologistic regression
ologit y x, table
more

* show the "odds ratio"
display exp( _b[x])


end