Hello, I was interested in using a two-part fractional logistic regression model, my dependent variable, -Prop_oopdental_costs- measures the proportion of dental expenditures of household income, the variable has a high number of 0s. I was wondering if this syntax would implement a two part fractional logistic regression, specifically a binary logit and a fractional logit. The syntax of what I think would be a two part model using the twopm command is shown below along with a dataex of the variables used for my regression.
Code:
***two step fractional regression models**** svy: twopm oopdental_costs inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.dentalinsurance_w1 i.QuantHI_wave1 i.Quant_wealth_wave1 /// i.smoke_now c.chronicdisease i.r11dentst, firstpart(logit) secondpart(glm, family(binomial) link(log))
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(Prop_oopdental_costs inc_d endentulism race age_cat) byte(male education veteran) float mothered byte(QuantHI_wave1 Quant_wealth_wave1) float(smoke_now chronicdisease_wave1 dentalinsurance_wave1)
.0004264392 0 0 1 3 0 5 0 1 3 4 0 2 0
0 0 0 1 1 0 5 0 1 1 3 0 2 1
.04166667 0 0 1 2 0 3 0 0 4 4 0 0 1
.13131976 0 0 1 2 0 4 0 1 1 4 1 0 0
.009246417 0 0 1 3 1 5 1 1 4 4 0 2 1
.06934813 0 0 1 2 0 5 0 0 4 4 0 0 1
.0026012764 0 0 4 3 0 5 0 1 4 4 0 2 1
0 0 0 3 3 0 1 0 . 1 1 0 2 1
.3121998 0 0 3 3 0 1 0 0 1 1 0 2 1
0 0 0 1 2 0 1 0 1 1 4 0 1 1
.011406844 0 0 1 4 1 5 1 0 3 4 0 1 0
0 0 0 3 3 0 1 0 0 1 1 0 2 1
0 0 1 4 3 1 1 0 0 1 1 0 3 1
0 0 1 2 3 0 3 0 0 1 1 0 1 1
0 0 0 2 3 1 1 0 . 1 1 0 0 0
.02031223 0 0 1 3 0 4 0 1 4 1 0 1 1
0 1 1 2 3 0 1 0 . 2 1 0 1 1
0 0 0 1 3 0 4 0 1 3 4 0 0 1
.005668934 0 0 1 3 1 5 1 1 4 4 0 0 1
.2329048 0 0 1 3 0 1 0 1 2 4 0 1 0
0 0 0 1 3 0 3 0 1 1 3 0 2 0
.0020826391 0 1 4 3 0 5 0 0 4 3 0 2 1
.005553704 0 0 4 3 1 5 1 0 4 3 0 2 1
.006561351 0 0 1 3 0 3 0 0 3 4 0 0 1
0 0 1 1 3 1 4 1 . 3 3 0 1 1
.012622437 0 0 1 2 0 5 0 1 3 3 0 0 1
.00494963 0 0 1 4 0 5 0 1 4 4 0 1 0
.021990104 0 0 1 3 1 5 1 1 1 4 0 1 0
.1649258 0 0 1 3 0 3 0 1 1 4 0 0 0
.030142045 0 0 1 3 1 5 0 0 4 3 0 3 1
.0028258166 0 0 1 3 0 5 0 1 4 3 0 0 1
0 0 0 2 3 0 1 0 0 2 3 0 2 1
0 0 0 2 3 0 5 0 1 2 1 0 2 0
0 0 1 2 3 0 1 0 0 1 3 0 1 0
0 0 1 2 2 0 3 0 0 3 1 0 0 0
0 0 0 2 3 1 3 1 1 2 3 0 2 1
0 0 0 2 3 0 3 0 1 2 3 0 0 1
0 0 0 2 3 1 3 0 0 1 1 . 2 0
.0044385265 0 0 3 1 0 3 0 1 2 4 0 0 1
0 0 0 4 3 0 1 0 0 3 3 0 0 1
.003188572 0 0 4 4 1 5 0 0 3 3 0 0 1
.028402055 0 0 3 3 1 4 1 1 1 3 0 0 1
0 0 0 1 2 0 4 0 1 1 3 0 0 1
0 0 0 1 2 0 4 0 0 4 4 0 1 0
.8988764 0 0 2 3 0 5 0 0 1 1 0 3 1
0 0 1 4 3 0 4 0 0 2 1 0 0 1
.005789047 1 0 1 3 1 1 0 1 2 4 0 1 1
.008683571 0 0 1 3 0 5 0 1 2 4 0 1 1
.001757428 0 0 1 3 1 3 1 0 3 4 0 2 1
0 0 0 2 3 0 4 0 0 2 3 0 2 0
.007549233 0 0 1 4 1 5 1 1 4 4 0 2 1
.08579273 0 0 3 3 0 3 0 0 2 2 0 1 0
0 0 1 3 3 1 2 1 0 2 2 0 1 0
16.666666 0 0 4 4 1 5 0 0 2 2 0 1 0
0 0 0 4 3 0 5 0 0 2 2 0 2 0
0 0 0 2 3 0 1 0 0 1 1 1 2 0
.0014092446 0 0 2 3 0 3 0 0 1 1 0 1 0
0 0 0 2 3 0 1 0 . 1 1 0 2 0
.02388725 0 0 1 3 1 4 1 0 3 1 0 3 0
.019906044 1 0 4 2 0 1 0 1 3 1 0 5 1
.007708119 0 0 1 4 1 3 1 1 3 4 0 3 0
.004946043 0 0 1 3 0 1 0 1 3 4 0 1 0
.02088542 0 0 1 3 0 5 0 1 3 4 0 1 1
0 0 0 1 3 0 3 0 1 2 2 0 2 0
.007807133 0 0 1 4 0 4 0 0 3 4 0 2 0
.007807133 0 0 1 4 1 5 1 . 3 4 0 1 1
.03898002 0 0 1 4 1 4 1 1 2 3 0 1 0
.01949001 0 0 1 3 0 3 0 0 2 3 0 3 0
0 0 0 1 2 0 3 0 1 2 2 0 1 1
0 0 0 1 3 0 2 0 0 1 4 0 0 1
.0011483693 0 0 3 3 0 3 0 . 1 1 0 3 0
0 1 0 2 1 0 1 0 0 2 1 1 1 0
0 0 0 2 1 0 4 0 0 1 1 1 1 1
0 0 1 2 3 0 4 0 1 1 2 0 2 0
0 0 0 1 3 0 1 0 0 1 1 0 2 0
.014272122 0 0 1 3 1 4 1 1 3 3 0 1 1
.007608323 0 0 1 3 1 3 0 . 2 3 0 1 0
0 0 0 1 3 0 3 0 1 1 4 0 1 0
0 0 0 1 4 1 1 1 0 3 3 0 0 1
.015284408 0 0 1 3 1 5 1 1 4 4 0 2 0
.004883766 0 0 1 3 1 5 0 0 2 4 0 1 1
.0019535066 0 0 1 1 0 5 0 1 2 4 0 0 1
0 0 1 1 3 1 2 0 0 2 2 0 3 0
.032701112 0 0 3 2 0 1 0 0 2 2 . 2 0
0 0 0 1 4 1 1 0 0 2 3 0 2 1
.009093665 0 0 1 4 0 4 0 0 1 3 0 0 0
0 0 1 2 3 0 3 0 0 1 1 0 3 0
0 0 0 2 3 1 2 0 1 1 1 1 0 0
0 1 1 2 3 1 3 0 1 1 2 1 0 0
0 0 0 2 3 0 4 0 0 1 1 0 2 0
.0007998232 0 0 2 3 1 4 1 0 4 2 0 2 1
0 1 0 2 1 0 4 0 0 4 2 1 2 1
.015086207 0 0 2 3 1 1 0 1 2 2 0 3 0
0 0 0 2 2 1 3 1 0 2 2 . 3 0
0 0 1 1 3 1 1 1 0 3 4 0 3 0
.04651163 0 0 1 3 0 1 0 0 3 4 0 0 0
0 0 1 2 3 1 1 1 . 1 2 0 3 0
0 0 0 2 2 0 3 0 1 2 3 0 1 0
0 0 1 1 2 0 3 0 . 2 1 1 0 0
0 1 1 1 3 1 4 0 1 3 3 0 3 0
end
label values inc_d inc_d
label def inc_d 0 "No", modify
label def inc_d 1 "Yes", modify
label values endentulism endentulism
label def endentulism 0 "No", modify
label def endentulism 1 "Yes", modify
label values race race
label def race 1 "White", modify
label def race 2 "Black", modify
label def race 3 "Hispanic", modify
label def race 4 "Other", modify
label values age_cat age_cat
label def age_cat 1 "50-59", modify
label def age_cat 2 "60-69", modify
label def age_cat 3 "70-79", modify
label def age_cat 4 "80+", modify
label values male male
label def male 0 "Female", modify
label def male 1 "Male", modify
label values education EDUC
label def EDUC 1 "1.lt high-school", modify
label def EDUC 2 "2.ged", modify
label def EDUC 3 "3.high-school graduate", modify
label def EDUC 4 "4.some college", modify
label def EDUC 5 "5.college and above", modify
label values veteran veteran
label def veteran 0 "No", modify
label def veteran 1 "Yes", modify
label values mothered mothered
label def mothered 0 "Less than High School", modify
label def mothered 1 "High School or Higher", modify
label values smoke_now smoke_now
label def smoke_now 0 "Non-Smoker", modify
label def smoke_now 1 "Currently Smokes", modify
label values dentalinsurance_wave1 dentalinsurance
label def dentalinsurance 0 "No", modify
label def dentalinsurance 1 "Yes", modify
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