Log-uniform distribution (always positive)
Posted: 09 Jun 2023, 13:08
In a WTP-space mixed logit model, I want to test a log-uniform distribution for the price attribute '(rationale: always positive and avoid long tail of log-normal);
I have two questions (related code below)
1. Is the specification "exp(b_lambda * draws_lambda)" correct if I want a log-uniform distribution with positive numbers?
2. So far, I have assumed correlation only among non-price attributes. Is is legitimate to test the correlation between lamba and other random parameters when not all variables are normally distributed? If yes, how can I write the lambda draws to estimate a potential correlation between let say wtp_legu and lambda?
Damien
I have two questions (related code below)
1. Is the specification "exp(b_lambda * draws_lambda)" correct if I want a log-uniform distribution with positive numbers?
2. So far, I have assumed correlation only among non-price attributes. Is is legitimate to test the correlation between lamba and other random parameters when not all variables are normally distributed? If yes, how can I write the lambda draws to estimate a potential correlation between let say wtp_legu and lambda?
Damien
Code: Select all
apollo_draws = list(
interDrawsType="mlhs",
interNDraws= 100,
interUnifDraws=c("draws_lambda"),
interNormDraws=c("draws_work" ,
"draws_fodd" ,
"draws_legu" ,
"draws_engr"),
intraDrawsType="mlhs",
intraNDraws=0,
intraUnifDraws=c(),
intraNormDraws=c()
)
### Create random parameters
apollo_randCoeff = function(apollo_beta, apollo_inputs){
randcoeff = list()
randcoeff[["wtp_work"]] = w_work + s_work * draws_work
randcoeff[["wtp_fodd"]] = w_fodd + s_work_fodd * draws_work + s_fodd_fodd * draws_fodd
randcoeff[["wtp_legu"]] = w_legu + s_work_legu * draws_work + s_fodd_legu * draws_fodd + s_legu_legu * draws_legu
randcoeff[["wtp_engr"]] = w_engr + s_work_engr * draws_work + s_fodd_engr * draws_fodd + s_legu_engr * draws_legu + s_engr_engr * raws_engr
randcoeff[["lambda"]] = exp(b_lambda * draws_lambda) #log-uniform distribution
return(randcoeff)
}