I am using a D-efficient Bayesian design with priors obtained following Bliemer et al (2016). Below the code

- Code: Select all
`Design`

;alts=A*,B*, SQ

;rows = 32

;eff = (mnl,d, mean)

;bdraws=sobol(2000)

;bseed = 12345

;block=8,minsum,noimprov(60 secs)

;alg=mfederov

;model:

U(A) = baesc[(n,0.142,0.064)]*pesc[1,0] + bagest.dummy[(n,0.231,0.023)|(n,0.124,0.018)]*agest[2,1,0] + baprice[(n,-1.775,0.888)]*aprice[0.03,0.09,0.15,0.21,0.27,0.33] +baint[0.037]*agest.dummy[2]*pesc/

U(B) = baesc*pesc + bagest.dummy*agest + baprice*aprice+baint*agest.dummy[2]*pesc /

U(SQ) = b0[(n,-0.1,0.05)]

$

I have 3 questions regarding the Sample size (S) indicator:

1) THe attribute "esc" has an extremely high "Sb mean estimate". This usually is explained by a low beta prior estimate (and/or high standard error). However it is not the case in our design (compared to the rest of parameters). Why it can be explained?

2) In my output the Sb mean t-ratios are always lower than 1.96 . I expected those ratios to be higher than 1.96 as the S-estimate are calculated to have significant results (therefore higher than 1.96)

3) Can you confirm that ALWAYS the S-estimate will be higher in the bayesian design than when it is considered fixed priors?. I guess it make sense as you had uncertainty.

Thanks for having active this forum that it is extremely helpful!

Maria E.

Thanks agin