Interactions between categorical attributes
Posted: Tue Oct 18, 2022 6:54 pm
Dear all,
I am trying to run the code below, in which I have a partial profile design, I am averaging two models, and I am adding interactions in the second model (M2). However, I am getting an error in Negene suggesting contacting ChoiceMetrics by email ("Something went unexpectedly wrong. You may wish to email ChoiceMetrics at contact@choice-metrics.com for assistance"), and then the software closes unexpectedly. I believe the problem is related to the interactions between dummy variables (when I use interactions between continuous variables or between one continuous and one categorical variable, Ngene works well).
I would appreciate your help very much.
Thanks!
Best wishes,
Pamela.
I am trying to run the code below, in which I have a partial profile design, I am averaging two models, and I am adding interactions in the second model (M2). However, I am getting an error in Negene suggesting contacting ChoiceMetrics by email ("Something went unexpectedly wrong. You may wish to email ChoiceMetrics at contact@choice-metrics.com for assistance"), and then the software closes unexpectedly. I believe the problem is related to the interactions between dummy variables (when I use interactions between continuous variables or between one continuous and one categorical variable, Ngene works well).
I would appreciate your help very much.
Thanks!
Best wishes,
Pamela.
- Code: Select all
Design ?Bayesian D-efficient design & D-efficient design
;alts(m1) = progA*, progB*
;alts(m2) = progA*, progB*
;rows = 26
;block = 2
;eff = M1(mnl,d,mean) + M2(mnl,d)
;alg = mfederov(candidates = candidate_set_design2.csv)
;model(M1):
U(progA) = b0[(n,-0.11622,0.1198)]
+ byol.dummy[(n,1.52926,0.349)|(n,0.42661,0.3536)] * yol[2,1,0] ? # years of life gained (base:0)
+ bqol.dummy[(n,1.71774,0.6043)|(n,1.23595,0.3445)] * qol[2,1,0] ? # improvements in HRQoL (base:0)
+ bexp.dummy[(n,1.8846,0.7068)|(n,0.83302,0.4517)] * exp[2,1,0] ? patient exp 0 = poor (base), 1 = fair, 2= good
+ bsize.dummy[(n,1.44939,0.5482)|(n,0.87223,0.4073)] * size[2,1,0] ? # Size of target population per 100,000 citizens (base:0)
+ bequ.dummy[0.000002|0.000001] * equ[2,1,0] ? % disadvantaged of the target population (base:0)
/
U(progB) = byol * yol
+ bqol * qol
+ bexp * exp
+ bsize * size
+ bequ * equ
;model(M2):
U(progA) = b0[0.00000001]
+ byol.dummy[0.000002|0.000001] * yol[2,1,0] ? # years of life gained (base:0)
+ bqol.dummy[0.000002|0.000001] * qol[2,1,0] ? # improvements in HRQoL (base:0)
+ bexp.dummy[0.000002|0.000001] * exp[2,1,0] ? patient exp 0 = poor (base), 1 = fair, 2= good
+ bsize.dummy[0.000002|0.000001] * size[2,1,0] ? # Size of target population per 100,000 citizens (base:0)
+ bequ.dummy[0.000002|0.000001] * equ[2,1,0] ? % disadvantaged of the target population (base:0)
+ i1[0.0000001] * exp.dummy[1]*qol.dummy[2] ? Interaction term, with exp at level 1 and qol at level 1
+ i2[0.0000001] * exp.dummy[2]*qol.dummy[1] ? Interaction term, with exp at level 2 and qol at level 2
/
U(progB) = byol * yol
+ bqol * qol
+ bexp * exp
+ bsize * size
+ bequ * equ
+ i1 * exp.dummy[1]*qol.dummy[2]
+ i2 * exp.dummy[2]*qol.dummy[1]
$