Hi Friends,
I'm trying to model the consumer decisions (Click-Through Rate and
Conversion) in Search Engine Advertising using a hierarchical Bayesian
binary logit. The input data is the weekly CTRs and Avg. Position for each
search keyword.
CTR is modeled as (for each keyword i and week j):
Pij = exp(C + Bi x Positionij + A1 x Lengthi + A2 x Brandi + A3 x
ProductSpecifici) / [1 + exp(C + Bi x Positionij + A1 x Lengthi + A2 x
Brandi + A3 x ProductSpecifici)]
The Position coefficient Bi is in turn allowed to vary along the population
mean (B1) and the keyword characteristics as:
Bi = B1 + K1 x Lengthi + K2 x Brandi + K3 x ProductSpecifici
How can I model this in R? Which function in R is used to do the
Hierarchical Bayesian Binary Logit modeling. Please help.
Thank you!
Kiran
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