pyfan.stats.multinomial.multilogit

Created on Dec 4, 2017

@author: fan

Module Contents

Classes

UtilityMultiNomial

each_j_indirect_utility:
pyfan.stats.multinomial.multilogit.logger[source]
class pyfan.stats.multinomial.multilogit.UtilityMultiNomial(scale_coef=1)[source]
each_j_indirect_utility:

N by J matrix

N is the number of individuals (unique states) J is the number of choices

N might be 0

prob_denominator(self, all_J_indirect_utility)[source]
if:

all_J_indirect_utility/self.scale_coef = -598.66/0.75

then:

prob_denominator = exp(-598.66/0.75) = 0.0

then:

sum(prob_denominator) = 0

then:

np.exp(all_J_indirect_utility/self.scale_coef)/prob_denominator_tile = INVALID

so there must be some minimal level for the division here. in terms of scaling

prob_j(self, all_J_indirect_utility, prob_denominator=None)[source]
expected_u_integrate_allj(self, prob_denominator)[source]

‘see Train discussion on consumer surplus and logit’ ‘Need to check the reference that Train cites to make sure this integration applies in my case, should derive it myself’ If one option has much higher utility, and if variance is low, integrated utility is linear in this option, in fact they are equal

get_outputs(self, all_J_indirect_utility)[source]