Hi,
I am estimating mixed logit models with mode choice panel data (multiple trips on multiple days) and have some questions regarding calculating logsums (and changes in consumer surplus before and after an attribute is modified).
1) Am I correct in assuming that the standard output from apollo_modelOutput(model) shows only the parameters as defined in apollo_beta, and that these have to be plugged into the respective formula for the distribution chosen for the random parameters to get the mean and sd (eg. for a lognormal)?
2) After estimation, I calculate the conditional distributions. Assuming a negative lognormal distribution for cost and time attributes, I use the conditional mean estimate for the random parameters in calculating predicted observed utility for each mode and trip. Am I correct in assuming that the "apollo_conditionals" function calculates the mean and sd of the negative lognormal distribution and not just the parameters as defined in apollo_beta?
3) With regard to a negative loguniform distribution for the random parameters, how do I convert the conditional distribution output to a mean parameter value that I can use to calculate the predicted observed utility?
4) Does it make sense to calculate the VOT for each mode by dividing the conditional mean for each individual for each time parameter by the conditional mean for each individual for the cost parameter? Or should I directly estimate the model in WTP space and use the conditional parameter estimates from there?
5) Finally and somewhat unrelated, what might be a reason for getting a negative "Rhosquared vs equal shares" value? In a similar vein, what might be a reason for getting a "Rhosquared vs observed shares" equal to 1?
Thank you in advance and kind regards
Important: Read this before posting to this forum
 This forum is for questions related to the use of Apollo. We will answer some general choice modelling questions too, where appropriate, and time permitting. We cannot answer questions about how to estimate choice models with other software packages.
 There is a very detailed manual for Apollo available at http://www.ApolloChoiceModelling.com/manual.html. This contains detailed descriptions of the various Apollo functions, and numerous examples are available at http://www.ApolloChoiceModelling.com/examples.html. In addition, help files are available for all functions, using e.g. ?apollo_mnl
 Before asking a question on the forum, users are kindly requested to follow these steps:
 Check that the same issue has not already been addressed in the forum  there is a search tool.
 Ensure that the correct syntax has been used. For any function, detailed instructions are available directly in Apollo, e.g. by using ?apollo_mnl for apollo_mnl
 Check the frequently asked questions section on the Apollo website, which discusses some common issues/failures. Please see http://www.apollochoicemodelling.com/faq.html
 Make sure that R is using the latest official release of Apollo.
 Users can check which version they are running by entering packageVersion("apollo").
 Then check what is the latest full release (not development version) at http://www.ApolloChoiceModelling.com/code.html.
 To update to the latest official version, just enter install.packages("apollo"). To update to a development version, download the appropriate binary file from http://www.ApolloChoiceModelling.com/code.html, and install the package from file
 If the above steps do not resolve the issue, then users should follow these steps when posting a question:
 provide full details on the issue, including the entire code and output, including any error messages
 posts will not immediately appear on the forum, but will be checked by a moderator first. This may take a day or two at busy times. There is no need to submit the post multiple times.
Using logsums to calculate changes in utility in different scenarios

 Site Admin
 Posts: 1142
 Joined: 24 Apr 2020, 16:29
Re: Using logsums to calculate changes in utility in different scenarios
Hi
1) correct. You should then use apollo_unconditionals (which gives you the same as the formula would)
2) correct.
3) not sure what you mean. The apollo_conditionals function already deals with the distributional transform
4) no, this would be incorrect as that would be a ratio of means. I would recommend instead working with sample level distributions (apollo_unconditionals) and then work out the mean of ratios, not ratio of means
5) that means that your model is not better than a model with constants only
Stephane
1) correct. You should then use apollo_unconditionals (which gives you the same as the formula would)
2) correct.
3) not sure what you mean. The apollo_conditionals function already deals with the distributional transform
4) no, this would be incorrect as that would be a ratio of means. I would recommend instead working with sample level distributions (apollo_unconditionals) and then work out the mean of ratios, not ratio of means
5) that means that your model is not better than a model with constants only
Stephane