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Data input:
- revealed preference or stated preference, disaggregate or aggregate data can be used; choice, ranking or proportional split data can be employed;
- input data can be manipulated and transformed freely to allow the user freedom in finding the required explanation of behaviour; extensive testing of input data to reveal modelling problems; simple controls, yet giving access when required to full sophistication;
- multiple data sets of different formats are accepted, either in succession or linked using named (key) variables; some binary matrix formats are supported.
系统要求
ALOGIT支持Windows或以上的操作系统,也支持旧的操作系统XP, 2000, NT, 98和95。
Model estimation:
- all coefficients are estimated simultaneously using maximum likelihood estimation (i.e. ‘full information’ estimates);
- models can be binomial, multinomial, or tree (nested) logit models, with unlimited branches and levels; alternatively, mixed logit or error component analysis, including differing distributions (including exponentials, e.g. lognormal) and correlated error terms;
- non-linear utility functions allow attraction variables to be included correctly;
- composite alternatives can be indicated as chosen;
- coefficient estimates, standard errors, correctly calculated elasticities, consumer surplus measures and several detailed tests are all standard, with informative, clearly labelled, well-laid-out output, suitable for immediate incorporation in reports;
- a function in the ALOGIT can be used to make comparisons between different model variants;
- an option for initial linear estimation reduces run time for complex models;
- problem sizes are not limited by ALOGIT.
non-linear utility functions allow attraction variables to be included correctly;
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