The function is a wrapper around calculate_a_error,
calculate_c_error, calculate_d_error and
calculate_s_error to provide a unified interface for
calling and calculating efficiency criteria.
calculate_efficiency_criteria(
design_vcov,
p,
dudx,
return_all = FALSE,
significance = 1.96,
type
)A variance-covariance matrix returned by
derive_vcov or returned by an estimation routine. The matrix
should be symmetrical and K-by-K
Prior values
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error'
If `TRUE` return a K or K-1 vector with parameter specific error measures. Default is `FALSE`.
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96.
A string indicating the type of efficiency criteria to calculate can be either: "a-error", "c-error", "d-error" or "s-error"
See individual efficiency criteria
The function is mainly used internally to evaluate and report on designs, but is exported to allow the user to use the function to calculate the efficiency criteria of the model once it has been run on their data.
Bliemer and Rose, 2009, Efficiency and sample size requirements for state choice experiments, Transportation Research Board Annual Meeting, Washington DC Scarpa and Rose, 2008, Designs efficiency for non-market valuation with choice modelling: How to measure it, what to report and why, Australian Journal of Agricultural and Resource Economics, 52(3):253-282 Bliemer and Rose, 2005a, Efficiency and sample size requirements for stated choice experiments, Report ITLS-WP-05-08, Institute for Transport and Logistics Studies, University of Sydney Kessels, R., Goos, P. and Vandebroek, M., 2006, A comparison of criteria to design efficient choice experiments, Journal of Marketing Research, 43(3):409-419