utility | R Documentation |
Fits utility models.
utility_pow_c()
fits a power utility for continuous responses.
utility_pow_d()
fits a power utility for discrete respoonses.
utility_pow_d( formula, data, choicerule, fix = list(), discount = 0, options = list(), ... ) utility_pow_c(formula, data, fix = list(), discount = 0, options = list(), ...)
formula |
A formula, the variables in |
data |
A data frame, the data to be modeled. |
choicerule |
A string, the choice rule. Allowed values, see |
fix |
(optional) A list with parameter-value pairs of fixed parameters. If missing all free parameters are estimated. If set to
|
discount |
A number, how many initial trials to not use during parameter fitting. |
options |
(optional) A list, list entries change the modeling procedure. For example, |
... |
other arguments, ignored. |
The power utility U(x) of positive inputs, x > 0, is x^r if r > 0, and is log(x) if r = 0, and is -x^r if r < 0. The power utility of negative inputs x is -U(-x) with a separate exponent r (Wakker, 2008). To fit a power utility with one single exponent for positive and negative x, set fix = list(rp = "rn")
, not recommended for mixed input.
The model has between 1 and 3 free parameters, depending on model and data
(see npar()
):
rp
is the power utility exponent for positive data x ≥ 0 (omitted if all x < 0).
rn
is the exponent for negative data x < 0 (omitted if all x ≥ 0).
In utility_pow_c()
: sigma
is the standard deviation of the normally-distributed loglikelihood of the responses.
In utility_pow_d()
: If choicerule = "softmax"
: tau
is the temperature or choice softness, higher values cause more equiprobable choices. If choicerule = "epsilon"
: eps
is the error proportion, higher values cause more errors from maximizing.
Returns a cognitive model object, which is an object of class cm. A model, that has been assigned to m
, can be summarized with summary(m)
or anova(m)
. The parameter space can be viewed using pa. rspace(m)
, constraints can be viewed using constraints(m)
.
Jana B. Jarecki, jj@janajarecki.com
Wakker, P. P. (2008). Explaining the characteristics of the power (CRRA) utility family. Health Economics, 17(12), 1329-1344. doi:10.1002/hec.1331
Tversky, A. (1967). Utility theory and additivity analysis of risky choices. Journal of Experimental Psychology, 75(1), 27-36. doi:10.1037/h0024915
Other cognitive models:
baseline_const_c()
,
bayes()
,
choicerules
,
cpt
,
ebm()
,
hm1988()
,
shift()
,
shortfall
,
threshold()
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