Description Usage Arguments Details Value References Examples
This function is a more basic function than dualValueMatrix
. This
function is based on the value function of the dual-reference point model
(dual-rp) [1]. It first builds a desicion matrix for each user and then applys
the drp-utility function over each value using smallerThanZero
.
The dual-rp value function first creates a gain-loss matrix based on the two
reference points. It then outputs the value of each gain/loss based on the
loss aversion (lambda) and the relative importance of the goal (delta).
1 2 3 |
dataset |
data.frame with the user generated data from a product
configurator. See |
userid |
a vector of integers that gives the information of which users the matrix should be calculated. Vectorised. |
attr |
attributes ID, one integer corresponding to the attribute
you desire to use; |
rounds |
integer vector or text option. Which steps of the configuration
process should be shown? Defaults are first and last step. Text options are
|
cost_ids |
argument used to convert selected cost attributes into benefit attributes. Cost attribute means that weith a lower value, the user is better off than with a higher value (e.g. price). Default assumes all attributes are of benefit type (higher amount is better). |
dual.refps |
numeric vector - two numbers indicating the status-quo and the
aspiration level(goal) for the given attributes. Status-quo should always be
the first input. Contrary to |
lambda |
numeric - parameter of loss aversion for the value function as
given by [1]. Default value is 2.25 as in [2] and should be |
delta |
numeric - expresses the relative importance of the aspiration
level to other factors. Default is 0.8 and it should satisfy |
This function does the same as dualValueMatrix
but only
for one attribute, for more details please see the mentioned function.
Note: When converting a cost attribute to a benefit attribute its two
reference points change as well, enter the unconverted refps, the function
transforms them automatically when it detects a cost_ids != NULL
a list of value matrices with one attribute for each user.
[1] Golman, R., & Loewenstein, G. (2011). Explaining Nonconvex Preferences with Aspirational and Status Quo Reference Dependence. Mimeo, Carnegie Mellon University.
[2] Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.
1 2 3 4 5 6 7 | dualValueMatrix.oneAttr(myData, 10, attr = 3, dual.refps = c(1, 3.5))
dualValueMatrix.oneAttr(myData, userid= 60, rounds= "all", attr = 1, dual.refps = c(1.5, 2.5)
dualValueMatrix.oneAttr(myData, 10, attr=4, dual.refps = c(0.17, -0.10), cost_ids = 4) # Note for cost_ids SQ > G
# Return an error, 1.Too many attributes or 2. none entered
dualValueMatrix.oneAttr(keyboard_data, 8:9 , attr = c(10,12,14,16), dual.refps = c(100, 150))
dualValueMatrix.oneAttr(data1, 2) # 2. No attribute entered
|
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