Skip to contents

Shifting Cognitive Model

shift.Rd

Fits a model that shifts over time from one value to another value, with the change point in time being a free parameter

  • shift_d() fits the change point model for discrete responses.

Usage

shift_d(formula, data, time, fix = NULL, options, discount = 0, ...)

Arguments

formula

A formula, the variables in data to be modeled. For example, y ~ x1 + x2 models response y as function of shifting from x1 to x2.

data

A data frame, the data to be modeled.

time

(optional) Variable with the decision time or trial across which the shift occurs; can be a numeric vector, string, or formula. If missing will be set to range from 1 to the number of rows in data.

fix

(optional) A list with parameter-value pairs of fixed parameters. If missing all free parameters are estimated. If set to "start" all parameters are fixed to their start values. Model parameter names are c (see details - model parameters).

  • list(c = 1.85) sets parameter c equal to 1.85.

  • "start" sets all parameters equal to their initial values (estimates none). Useful for building a first test model.

options

(optional) A list, list entries change the modeling procedure. For example, list(lb = c(k=0)) changes the lower bound of parameter k to 0, or list(fit_measure = "mse") changes the goodness of fit measure in parameter estimation to mean-squared error, for all options, see cm_options.

discount

(optional) A number, e.g. 10 ignores the first ten data rows when fitting parameters and when calculating goodness of fits

...

other arguments, ignored.

Value

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).

Details

The model models a shift between two input values over time.

Parameter Space

The model has 1 free parameter:

  • c the change point when the shift occurs in time

  • Additoinal parameters in shift_c(): 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.

See also

Other cognitive models: Cm, baseline_const_c(), bayes(), choicerules, cpt, ebm(), hm1988(), shortfall, threshold, utility

Author

Jana B. Jarecki, jj@janajarecki.com

Examples

## Make fake data -----------------------------------------------------
D <- data.frame(a = rep(0L,4), b = 1L, y = c(0,0,1,1))
M <- shift_d(~ a + b, D, fix = c(c=1.5))
predict(M)
#> [1] 4.539787e-05 9.999546e-01 1.000000e+00 1.000000e+00