one.sim.psyphys2afc: Generate fake data for one subject, 2AFC psychophysics-style...

In monicathieu/psytrial: Estimate Necessary Trial Counts for a Behavioral Task

Description

Fits a probit/cumulative normal model to fake single-subject data.

Usage

one.sim.psyphys2afc(trash = NULL, xmin = NULL, xmax = NULL,
  n.xlevels = NULL, xstep = NULL, mean, sd, trials.per.bin)

Arguments

trash	A throwaway first argument to allow piping from apply functions like replicate.
xmin	A numeric value specifying the minimum stimulus intensity to be presented.
xmax	A numeric value specifying the maximum stimulus intensity to be presented.
n.xlevels	A numeric value specifying the number of intensity levels at which stimuli will be presented. Assumes that xmin and xmax will be the first and last intensity levels respectively.
xstep	Optional numeric value specifying the step size from one intensity level to the next. If specifying this, do not specify n.xlevels. If both are specified, the function defaults to calculating intensity levels from n.xlevels.
mean	A numeric value specifying the "true mean" of the probit model from which simulated data will be generated.
sd	A numeric value specifying the "true standard deviation" of the probit model from which simulated data will be generated.
trials.per.bin	A numeric value specifying the number of trials presented PER stimulus intensity bin.

Value

The estimated mean and standard deviation of the probit model fit to one round of simulated data.

monicathieu/psytrial documentation built on May 28, 2019, 1 p.m.