ddWDM {WienR} | R Documentation |
Wrapper function for the partial derivative of the first-passage time probability density function of the diffusion model
Description
Calculates the partial derivative of the first-passage time probability density function of the diffusion model with respect to one of t, a, v, w, t0, sv, sw, or st0, or calculate the gradient.
Usage
ddWDM(
wrt,
t,
response,
a,
v,
w,
t0 = 0,
sv = 0,
sw = 0,
st0 = 0,
precision = NULL,
K = NULL,
n.threads = FALSE,
n.evals = 6000
)
Arguments
wrt |
partial derivative w.r.t. one of the following:
|
t |
First-passage time. Numeric vector. |
response |
Response boundary. Character vector with |
a |
Upper barrier. Numeric vector. |
v |
Drift rate. Numeric vector. |
w |
Relative starting point. Numeric vector. |
t0 |
Non-decision time. Numeric vector |
sv |
Inter-trial variability of drift rate. Numeric vector. Standard deviation of a normal distribution |
sw |
Inter-trial variability of relative starting point. Numeric vector. Range of uniform distribution |
st0 |
Inter-trial variability of non-decision time. Numeric vector. Range of uniform distribution |
precision |
Optional numeric value. Precision of the partial derivative. Numeric value. Default is |
K |
Optional. Number of iterations to calculate the infinite sums. Numeric value (integer). Default is
We recommend using either default ( |
n.threads |
Optional numerical or logical value. Number of threads to use. If not provided (or 1 or |
n.evals |
Optional. Number of maximal function evaluations in the numeric integral if sv, sw, and/or st0 are not zero. Default is |
Value
A list of the class Diffusion_deriv
containing
-
deriv
: the derivatives of the PDF with respect to the chosenwrt
, -
call
: the function call, -
err
: the absolute error. Only provided if sv, sw, or st0 is non-zero. If numerical integration is used, the precision cannot always be guaranteed.
Author(s)
Raphael Hartmann
References
Hartmann, R., & Klauer, K. C. (2021). Partial derivatives for the first-passage time distribution in Wiener diffusion models. Journal of Mathematical Psychology, 103, 102550. doi:10.1016/j.jmp.2021.102550
Examples
ddWDM(wrt = "a", t = 1.2, response = "upper", a = 1.1, v = 13, w = .6, precision = NULL, K = NULL)