distance {qmvs} | R Documentation |
Calculation of the Wasserstein metric between an empirical data set and a data set similated by the queueing model of visual search
Description
WM
calculates a distance between the empirical and simulated
response time on a given number of trials in an experiment using
standard visual search paradigm.
WMdiffresp
takes both correct and incorrect response times into
account.
WMdiffrespweight
takes both correct and incorrect response times
into account and weights the distances of correct and incoreect
response times with the relative frequencies of the data.
WMdiffrespshift
takes both correct and incorrect response times
into account and assumes different non-decision times for no and yes
responses.
WMdiffrespshiftweight
takes both correct and incorrect response
times into account assuming different non-decision times for no and yes
responses and weights the distances with the relative frequencies of
the data.
Usage
WM(par, esterrorpar, c, k, pr, N, empRT, old=FALSE)
WMdiffresp(par, esterrorpar, c, k, pr, N, empRT, empresp, old=FALSE,
seed=0)
WMdiffrespweight(par, esterrorpar, c, k, pr, N, empRT, empresp,
old=FALSE, seed=0)
WMdiffrespshift(par, esterrorpar, c, k, pr, N, empRT, empresp,
old=FALSE, seed=0)
WMdiffrespshiftweight(par, esterrorpar, c, k, pr, N, empRT, empresp,
old=FALSE, sep_shift = TRUE, wcorrect = NULL, seed=0)
Arguments
par |
A vector of length 3 or 4, equals (miat, mst, Tres) if non-decision time is assumed to be the same for no and yes responses (as in WM, WMdiffresp and WMdiffrespweight) and ( |
esterrorpar |
A vector of length 5. Estimates of the accuracy-related parameters ( |
c |
A natural number representing the number of parallel servers of the system. |
k |
A natural number representing the total number of stimuli in the display (set size). |
pr |
Logical. If pr is |
N |
A natural number representing the number of simulation runs. |
empRT |
A vector of empirical response times collected under given target presence and set size condition. |
empresp |
A vector of empirical responses collected under given target presence and set size condition. |
old |
Logical. If old is |
sep_shift |
Logical. Shall separate shifts be used for positive and negative answers? |
wcorrect |
Logical or |
seed |
The random seed used in the simulation. |
Value
A positive number.
WMdiffresp
returns the sum of the distances associated with
correct and incorrect response times,
WMdiffrespweight
the sum of the weighted distances.
WMdiffrespshift
the sum of the distances associated with
correct and incorrect response times, assuming different non-decision
times for no and yes responses.
WMdiffrespshiftweight
the weighted sum.
Author(s)
Yiq Li, yiqi.li@web.de,https://www.xing.com/profile/Yiqi_Li3, Martin Schlather,martin.schlather@uni-mannheim.de,https://www.wim.uni-mannheim.de/schlather/
References
Li, Yiqi (2020) Visual search as a queueing process. Doctoral dissertation, University of Mannheim.
See Also
Examples
simdata1 <- sim.ny(par = c(30, 200, 250, 350), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, N = 10000, pr = TRUE, seed = 0)
simdata2 <- sim.ny(par = c(30, 200, 250, 350), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, N = 10000, pr = TRUE, seed =
12345)
WM(par = c(30, 200, 300), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, pr = TRUE, N = 10000, empRT =
simdata2[,1], old=FALSE)
WMdiffresp(par = c(30, 200, 300), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, pr = TRUE, N = 10000, empRT =
simdata2[,1], empresp = simdata2[,2], old=FALSE)
WMdiffrespweight(par = c(30, 200, 300), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, pr = TRUE, N = 10000, empRT =
simdata2[,1], empresp = simdata2[,2], old=FALSE)
WMdiffrespshift(par = c(30, 200, 250, 350), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, pr = TRUE, N = 10000, empRT =
simdata2[,1], empresp = simdata2[,2], old=FALSE)
WMdiffrespshiftweight(par = c(30, 200, 250, 350), esterrorpar = c(-2.67, 0.0094,
0.0299, 0.0020, 1.13), c = 4, k = 12, pr = TRUE, N = 10000, empRT =
simdata2[,1], empresp = simdata2[,2], old=FALSE)