simData {DstarM} | R Documentation |
Simulate data from a given density function via multinomial sampling
Description
Simulate data from a given density function via multinomial sampling
Usage
simData(
n,
pars,
tt,
pdfND,
fun.density = Voss.density,
args.density = list(prec = 3),
npars = 5,
return.pdf = FALSE,
normalizePdfs = TRUE
)
Arguments
n |
Number of observations to be sampled |
pars |
Parameter values for the density function to be evaluated with. |
tt |
time grid on which the density function will be evaluated. Responses not in this time grid cannot appear. |
pdfND |
either a vector of length tt specifying the nondecision density for all condition-response pairs,
or a matrix where columns corresponds to the nondecision densities of condition-response pairs. Supplying |
fun.density |
Density function to use. |
args.density |
Additional arguments to be passed to |
npars |
Number of parameters |
return.pdf |
Logical, if TRUE |
normalizePdfs |
Logical, should the pdf of the nondecision distribution be normalized? |
Details
Simulate data via multinomial sampling.
The response options to sample from should be provided in tt
.
The number of conditions is defined as length(pars) / npars
.
Value
A sorted dataframe where rows represent trials. It contains: a column named rt
containing reaction times in seconds, a column named response containing either
response option lower or upper, and a column named condition indicating which
condition a trials belongs to. If return.pdf
is TRUE it returns a list where the
first element is the sorted dataframe, the second through the fifth elements are lists
that contain densities used for simulating data.
Examples
tt = seq(0, 5, .01)
pdfND = dbeta(tt, 10, 30)
n = 100
pars = c(1, 2, .5, .5, .5)
dat = simData(n, pars, tt, pdfND)
head(dat)