Description Usage Arguments Details Value Examples
Do a D*M analysis
1 2 3 4 5 6  estDstarM(formula = NULL, data, tt, restr = NULL, fixed = list(), lower,
upper, Optim = list(), DstarM = TRUE, SE = 0, oscPdf = TRUE,
splits = rep(0L, (ncondition)), forceRestriction = TRUE, mg = NULL,
h = 1, pars, fun.density = Voss.density, args.density = list(),
fun.dist = chisq, args.dist = list(tt = tt), verbose = 1L,
useRcpp = TRUE)

formula 
A formula object of the form:

data 
A dataframe for looking up data specified in formula.
For backwards compatability this can also be with: a column named 
tt 
A time grid on which the density function will be evaluated. Should be larger than the highest observed reaction time. 
restr 
A restriction matrix where each column depicts one condition.
The number of rows should match the number of parameters (and be equal to the length of lower).
The contents of 
fixed 
A matrix that allows for fixing parameters to certain values. 
lower 
Should be a vector containing lower bounds for each parameter.
Has a default if 
upper 
Should be a vector containing upper bounds for each parameter.
Has a default if 
Optim 
a named list with identical arguments to 
DstarM 
If TRUE a D*M analysis is done, otherwise the Chi square distance between data and model is minimized. 
SE 
positive value, how many standard error to add to the variance to relax the variance restriction a bit. 
oscPdf 
Logical, if TRUE check for oscillations in calculated densities and remove densities with oscilations. 
splits 
Numeric vector determining which conditions have an equal nondecision density. Identical values in two positions indicate that the conditions corresponding to the indices of those values have an identical nondecision distribution. 
forceRestriction 
if TRUE the variance restriction is enforced. 
mg 
Supply a data density, usefull if a uniform kernel approximation does not suffice. Take care that densities of response categories within conditions are degenerate and therefore integrate to the proportion a category was observed (and not to 1). 
h 
bandwidth of a uniform kernel used to generate data based densities. 
pars 
Optional parameter vector to supply if one wishes to evaluate the objective
function in a given parameter vector. Only used if 
fun.density 
Function used to calculate densities. See details. 
args.density 
A names list containing additional arguments to be send to fun.density. 
fun.dist 
Function used to calculate distances between densities. Defaults to a chisquare distance. 
args.dist 
A named list containing additional arguments to be send to fun.dist. 
verbose 
Numeric, should intermediate output be printed? Defaults to 1, higher values result in more progress output.
Estimation will speed up if set to 0. If set to TRUE, 
useRcpp 
Logical, setting this to true will make the objective function use an Rcpp implementation
of 
Response options will be alphabetically sorted and the first response option will be treated as the 'lower' option. This means that if the observed proportion of the first response options is higher, the drift speed will most likely be negative.
fun.density
allows a user to specify a custom density function. This function must (at least) take the following arguments:
t
: a vector specifying at which time points to calculate the density
pars
: a parameter vector
boundary
: character 'upper' or 'lower' specifying for which response option the density will be calculated.
DstarM
: Logical, if TRUE the density should not describe the nondecision density, if FALSE it should describe the nondecision density.
Any additional arguments can be passed to fun.density
via the argument args.density
.
If one intends to use a custom density function it is recommended to test the function first with testFun
.
When specifying a custom density function it is probably also necessary to change the lower and upper bounds of the parameter space.
For purposes of speed, the function can be run in parallel by providing the argument Optim = list(parallelType = 1)
.
See DEoptim.control
for details. Also, for Ratcliff models the objective function has been rewritten in Rcpp.
This limits some functionality but does result in a faster estimation. Usage of Rcpp can be enabled via useRcpp = TRUE
.
When verbose is set to 1, the ETA is an estimated of the time it takes to execute ALL iterations. Convergence can (and is usually) reached before then.
Returns a list of class DstarM.fitD
that contains:
Bestvals 
Named numeric vector. Contains the best parameter estimates. 
fixed 
Numeric vector. Contains the best parameter estimates. 
GlobalOptimizer 
List. All output from the call to 
Debug 
List. contains the number of DEoptim iterations, the number of function evaluation of the objective function, and the maximum number of iterations. 
note 
String. A possible note that is used for summary purposes 
tt 
Numeric vector. Contains the time grid used. 
g.hat 
Numeric matrix. Named columns represent the (possibly smoothed) densities of the data distribution of each conditionresponse pair. 
modelDist 
Numeric matrix. Named columns represent the densities of the model evaluated at grid 
ncondition 
Numeric scalar. The number of conditions 
var.data 
Numeric vector. The variance of each conditionresponse pair. There are as many values as hypothesized nondecision densities. 
var.m 
Numeric vector. The variance of the model distributions. There are as many values as hypothesized nondecision densities. 
restr.mat 
Numeric matrix. Contains the restrictions used. 
splits 
Numeric vector. Equal to the input argument with the same name. 
n 
Numeric scalar. The total number of observations. 
DstarM 
Logical. Equal to the input argument with the same name. 
fun.density 
Function. Equal to the input argument with the same name. 
fun.dist 
Function. Equal to the input argument with the same name. 
h 
Scalar. Equal to the input argument with the same name. 
args.density 
Named list. Equal to the input argument with the same name. 
args.dist 
Named list. Equal to the input argument with the same name. 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  # simulate data with three stimuli of different difficulty.
# this implies different drift rates across conditions.
# define a time grid. A more reasonable stepsize is .01; this is just for speed.
tt = seq(0, 5, .1)
pars = c(.8, 2, .5, .5, .5, # condition 1
.8, 3, .5, .5, .5, # condition 2
.8, 4, .5, .5, .5) # condition 3
pdfND = dbeta(tt, 10, 30)
# simulate data
data = simData(n = 3e3, pars = pars, tt = tt, pdfND = pdfND)
# define restriction matrix
restr = matrix(1:5, 5, 3)
restr[2, 2:3] = 6:7 # allow drift rates to differ
# fix variance parameters
fixed = matrix(c('sz1', .5, 'sv1', .5), 2, 2)
## Not run:
# Run D*M analysis
res = estDstarM(data = data, tt = tt, restr = restr, fixed = fixed)
coef(res)
summary(res)
## End(Not run)

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