Wiener diffusion model fit function
wdm creates parameter estimates for the four parameters
of the Wiener model.
wdm(data, yvar=c("q", "resp"), alpha=NULL, tau=NULL, beta=NULL,
delta=NULL, xvar=NULL, start=NULL, fixed=0)
## S3 method for class 'wdm'
vcov(object, ..., method="hessian")data |
is the data object containing data coming from a (hypothetical) Wiener diffusion process. For further details on the data object, see is.wiener. |
yvar |
represents an optional vector, that can be used to define the names of the reaction time column. For further details on the data object, see is.wiener. |
alpha |
optional, can be used to fix the alpha parameter to the given value. |
tau |
optional, can be used to fix the tau parameter to the given value. |
beta |
optional, can be used to fix the beta parameter to the given value. |
delta |
optional, can be used to fix the delta parameter to the given value. |
xvar |
optional: group factor variable to estimate all unfixed parameters separate for the given groups. |
start |
an optional vector with the four starting parameter values in the following order: alpha, tau, beta, delta. |
fixed |
a number indicating how many of the parameters are fixed (not free). This number will be subtracted from the number of free parameters. Defaults to 0. |
method |
the method to use for estimating the covariance matrix of
the parameter estimates. Options are |
object |
a wdm object file or a list containing a |
... |
arguments passed to methods. |
The wdm function calls an estimation routine, to estimate the
model parameters.
If all but one parameters are fixed, a "Brent
(optim)" type algorithm is used. For the estimation of more than one
parameter, first a "BFGS (optim)" type algorithm is tried, if
unsuccessful, a "Newton type (nlm)" algorithm is tried, if again
unsuccessful, a "Nelder-Mead (optim)" algorithm is used.
In case all parameters are set to fixed values, no estimation routine is
called, but a wdm object will still be created.
The returned wdm object is basically a list containing the
parameter estimates in $coefficients. $hessian contains the
numerically differentiated Hessian matrix (if available, else NULL).
$data contains the data passed to the wdm function call.
$loglik contains the log-likelihood value for the wdm
object and its parameter estimates. $estpar contains a vector,
that is TRUE if the respective parameter was estimated and
FALSE if the respective parameter was set to a fixed value.
Additional information is given in other list objects.
The standard R functions coef, vcov, confint,
summary can be used with wdm objects.
## generate random data
dat <- rbind(cbind(rwiener(100, 2,.3,.5,1), group=factor("A", c("A","B"))),
cbind(rwiener(100,2,.3,.5,-1), group=factor("B", c("A", "B"))))
## fit wdm
wdm1 <- wdm(dat)
## extract parameters
coef(wdm1)
## further models
wdm2 <- wdm(dat, beta=.5)
wdm3 <- wdm(dat, alpha=wdm1$coefficients[1], tau=wdm1$coefficients[2],
beta=wdm1$coefficients[3], xvar="group")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.