tau2tau {rtmpt} | R Documentation |
Set process completion times equal
Description
Setting multiple process completion times (taus) equal. This means all process times of negative outcomes will be set equal and all process times of positive outcomes will be set equal. Only two process times (one for the negative and one for the positive outcome) of the named processes will be estimated. The equality can be removed by just naming only one process name.
Usage
tau2tau(model, names, outcome, keep_zeros = FALSE)
set_taus_equal(model, names, outcome, keep_zeros = FALSE)
Arguments
model |
A list of the class |
names |
Character vector giving the names of the processes for which the process completion times should be equal. If
|
outcome |
Character (no vector) indicating for which process outcome the process completion times should be set equal. Allowed characters are:
|
keep_zeros |
Can be one of the following
|
Value
A list of the class ertmpt_model
.
Note
If you use theta2theta()
and tau2tau()
with the same process names you might just change the EQN or MDL file accordingly
by using the same process name for all processes which should have equal process times and probabilities.
Author(s)
Raphael Hartmann
See Also
delta2delta
, theta2const
, tau2zero
and theta2theta
Examples
####################################################################################
# Detect-Guess variant of the Two-High Threshold model.
# The encoding and motor execution times are assumed to be equal for each category.
# The process completion times for both detection processes ("do" and "dn") will be
# set equal.
####################################################################################
mdl_2HTM <- "
# targets
do+(1-do)*g
(1-do)*(1-g)
# lures
(1-dn)*g
dn+(1-dn)*(1-g)
# do: detect old; dn: detect new; g: guess
"
model <- to_ertmpt_model(mdl_file = mdl_2HTM)
## make do = dn
new_model <- tau2tau(model = model, names = c("do", "dn"), outcome = "both")
new_model
## make do = dn
new_model <- set_taus_equal(model = model, names = c("do", "dn"), outcome = "both")
new_model