View source: R/likelihoods_WEV.R
LogLikWEV  R Documentation 
Computes the Loglikelihood for given data and parameters in the
dynWEV model (Hellmann et al., 2023) and the 2DSD model
(Pleskac & Busemeyer, 2010). It is a wrapped version of the
respective densities dWEV
and d2DSD
,
where one can find more information about the parameters
(z
is always given relatively, in the likelihood).
The function is mainly used in fitRTConf
but
exported for individual usage in other contexts.
LogLikWEV(data, paramDf, model = "dynaViTE", simult_conf = FALSE,
precision = 1e05, stop_on_error = TRUE, data_names = list(), ...)
data 
a dataframe where each row is one trial. Containing following variables:

paramDf 
list or data.frame with one row. Names should match the names of
dynaViTE and 2DSD model specific parameter names. For different
stimulus quality/mean drift rates, names should be 
model 
character scalar. One of "dynWEV" or "2DSD" for the model to fit. 
simult_conf 
logical. Whether in the experiment confidence was reported simultaneously
with the decision, as then decision and confidence judgment are assumed to have happened
subsequent before response and computations are different, when there is an observable
interjudgment time (then 
precision 
numerical scalar. Precision of calculation for integration over z and t0. 
stop_on_error 
logical. If TRUE an error in the function will be returned in case of invalid parameters. Otherwise, the output will be 0 without error. 
data_names 
list. Possibility of giving alternative column names for the variables in the data. By default column names are identical to the ones given in the data argument description. 
... 
Possibility of giving alternative variable names in data frame
(in the form 
Note, that the requirements on the format of the columns for the likelihood functions
are much stricter, than in fitRTConf
.
This is because the function is very frequently calls in the optimization routines of the
fitting process and the preprocessing steps are
therefore included in that function.
rating, condition. If integer, values should range from 1 to number of possible
ratings/conditions. If a factor, the number of levels should be
equal to number of possible ratings/conditions. This should be consistent with the
parameter vector. The confidence thresholds should be named
as thetaUpper1
, thetaLower1
,.... (or theta1
,... for symmetric thresholds), with the
number of ratings 1 and the mean drift rates (and possibly the
standard deviation in drift rates) should be denoted as v1
, v2
,...
(and sv1
, sv2
,.../s1
, s2
, ...) with the number equal to the number of conditions.
If only one condition is used v
will be accepted as well as v1
.
stimulus, response. stimulus should always be given in numerical format with values 1 and 1.
response should always be given as a character vector with "lower"
and "upper"
as values.
This corresponds to the situation of Ratcliff's diffusion model (Ratcliff, 1978), where stimulus is the sign of the mean drift direction and
the response is the "upper"
or "lower"
boundary that is first hit by the evidence accumulation. A correct decision is therefore "lower"
,
if stimulus is 1, and "upper"
, if stimulus is 1.
Numeric scalar. The summed Loglikelihood of the data given the parameters in the respective model. If one or more rowwise probabilities is <=0, the function returns 1e+12.
Sebastian Hellmann.
Hellmann, S., Zehetleitner, M., & Rausch, M. (2023). Simultaneous modeling of choice, confidence and response time in visual perception. Psychological Review 2023 Mar 13. doi: 10.1037/rev0000411. Epub ahead of print. PMID: 36913292.
# 1. Generate data from an artificial participants
# Get random drift direction (i.e. stimulus category) and
# stimulus discriminability (two steps: hard, easy)
stimulus < sample(c(1, 1), 200, replace=TRUE)
discriminability < sample(c(1, 2), 200, replace=TRUE)
# generate data for participant 1
data < rWEV(200, a=2,v=stimulus*discriminability*0.5,
t0=0.2,z=0.5, sz=0.1,sv=0.1, st0=0, tau=4, s=1, w=0.3)
# discretize confidence ratings (only 2 steps: unsure vs. sure)
data$rating < as.numeric(cut(data$conf, breaks = c(Inf, 1, Inf), include.lowest = TRUE))
data$participant = 1
data$stimulus < stimulus
data$discriminability < discriminability
data < data[data$response!=0, ] # drop not finished decision processes
data < data[,3] # drop conf measure (unobservable variable)
head(data)
# 2. Define some parameter set in a data.frame
paramDf < data.frame(a=2.5,v1=0.5, v2=1, t0=0.1,z=0.7,
sz=0,sv=0.2, st0=0, tau=3, w=0.3,
theta1=0.8, svis=0.5, sigvis=0.8)
# 3. Compute log likelihood for parameter and data
LogLikWEV(data, paramDf, model="dynWEV", condition="discriminability")
# adding the hypothetical interjudgment time to response times
# results in the same log likelihood as before when simult_conf=TRUE
data$rt < data$rt + paramDf$tau
LogLikWEV(data, paramDf, model="dynWEV", condition="discriminability", simult_conf=TRUE)
# the same function for "2DSD" model
paramDf < data.frame(a=2.5,v1=0.5, v2=1, t0=0.1,z=0.7,
sz=0,sv=0.2, st0=0, tau=3, theta1=0.8)
LogLikWEV(data, paramDf, model="2DSD", condition="discriminability", simult_conf=TRUE)
# this results in the same log likelihood as before
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