Fit LBA models with explanatory variables.

Description

Fit LBA models with explanatory variables on the parameters.

Details

See ?lba for examples.

Author(s)

Ingmar Visser (with functions adapted from Brown, Heathcote and Donkin). Maintainer: Ingmar Visser <i.visser@uva.nl>

References

Brown, S. D., and Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive psychology, 57(3), 153-178.

Examples

# To be added later

Example data from Brown and Heathcote (2008).

Description

Example data from Brown and Heathcote (2008); response times and accuracies for three difficulty levels.

Usage

data(bh08)

Format

A data frame with 3000 observations on the following 3 variables.

acc

response accuracy

rt

response times

diff

difficulty, a factor with levels easy medium diff

Details

Example data from Brown and Heathcote, 2008.

Source

Data from: http://www.newcl.org/members/chris/fitLBA.zip

Brown and Heathcote, Cognitive science, 2008

References

Brown and Heathcote, 2008

Examples

data(bh08)

Core functions to compute the probability density function, cumulative distribution function, and defective distribution of the LBA (see Brown and Heathcote, 2008).

Description

For internal use only.

Author(s)

Ingmar Visser adapted these functions, to make them fully vectorized in all relevant arguments. Original functions fptpdf, fptcdf and n1PDF are provided by Brown, Heathcote and Donkin and are retrieved from: http://www.newcl.org/members/chris/fitLBA.zip

References

http://www.newcl.org/members/chris/fitLBA.zip

Implicit learning data from Visser et al (2007).

Description

Implicit learning data from Visser et al (2007); these data are from blocks 1-12 of participant 2 of Experiment 1.

Usage

data(ilpp2)

Format

A data frame with 12 blocks of 395 trials of a sequential response time task; observations on the following variables:

id

participant id

ses

session number (1-2)

blk

block number within session (1-6)

tri

trial location (1-4)

ans

answer location (1-4)

rt

response time in seconds

acc

accuracy (factor with levels 'cor'rect and 'inc'orrect

acBin

accuracy in binary format

blk2

block number continuous (1-12)

tt

trial type; factor with levels 'seq'uential and 'ran'dom

Details

Data from Visser et al (2007), Memory and Cognition.

Source

Visser et al (2007), Memory and Cognition.

References

Visser et al (2007), Memory and Cognition.

Examples

data(ilpp2)

Utility functions for internal use.

Description

Functions for specifying, printing and altering the submodels for each of the parameters.

Author(s)

Ingmar Visser

Specify and fit lba models.

Description

Specification of lba models with explanatory variables on the parameters. Models are fitted using full information maximum likelihood.

Usage

lba(rt, response, data, weights, 
sddr = ~1, 
sp = ~1, 
bound = ~1, 
nond = ~1, 
drift = ~1, 
scaling = "sum", 
loglink = c(FALSE, FALSE, FALSE, FALSE), 
sdstart=0.1,
startpars, 
fixed = NULL,
method = "L-BFGS-B",
hessian = FALSE,
lower = -Inf,
upper = Inf,
fit=TRUE,
debug=FALSE) 

## S3 method for class 'lba'
logLik(object, ...)
## S3 method for class 'lba'
print(x, ...)
## S3 method for class 'lba'
summary(object, ...)

tablba(object)

Arguments

Details

The 5 sub-models of the LBA model can be specified using a formula. Eg, nond=~diff, specifies a model in which the nondecision time is modeled as function of difficulty; diff may be a factor or numerical variable.

Identification of the model is attained by having the drift rates for alternatives in response sum to unity or by having a fixed standard deviation of the drift rates. These options are specified by scaling="sum" or scaling="fixedSD", respectively. In the latter case, the value of the fixed standard deviation is assumed to be the first value of startpars.

If hessian is set to TRUE, optim is used to get the Hessian at the maximum likelihood estimates of the parameters. This is used in turn to compute standard errors. The summary function pretty prints a table of parameter estimates, standard errors, z- and p-values. The summary function also prints the separate sub-model specifications and the parameter estimates. The print function prints basic information and a (named) vector of parameter estimates.

Value

The lba function returns a list with the following components:

Function tablba returns a data.frame with columns:

Author(s)

Ingmar Visser

References

LBA models as in Brown and Heathcote, Cognitive science, 2008.

Examples

data(bh08)

# reduce the data set for faster runtimes
bh1 <- bh08[c(1:300,1001:1300,2001:2300),]

# remove extreme RTs
bh1 <- bh1[bh1$rt>.180&bh1$rt<2,]

# starting values based on the quantile parameter 
# estimates from Brown and Heathcote, 2008:
# pars <- c(.25,.3,.1,.2,.9,.75,.6)

# fit the model with starting values
pars <- c(.25,.3,.1,.2,.9,.75,.6)
m1 <- lba(rt=rt,response=acc,drift=~diff-1,data=bh1,
	startpars=pars)

m1

summary(m1)

## Not run: 
	# use random starting values instead
	set.seed(2)
	m1 <- lba(rt=rt,response=acc,drift=~diff-1,data=bh08,
	startpars=pars, hessian=TRUE)

	summary(m1)
	
	# get the parameters
	pp <- m1$pars
	# simulate some data from the model for the three conditions
	set.seed(1)
	dt1 <- rlba(1000, b=pp[3]+pp[2], A=pp[2], vs=c(pp[5],1-pp[5]), 
		s=pp[1], t0=pp[4], truncdrifts=TRUE)
	dt2 <- rlba(1000, b=pp[3]+pp[2], A=pp[2], vs=c(pp[6],1-pp[6]), 
		s=pp[1], t0=pp[4], truncdrifts=TRUE)
	dt3 <- rlba(1000, b=pp[3]+pp[2], A=pp[2], vs=c(pp[7],1-pp[7]), 
		s=pp[1], t0=pp[4], truncdrifts=TRUE)
	dt <- rbind(dt1,dt2,dt3)
	dt$diff <- gl(3,1000)
	dt$resp <- ifelse(dt$resp==2,0,1)
	
	# remove extreme RTs
	dt <- dt[dt$rt>.180&dt$rt<2,]
	
	# refit the model with the simualted data
	m1rf <- lba(rt=rt,response=resp,drift=~diff-1,data=dt,startpars=pp)
	
	data(numpp1)
	
	set.seed(9)
	m2 <- lba(rt=rt,response=acc,drift=~diff+block,data=numpp1,
	startpars=c(0.25,runif(4),0,0), scaling="fixedSD", 
		sdstart=0.25, hessian=TRUE) 
	tablba(m2) 
	
	
## End(Not run)

Example data from a numerosity task.

Description

Example data from a numerosity task. Participant is required to categorize a display of randomly placed dots into 'few' or 'many'.

Usage

data(numpp1)

Format

A data frame with 186 observations on the following 10 variables. Extreme data are removed.

stim

file name of the stimulus

trial

trial number

acc

accuracy in binary format

rt

response time in seconds

nr

required response; factor with levels 'few' and 'many'

block

block number (1-5)

dots

number of dots in the display

diff

absolute value of 'dots'-50

df2

dichotomized version of 'diff'

id

participant id

Details

Example data from a numerosity task.

Source

Unpublished data.

References

Unpublished data.

Examples

data(numpp1)

Generate data from an LBA model.

Description

Generate data from an LBA model.

Usage

rlba(n, b, A, vs, s, t0, st0 = 0, truncdrifts = TRUE)

Arguments

Details

No more details.

Value

A data.frame with two variables, 'rt' and 'resp', for the response time and the response, respectively.

Author(s)

Ingmar Visser, function adapted from Brown and Heathcote.

http://www.newcl.org/publications/2008/lbasoftware.htm

References

Function adapted from Brown and Heathcote.

http://www.newcl.org/publications/2008/lbasoftware.htm

Examples

# to be added later