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R/WTM.R
In ream: Density, Distribution, and Sampling Functions for Evidence Accumulation Models
Defines functions
dWTM_grid
rWTM
pWTM
dWTM
Documented in
dWTM
dWTM_grid
pWTM
rWTM
#' Weibull Threshold Model
#'
#' SDDM with thresholds that change with time. Thresholds are Weibull functions of the
#' form \eqn{b_u(t) = -b_l(t) = b_0 - b_0*(1 – c)*[1 - exp(-(t/\lambda)^{\kappa})].}
#'
#' @param rt vector of response times
#' @param resp vector of responses ("upper" and "lower")
#' @param n number of samples
#' @param phi parameter vector in the following order:
#' \enumerate{
#' \item Non-decision time (\eqn{t_{nd}}). Time for non-decision processes such as stimulus
#' encoding and response execution. Total decision time t is the sum of the decision
#' and non-decision times.
#' \item Relative start (\eqn{w}). Sets the start point of accumulation as a ratio of
#' the two decision thresholds. Related to the absolute start z point via equation
#' \eqn{z = b_l + w*(b_u - b_l)}.
#' \item Stimulus strength (\eqn{\mu}). Strength of the stimulus and used to set the drift
#' rate. For changing threshold models \eqn{v(x,t) = \mu}.
#' \item Noise scale (\eqn{\sigma}). Model noise scale parameter.
#' \item Initial decision threshold location (\eqn{b_0}). Sets the location of each decision
#' threshold at time \eqn{t = 0}.
#' \item Log10-decision threshold scale (\eqn{log_{10}(\lambda)}). Sets the approximate time for threshold
#' collapse or rise.
#' \item Log10-decision threshold shape (\eqn{log_{10}(\kappa)}). Sets the threshold shape. \eqn{\kappa > 1} produces
#' logistic-like thresholds, \eqn{\kappa < 1} produces exponential-like thresholds.
#' \item Collapse parameter (\eqn{c}). Sets the amount of collapse. \eqn{c = -1} gives collapse to
#' zero, \eqn{c = 1} gives no collapse, and \eqn{c > 1} gives rise.
#' \item Contamination (\eqn{g}). Sets the strength of the contamination process. Contamination
#' process is a uniform distribution \eqn{f_c(t)} where \eqn{f_c(t) = 1/(g_u-g_l)}
#' if \eqn{g_l <= t <= g_u} and \eqn{f_c(t) = 0} if \eqn{t < g_l} or \eqn{t > g_u}. It is
#' combined with PDF \eqn{f_i(t)} to give the final combined distribution
#' \eqn{f_{i,c}(t) = g*f_c(t) + (1-g)*f_i(t)}, which is then output by the program.
#' If \eqn{g = 0}, it just outputs \eqn{f_i(t)}.
#' \item Lower bound of contamination distribution (\eqn{g_l}). See parameter \eqn{g}.
#' \item Upper bound of contamination distribution (\eqn{g_u}). See parameter \eqn{g}.
#' }
#' @param x_res spatial/evidence resolution
#' @param t_res time resolution
#' @param dt step size of time. We recommend 0.00001 (1e-5)
#' @return For the density a list of PDF values, log-PDF values, and the sum of the
#' log-PDFs, for the distribution function a list of of CDF values, log-CDF values,
#' and the sum of the log-CDFs, and for the random sampler a list of response
#' times (rt) and response thresholds (resp).
#' @references
#' Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015).
#' Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual
#' Decision-Making. \emph{The Journal of Neuroscience, 35}(6), 2476-2484.
#'
#' Palestro, J. J., Weichart, E., Sederberg, P. B., & Turner, B. M. (2018). Some task demands
#' induce collapsing bounds: Evidence from a behavioral analysis. \emph{Psychonomic
#' Bulletin & Review, 25}(4), 1225-1248.
#' @examples
#' # Probability density function
#' dWTM(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
#' phi = c(0.3, 0.5, 1.0, 1.0, 1.5, 0.2, 0.5, -1.0, 0.0, 0.0, 1.0))
#'
#' # Cumulative distribution function
#' pWTM(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
#' phi = c(0.3, 0.5, 1.0, 1.0, 1.5, 0.2, 0.5, -1.0, 0.0, 0.0, 1.0))
#'
#' # Random sampling
#' rWTM(n = 100, phi = c(0.3, 0.5, 1.0, 1.0, 1.5, 0.2, 0.5, -1.0, 0.0, 0.0, 1.0),
#' dt = 0.0001)
#' @author Raphael Hartmann & Matthew Murrow
#' @name WTM
NULL
########### PDF ###########
#' @rdname WTM
#' @useDynLib "ream", .registration=TRUE
#' @export
dWTM <- function(rt,
resp,
phi,
x_res = "default",
t_res = "default") {
# constants
modelname <- "WTM"
Nphi <- 11
# check
dist_checks(rt, resp, phi, Nphi, x_res, t_res, modelname)
# more specific checks
# setting options
opt <- dist_options(rt, x_res, t_res)
# get separated RTs for lower and upper response and get order
len_rt <- length(rt)
ind_l <- which(resp=="lower")
RTL <- rt[ind_l]
order_l <- order(RTL)
ind_u <- which(resp=="upper")
RTU <- rt[ind_u]
order_u <- order(RTU)
# prepare arguments for .Call
dt_scale <- N_deps <- NULL
REAL <- c(dt_scale = opt[[3]], rt_max = opt[[1]], phi = phi)
REAL_RTL <- as.double(RTL[order_l])
REAL_RTU <- as.double(RTU[order_u])
INTEGER <- c(N_deps = opt[[2]], N_rtl = length(REAL_RTL), N_rtu = length(REAL_RTU), Nphi = length(phi))
CHAR <- modelname
# call C++ function
out <- .Call("PDF",
as.double(REAL),
as.integer(INTEGER),
as.double(REAL_RTL),
as.double(REAL_RTU),
as.character(CHAR))
# transform output
out$pdf <- numeric(length = len_rt)
out$pdf[ind_l] <- out$likl[order_l]
out$pdf[ind_u] <- out$liku[order_u]
out$log_pdf <- numeric(length = len_rt)
out$log_pdf[ind_l] <- out$loglikl[order_l]
out$log_pdf[ind_u] <- out$logliku[order_u]
out$likl <- out$liku <- out$loglikl <- out$logliku <- NULL
return(out)
}
########### CDF ###########
#' @rdname WTM
#' @useDynLib "ream", .registration=TRUE
#' @export
pWTM <- function(rt,
resp,
phi,
x_res = "default",
t_res = "default") {
# constants
modelname <- "WTM"
Nphi <- 11
# check
dist_checks(rt, resp, phi, Nphi, x_res, t_res, modelname)
# more specific checks
# setting options
opt <- dist_options(rt, x_res, t_res)
# get separated RTs for lower and upper response and get order
len_rt <- length(rt)
ind_l <- which(resp=="lower")
RTL <- rt[ind_l]
order_l <- order(RTL)
ind_u <- which(resp=="upper")
RTU <- rt[ind_u]
order_u <- order(RTU)
# prepare arguments for .Call
dt_scale <- N_deps <- NULL
REAL <- c(dt_scale = opt[[3]], rt_max = opt[[1]], phi = phi)
REAL_RTL <- as.double(RTL[order_l])
REAL_RTU <- as.double(RTU[order_u])
INTEGER <- c(N_deps = opt[[2]], N_rtl = length(REAL_RTL), N_rtu = length(REAL_RTU), Nphi = length(phi))
CHAR <- modelname
# call C++ function
out <- .Call("CDF",
as.double(REAL),
as.integer(INTEGER),
as.double(REAL_RTL),
as.double(REAL_RTU),
as.character(CHAR))
# transform output
out$cdf <- numeric(length = len_rt)
out$cdf[ind_l] <- out$CDFlow[order_l]
out$cdf[ind_u] <- out$CDFupp[order_u]
out$log_cdf <- numeric(length = len_rt)
out$log_cdf[ind_l] <- out$logCDFlow[order_l]
out$log_cdf[ind_u] <- out$logCDFupp[order_u]
out$CDFlow <- out$CDFupp <- out$logCDFlow <- out$logCDFupp <- NULL
return(out)
}
########### RAND ###########
#' @rdname WTM
#' @useDynLib "ream", .registration=TRUE
#' @export
rWTM <- function(n,
phi,
dt = 0.00001) {
# constants
modelname <- "WTM"
Nphi <- 11
# check arguments
sim_checks(n, phi, Nphi, dt, modelname)
# more checks needed for limits etc.
# prepare arguments for .Call
REAL <- c(dt = dt, phi = phi)
INTEGER <- c(N = n, Nphi = length(phi))
CHAR <- modelname
# call C++ function
out <- .Call("SIM",
as.double(REAL),
as.integer(INTEGER),
as.character(CHAR))
# transform output
out$resp <- ifelse(out$rt >= 0, "upper", "lower")
out$rt <- abs(out$rt)
return(out)
}
########### GRID PDF ###########
#' Generate Grid for PDF of the Weibull Threshold Model
#'
#' Generate a grid of response-time values and the corresponding PDF values.
#' For more details on the model see, for example, \code{\link{dWTM}}.
#'
#' @param rt_max maximal response time <- max(rt)
#' @param phi parameter vector in the following order:
#' \enumerate{
#' \item Non-decision time (\eqn{t_{nd}}). Time for non-decision processes such as stimulus
#' encoding and response execution. Total decision time t is the sum of the decision
#' and non-decision times.
#' \item Relative start (\eqn{w}). Sets the start point of accumulation as a ratio of
#' the two decision thresholds. Related to the absolute start z point via equation
#' \eqn{z = b_l + w*(b_u - b_l)}.
#' \item Stimulus strength (\eqn{\mu}). Strength of the stimulus and used to set the drift
#' rate. For changing threshold models \eqn{v(x,t) = \mu}.
#' \item Noise scale (\eqn{\sigma}). Model noise scale parameter.
#' \item Initial decision threshold location (\eqn{b_0}). Sets the location of each decision
#' threshold at time \eqn{t = 0}.
#' \item Log10-decision threshold scale (\eqn{log_{10}(\lambda)}). Sets the approximate time for threshold
#' collapse or rise.
#' \item Log10-decision threshold shape (\eqn{log_{10}(\kappa)}). Sets the threshold shape. \eqn{\kappa > 1} produces
#' logistic-like thresholds, \eqn{\kappa < 1} produces exponential-like thresholds.
#' \item Collapse parameter (\eqn{c}). Sets the amount of collapse. \eqn{c = -1} gives collapse to
#' zero, \eqn{c = 1} gives no collapse, and \eqn{c > 1} gives rise.
#' \item Contamination (\eqn{g}). Sets the strength of the contamination process. Contamination
#' process is a uniform distribution \eqn{f_c(t)} where \eqn{f_c(t) = 1/(g_u-g_l)}
#' if \eqn{g_l <= t <= g_u} and \eqn{f_c(t) = 0} if \eqn{t < g_l} or \eqn{t > g_u}. It is
#' combined with PDF \eqn{f_i(t)} to give the final combined distribution
#' \eqn{f_{i,c}(t) = g*f_c(t) + (1-g)*f_i(t)}, which is then output by the program.
#' If \eqn{g = 0}, it just outputs \eqn{f_i(t)}.
#' \item Lower bound of contamination distribution (\eqn{g_l}). See parameter \eqn{g}.
#' \item Upper bound of contamination distribution (\eqn{g_u}). See parameter \eqn{g}.
#' }
#' @param x_res spatial/evidence resolution
#' @param t_res time resolution
#' @return list of RTs and corresponding defective PDFs at lower and upper threshold
#' @references
#' Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015).
#' Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual
#' Decision-Making. \emph{The Journal of Neuroscience, 35}(6), 2476-2484.
#'
#' Palestro, J. J., Weichart, E., Sederberg, P. B., & Turner, B. M. (2018). Some task demands
#' induce collapsing bounds: Evidence from a behavioral analysis. \emph{Psychonomic
#' Bulletin & Review, 25}(4), 1225-1248.
#' @author Raphael Hartmann & Matthew Murrow
#' @useDynLib "ream", .registration=TRUE
#' @export
dWTM_grid <- function(rt_max = 10.0,
phi,
x_res = "default",
t_res = "default") {
# constants
modelname <- "WTM"
Nphi <- 11
# checking input
grid_checks(rt_max, phi, Nphi, x_res, t_res, modelname)
# more specific checks
# setting options
opt <- grid_options(x_res, t_res)
# prepare arguments for r
dt_scale <- N_deps <- NULL
CHAR <- modelname
REAL <- c(dt_scale = dt_scale, rt_max = rt_max, phi = phi)
INTEGER <- c(N_deps = N_deps, N_phi = length(phi))
# call C++ function
out <- .Call("grid_pdf",
as.double(REAL),
as.integer(INTEGER),
as.character(CHAR))
return(out)
}
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Defines functions dWTM_grid rWTM pWTM dWTM
Documented in dWTM dWTM_grid pWTM rWTM
#' Weibull Threshold Model
#'
#' SDDM with thresholds that change with time. Thresholds are Weibull functions of the
#' form \eqn{b_u(t) = -b_l(t) = b_0 - b_0*(1 – c)*[1 - exp(-(t/\lambda)^{\kappa})].}
#'
#' @param rt vector of response times
#' @param resp vector of responses ("upper" and "lower")
#' @param n number of samples
#' @param phi parameter vector in the following order:
#' \enumerate{
#' \item Non-decision time (\eqn{t_{nd}}). Time for non-decision processes such as stimulus
#' encoding and response execution. Total decision time t is the sum of the decision
#' and non-decision times.
#' \item Relative start (\eqn{w}). Sets the start point of accumulation as a ratio of
#' the two decision thresholds. Related to the absolute start z point via equation
#' \eqn{z = b_l + w*(b_u - b_l)}.
#' \item Stimulus strength (\eqn{\mu}). Strength of the stimulus and used to set the drift
#' rate. For changing threshold models \eqn{v(x,t) = \mu}.
#' \item Noise scale (\eqn{\sigma}). Model noise scale parameter.
#' \item Initial decision threshold location (\eqn{b_0}). Sets the location of each decision
#' threshold at time \eqn{t = 0}.
#' \item Log10-decision threshold scale (\eqn{log_{10}(\lambda)}). Sets the approximate time for threshold
#' collapse or rise.
#' \item Log10-decision threshold shape (\eqn{log_{10}(\kappa)}). Sets the threshold shape. \eqn{\kappa > 1} produces
#' logistic-like thresholds, \eqn{\kappa < 1} produces exponential-like thresholds.
#' \item Collapse parameter (\eqn{c}). Sets the amount of collapse. \eqn{c = -1} gives collapse to
#' zero, \eqn{c = 1} gives no collapse, and \eqn{c > 1} gives rise.
#' \item Contamination (\eqn{g}). Sets the strength of the contamination process. Contamination
#' process is a uniform distribution \eqn{f_c(t)} where \eqn{f_c(t) = 1/(g_u-g_l)}
#' if \eqn{g_l <= t <= g_u} and \eqn{f_c(t) = 0} if \eqn{t < g_l} or \eqn{t > g_u}. It is
#' combined with PDF \eqn{f_i(t)} to give the final combined distribution
#' \eqn{f_{i,c}(t) = g*f_c(t) + (1-g)*f_i(t)}, which is then output by the program.
#' If \eqn{g = 0}, it just outputs \eqn{f_i(t)}.
#' \item Lower bound of contamination distribution (\eqn{g_l}). See parameter \eqn{g}.
#' \item Upper bound of contamination distribution (\eqn{g_u}). See parameter \eqn{g}.
#' }
#' @param x_res spatial/evidence resolution
#' @param t_res time resolution
#' @param dt step size of time. We recommend 0.00001 (1e-5)
#' @return For the density a list of PDF values, log-PDF values, and the sum of the
#' log-PDFs, for the distribution function a list of of CDF values, log-CDF values,
#' and the sum of the log-CDFs, and for the random sampler a list of response
#' times (rt) and response thresholds (resp).
#' @references
#' Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015).
#' Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual
#' Decision-Making. \emph{The Journal of Neuroscience, 35}(6), 2476-2484.
#'
#' Palestro, J. J., Weichart, E., Sederberg, P. B., & Turner, B. M. (2018). Some task demands
#' induce collapsing bounds: Evidence from a behavioral analysis. \emph{Psychonomic
#' Bulletin & Review, 25}(4), 1225-1248.
#' @examples
#' # Probability density function
#' dWTM(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
#' phi = c(0.3, 0.5, 1.0, 1.0, 1.5, 0.2, 0.5, -1.0, 0.0, 0.0, 1.0))
#'
#' # Cumulative distribution function
#' pWTM(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
#' phi = c(0.3, 0.5, 1.0, 1.0, 1.5, 0.2, 0.5, -1.0, 0.0, 0.0, 1.0))
#'
#' # Random sampling
#' rWTM(n = 100, phi = c(0.3, 0.5, 1.0, 1.0, 1.5, 0.2, 0.5, -1.0, 0.0, 0.0, 1.0),
#' dt = 0.0001)
#' @author Raphael Hartmann & Matthew Murrow
#' @name WTM
NULL
########### PDF ###########
#' @rdname WTM
#' @useDynLib "ream", .registration=TRUE
#' @export
dWTM <- function(rt,
resp,
phi,
x_res = "default",
t_res = "default") {
# constants
modelname <- "WTM"
Nphi <- 11
# check
dist_checks(rt, resp, phi, Nphi, x_res, t_res, modelname)
# more specific checks
# setting options
opt <- dist_options(rt, x_res, t_res)
# get separated RTs for lower and upper response and get order
len_rt <- length(rt)
ind_l <- which(resp=="lower")
RTL <- rt[ind_l]
order_l <- order(RTL)
ind_u <- which(resp=="upper")
RTU <- rt[ind_u]
order_u <- order(RTU)
# prepare arguments for .Call
dt_scale <- N_deps <- NULL
REAL <- c(dt_scale = opt[[3]], rt_max = opt[[1]], phi = phi)
REAL_RTL <- as.double(RTL[order_l])
REAL_RTU <- as.double(RTU[order_u])
INTEGER <- c(N_deps = opt[[2]], N_rtl = length(REAL_RTL), N_rtu = length(REAL_RTU), Nphi = length(phi))
CHAR <- modelname
# call C++ function
out <- .Call("PDF",
as.double(REAL),
as.integer(INTEGER),
as.double(REAL_RTL),
as.double(REAL_RTU),
as.character(CHAR))
# transform output
out$pdf <- numeric(length = len_rt)
out$pdf[ind_l] <- out$likl[order_l]
out$pdf[ind_u] <- out$liku[order_u]
out$log_pdf <- numeric(length = len_rt)
out$log_pdf[ind_l] <- out$loglikl[order_l]
out$log_pdf[ind_u] <- out$logliku[order_u]
out$likl <- out$liku <- out$loglikl <- out$logliku <- NULL
return(out)
}
########### CDF ###########
#' @rdname WTM
#' @useDynLib "ream", .registration=TRUE
#' @export
pWTM <- function(rt,
resp,
phi,
x_res = "default",
t_res = "default") {
# constants
modelname <- "WTM"
Nphi <- 11
# check
dist_checks(rt, resp, phi, Nphi, x_res, t_res, modelname)
# more specific checks
# setting options
opt <- dist_options(rt, x_res, t_res)
# get separated RTs for lower and upper response and get order
len_rt <- length(rt)
ind_l <- which(resp=="lower")
RTL <- rt[ind_l]
order_l <- order(RTL)
ind_u <- which(resp=="upper")
RTU <- rt[ind_u]
order_u <- order(RTU)
# prepare arguments for .Call
dt_scale <- N_deps <- NULL
REAL <- c(dt_scale = opt[[3]], rt_max = opt[[1]], phi = phi)
REAL_RTL <- as.double(RTL[order_l])
REAL_RTU <- as.double(RTU[order_u])
INTEGER <- c(N_deps = opt[[2]], N_rtl = length(REAL_RTL), N_rtu = length(REAL_RTU), Nphi = length(phi))
CHAR <- modelname
# call C++ function
out <- .Call("CDF",
as.double(REAL),
as.integer(INTEGER),
as.double(REAL_RTL),
as.double(REAL_RTU),
as.character(CHAR))
# transform output
out$cdf <- numeric(length = len_rt)
out$cdf[ind_l] <- out$CDFlow[order_l]
out$cdf[ind_u] <- out$CDFupp[order_u]
out$log_cdf <- numeric(length = len_rt)
out$log_cdf[ind_l] <- out$logCDFlow[order_l]
out$log_cdf[ind_u] <- out$logCDFupp[order_u]
out$CDFlow <- out$CDFupp <- out$logCDFlow <- out$logCDFupp <- NULL
return(out)
}
########### RAND ###########
#' @rdname WTM
#' @useDynLib "ream", .registration=TRUE
#' @export
rWTM <- function(n,
phi,
dt = 0.00001) {
# constants
modelname <- "WTM"
Nphi <- 11
# check arguments
sim_checks(n, phi, Nphi, dt, modelname)
# more checks needed for limits etc.
# prepare arguments for .Call
REAL <- c(dt = dt, phi = phi)
INTEGER <- c(N = n, Nphi = length(phi))
CHAR <- modelname
# call C++ function
out <- .Call("SIM",
as.double(REAL),
as.integer(INTEGER),
as.character(CHAR))
# transform output
out$resp <- ifelse(out$rt >= 0, "upper", "lower")
out$rt <- abs(out$rt)
return(out)
}
########### GRID PDF ###########
#' Generate Grid for PDF of the Weibull Threshold Model
#'
#' Generate a grid of response-time values and the corresponding PDF values.
#' For more details on the model see, for example, \code{\link{dWTM}}.
#'
#' @param rt_max maximal response time <- max(rt)
#' @param phi parameter vector in the following order:
#' \enumerate{
#' \item Non-decision time (\eqn{t_{nd}}). Time for non-decision processes such as stimulus
#' encoding and response execution. Total decision time t is the sum of the decision
#' and non-decision times.
#' \item Relative start (\eqn{w}). Sets the start point of accumulation as a ratio of
#' the two decision thresholds. Related to the absolute start z point via equation
#' \eqn{z = b_l + w*(b_u - b_l)}.
#' \item Stimulus strength (\eqn{\mu}). Strength of the stimulus and used to set the drift
#' rate. For changing threshold models \eqn{v(x,t) = \mu}.
#' \item Noise scale (\eqn{\sigma}). Model noise scale parameter.
#' \item Initial decision threshold location (\eqn{b_0}). Sets the location of each decision
#' threshold at time \eqn{t = 0}.
#' \item Log10-decision threshold scale (\eqn{log_{10}(\lambda)}). Sets the approximate time for threshold
#' collapse or rise.
#' \item Log10-decision threshold shape (\eqn{log_{10}(\kappa)}). Sets the threshold shape. \eqn{\kappa > 1} produces
#' logistic-like thresholds, \eqn{\kappa < 1} produces exponential-like thresholds.
#' \item Collapse parameter (\eqn{c}). Sets the amount of collapse. \eqn{c = -1} gives collapse to
#' zero, \eqn{c = 1} gives no collapse, and \eqn{c > 1} gives rise.
#' \item Contamination (\eqn{g}). Sets the strength of the contamination process. Contamination
#' process is a uniform distribution \eqn{f_c(t)} where \eqn{f_c(t) = 1/(g_u-g_l)}
#' if \eqn{g_l <= t <= g_u} and \eqn{f_c(t) = 0} if \eqn{t < g_l} or \eqn{t > g_u}. It is
#' combined with PDF \eqn{f_i(t)} to give the final combined distribution
#' \eqn{f_{i,c}(t) = g*f_c(t) + (1-g)*f_i(t)}, which is then output by the program.
#' If \eqn{g = 0}, it just outputs \eqn{f_i(t)}.
#' \item Lower bound of contamination distribution (\eqn{g_l}). See parameter \eqn{g}.
#' \item Upper bound of contamination distribution (\eqn{g_u}). See parameter \eqn{g}.
#' }
#' @param x_res spatial/evidence resolution
#' @param t_res time resolution
#' @return list of RTs and corresponding defective PDFs at lower and upper threshold
#' @references
#' Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015).
#' Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual
#' Decision-Making. \emph{The Journal of Neuroscience, 35}(6), 2476-2484.
#'
#' Palestro, J. J., Weichart, E., Sederberg, P. B., & Turner, B. M. (2018). Some task demands
#' induce collapsing bounds: Evidence from a behavioral analysis. \emph{Psychonomic
#' Bulletin & Review, 25}(4), 1225-1248.
#' @author Raphael Hartmann & Matthew Murrow
#' @useDynLib "ream", .registration=TRUE
#' @export
dWTM_grid <- function(rt_max = 10.0,
phi,
x_res = "default",
t_res = "default") {
# constants
modelname <- "WTM"
Nphi <- 11
# checking input
grid_checks(rt_max, phi, Nphi, x_res, t_res, modelname)
# more specific checks
# setting options
opt <- grid_options(x_res, t_res)
# prepare arguments for r
dt_scale <- N_deps <- NULL
CHAR <- modelname
REAL <- c(dt_scale = dt_scale, rt_max = rt_max, phi = phi)
INTEGER <- c(N_deps = N_deps, N_phi = length(phi))
# call C++ function
out <- .Call("grid_pdf",
as.double(REAL),
as.integer(INTEGER),
as.character(CHAR))
return(out)
}
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