# R/quickpsy.R In quickpsy: Fits Psychometric Functions for Multiple Groups

#### Documented in quickpsy

#' Fits psychometric functions
#'
#' \code{quickpsy} fits, by direct maximization of the likelihood
#' (Prins and Kingdom, 2010; Knoblauch and Maloney, 2012),
#'  psychometric functions of the form
#' \deqn{\psi(x) = \gamma + (1 - \gamma - \lambda) * fun(x)}
#' where \eqn{\gamma} is the guess rate, \eqn{\lambda} is the lapse rate and
#' \eqn{fun} is a sigmoidal-shape function with asymppotes at 0 and 1.
#' @param d Data frame with the results of a Yes-No experiment to fit.
#' It should have a
#' each column corresponds to a variable and each row is an observation.
#' @param x Name of the explanatory variable.
#' @param k Name of the response variable. The response variable could be the
#' number of trials in which a yes-type response was given or a vector of 0s
#' (or -1s; no-type response) and 1s (yes-type response) indicating the
#' response on each trial.
#' @param n Only necessary if \code{k} refers to the number of trials
#' in which a yes-type response was given. It corresponds to the name of the
#' variable indicating the total number of trials.
#' @param grouping Name of the grouping variables. It should be specified as
#' \code{grouping = .(variable_name1, variable_name2)}.
#' @param random Name of the random variable. It should be specified as
#' \code{random = .(variable_name1, variable_name2)}. In the current version
#' of quickpsy, the random variable has not special treatment. It does the
#' same as \code{grouping}.
#' @param within Name of the within variable. It should be specified as
#' \code{within = .(variable_name1, variable_name2)}. In the current version
#' of quickpsy, the within variable has not special treatment. It does the
#' same as \code{grouping}.
#' @param between Name of the between variable.  It should be specified as
#' \code{between = .(variable_name1, variable_name2)}. In the current version
#' of quickpsy, the between variable has not special treatment. It does the
#' same as \code{grouping}.
#' @param xmin Minimum value of the explanatory variable for which the curves
#' should be calculated (the default is the minimum value of the explanatory
#' variable).
#' @param xmax Maximum value of the explanatory variable for which the curves
#' should be calculated (the default is the maximum value of the explanatory
#' variable).
#' @param log If \code{TRUE}, the logarithm of the explanatory variable is used
#' to fit the curves (default is \code{FALSE}).
#' @param fun Name of the shape of the curve to fit. It could be a predefined
#' shape (\code{cum_normal_fun}, \code{logistic_fun}, \code{weibull_fun})
#' or the name of a function introduced by the user
#' (default is \code{cum_normal_fun}).
#' @param parini Initial parameters. quickpsy calculates default
#' initial parameters using probit analysis, but it is also possible to
#' specify a vector of initial parameters or a list of the form
#' \code{list(c(par1min, par1max), c(par2min, par2max))} to
#' constraint the lower and upper bounds of the parameters (when
#' \code{optimization = 'DE'}, parini should be also a list).
#' @param guess Value indicating the guess rate \eqn{\gamma} (default is 0). If
#' \code{TRUE}, the guess rate is estimated as the i + 1 paramEter where
#' i corresponds to the number of parameters of \code{fun}. If, for
#' example, \code{fun} is a predefined shape with parameters p1 and p2,
#' then the guess rate corresponds to parameter p3.
#' @param lapses Value indicating the lapse rate \eqn{\lambda} (default is 0).
#'  If \code{TRUE}, the lapse rate is estimated as the i + 1 parameter where
#' i corresponds to the number of parameters of \code{fun} plus one if
#' the guess rate is estimated. If, for example, \code{fun} is a
#' predefined shape with parameters p1 and p2,
#' then the lapse rate corresponds to parameter p3. If the guess rate is also
#' estimated, p3 will be the guess rate and p4 the lapse rate.
#' @param prob Probability to calculate the threshold (default is
#' \code{guess + .5 * (1 - guess)}).
#' @param thresholds If \code{FALSE}, thresholds are not calculated
#' (default is \code{TRUE}).
#' @param bootstrap \code{'parametric'} performs parametric bootstrap;
#' \code{'nonparametric'} performs non-parametric bootstrap;
#' \code{'none'} does not perform bootstrap (default is \code{'parametric'}).
#' @param B number of bootstrap samples (default is 100 ONLY).
#' @param ci Confidence intervals level based on percentiles (default is .95).
#' @param optimization Method used for optimizization. The default is 'optim' which uses
#' the \code{optim} function. It can also be \code{'DE'} which uses de function
#' \code{DEoptim} from the package DEoptim, which performs differential
#' evolution optimization. By using \code{DEoptim}, it is less likely that the
#' optimization finishes in a local minimum, but the optimization is slow.
#' When \code{'DE'} is used, \code{parini} should be specified as a list with
#' lower and upper bounds.
#' @return A list containing the following components:
#' \itemize{
#'   \item \code{x, k, n}
#'   \item \code{groups} The grouping variables.
#'   \item \code{funname} String with the name of the shape of the curve.
#'   \item \code{psyfunguesslapses} Curve including guess and lapses.
#'   \item \code{limits} Limits of the curves.
#'   \item \code{parini} Initial parameters.
#'   \item \code{optimization} Method to optimize.
#'   \item \code{pariniset} \code{FALSE} if initial parameters are not given.
#'   \item \code{ypred} Predicted probabilities at the values of the explanatory
#'   variable.
#'   \item \code{curves} Curves.
#'   \item \code{par} Fitted parameters and its confidence intervals.
#'   \item \code{curvesbootstrap} Bootstrap curves.
#'   \item \code{thresholds} Thresholds.
#'   \item \code{thresholdsci} Confidence intervals for the thresholds.
#'   \item \code{logliks} Log-likelihoods of the model.
#'   \item \code{loglikssaturated} Log-likelihoods of the saturated model.
#'   \item \code{deviance} Deviance of the model and the p-value calculated by
#'    bootstraping.
#'   \item \code{aic} AIC of the model defined as \deqn{ - 2 * loglik + 2  *k}
#'   where k is the number of parameters of the model.
#' }
#' @references
#' Burnham, K. P., & Anderson, D. R. (2003). Model selection and multimodel
#' inference: a practical information-theoretic approach. Springer Science &
#'
#' Knoblauch, K., & Maloney, L. T. (2012). Modeling Psychophysical Data in R.
#' New York: Springer.
#'
#' Prins, N., & Kingdom, F. A. A. (2016). Psychophysics: a practical
#' @examples
#' # make sure that all the requires packages are installed
#' # and loaded; instructions at https://github.com/danilinares/quickpsy
#' library(MPDiR) # contains the Vernier data; use ?Vernier for the reference
#' fit <- quickpsy(Vernier, Phaseshift, NumUpward, N,
#'                 grouping = .(Direction, WaveForm, TempFreq), B = 10)
#' plotcurves(fit)
#' plotpar(fit)
#' plotthresholds(fit, geom = 'point')

#' @export
#' @import MPDiR
#' @importFrom  graphics par
#' @importFrom stats approx as.formula lm median optim pnorm pweibull qnorm
#' quantile qweibull rbinom

quickpsy <- function(d, x = x, k = k, n = n, grouping, random, within, between,
xmin = NULL, xmax = NULL, log = FALSE,
fun = cum_normal_fun, parini = NULL, guess = 0, lapses = 0,
prob = NULL, thresholds = T,
bootstrap = 'parametric', B = 100, ci = .95,
optimization = 'optim') {

x <- deparse(substitute(x))
k <- deparse(substitute(k))
fun <- deparse(substitute(fun))
if (!missing(n)) n <- deparse(substitute(n))
else n <- NULL
if (!missing(random)) random <- as.character(substitute(random))[-1]
if (!missing(within)) within <- as.character(substitute(within))[-1]
if (!missing(between)) between <- as.character(substitute(between))[-1]
if (!missing(grouping)) grouping <- as.character(substitute(grouping))[-1]

### calling the standard evaluation of quickpsy
quickpsy_(d, x, k, n, grouping, random, within, between, xmin, xmax, log, fun,
parini, guess, lapses, prob, thresholds, bootstrap,
B, ci, optimization)
}

#' Data set for demonstration
#'
#' It is part of the data associated with the paper 'Motion signal and
#' the perceived positions of moving objects'.
#' @name qpdat
#' @docType data
#' @references Linares, D., López-Moliner, J., & Johnston, A. (2007). Motion
#'signal and the perceived positions of moving objects. Journal of Vision,
#' 7(7), 1.

'qpdat'


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quickpsy documentation built on May 30, 2017, 1:41 a.m.