Nothing
R/zest.r
In OPI: Open Perimetry Interface
Defines functions
ZEST.final
ZEST.stop
ZEST.step
ZEST.start
ZEST
ZEST.entropy
ZEST.stdev
Documented in
ZEST
ZEST.final
ZEST.start
ZEST.step
ZEST.stop
#
# ZEST algorithm that maintains state in a list - good for interleaved.
# Includes
# ZEST # just for a single location
# ZEST.start # initialise list state
# ZEST.step # take state, present stim, update and return state
# ZEST.stop # boolean - true if state is finished
# ZEST.final # return final estimate from state
#
# Author: Andrew Turpin
# Date: August 2012
# Modified Tue 21 Mar 2023: changed licence from gnu to Apache 2.0
#
# Copyright [2022] [Andrew Turpin]
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
require(stats)
require(utils)
##########################
# little helper functions
##########################
ZEST.stdev <- function(state) { sqrt(sum(state$pdf*state$domain*state$domain) - sum(state$pdf * state$domain)^2) }
ZEST.entropy <- function(state) {
z <- which(state$pdf > 0)
return(-sum(state$pdf[z] * log2(state$pdf[z])))
}
#' @rdname ZEST
#' @title ZEST
#' @description An implementation of the Bayesian test procedures of King-Smith et al.
#' and Watson and Pelli. Note that we use the term \code{pdf} throughout as in the
#' original paper, even though they are discrete probability functions in this
#' implementation.
#' @param domain Vector of values over which pdf is kept.
#' @param prior Starting probability distribution over domain. Same length as \code{domain}.
#' @param likelihood Matrix where \code{likelihood[s,t]} is likelihood of seeing \code{s}
#' given \code{t} is the true threshold. That is, Pr(s|t) where \code{s} and \code{t} are
#' indexes into \code{domain}.
#' @param stopType \code{N}, for number of presentations; \code{S}, for standard deviation
#' of the pdf; and \code{H}, for the entropy of the pdf.
#' @param stopValue Value for number of presentations (\code{stopType=N}), standard deviation
#' (\code{stopType=S)} or Entropy (\code{stopType=H}).
#' @param minStimulus The smallest stimuli that will be presented. Could be different from
#' \code{domain[1]}.
#' @param maxStimulus The largest stimuli that will be presented. Could be different from
#' \code{tail(domain,1)}.
#' @param minNotSeenLimit Will terminate if \code{minStimulus} value is not seen this many times.
#' @param maxSeenLimit Will terminate if \code{maxStimulus} value is seen this many times.
#' @param maxPresentations Maximum number of presentations regardless of \code{stopType}.
#' @param minInterStimInterval If both \code{minInterStimInterval} and \code{maxInterStimInterval}
#' are not \code{NA}, then between each stimuli there is a random wait period drawn uniformly
#' between \code{minInterStimInterval} and \code{maxInterStimInterval}.
#' @param maxInterStimInterval \code{minInterStimInterval}.
#' @param verbose \code{verbose=0} does nothing, \code{verbose=1} stores pdfs for returning,
#' and \code{verbose=2} stores pdfs and also prints each presentation.
#' @param makeStim A function that takes a dB value and numPresentations and returns an OPI datatype
#' ready for passing to opiPresent. See examples.
#' @param stimChoice A true ZEST procedure uses the \code{"mean"} of the current pdf as the stimulus,
#' but \code{"median"} and \code{"mode"} (as used in a QUEST procedure) are provided for your
#' enjoyment.
#' @param ... Extra parameters to pass to the opiPresent function
#' @details This is an implementation of King-Smith et al.'s ZEST procedure and Watson and Pelli's
#' QUEST procedure. All presentations are rounded to an element of the supplied domain.
#'
#' Note this function will repeatedly call \code{opiPresent} for a stimulus until \code{opiPresent}
#' returns \code{NULL} (ie no error occurred).
#'
#' The \code{checkFixationOK} function is called (if present in stim made from \code{makeStim})
#' after each presentation, and if it returns FALSE, the pdf for that location is not changed
#' (ie the presentation is ignored), but the stim, number of presentations etc is recorded in
#' the state.
#'
#' If more than one ZEST is to be interleaved (for example, testing multiple locations), then the
#' \code{ZEST.start}, \code{ZEST.step}, \code{ZEST.stop} and \code{ZEST.final} calls can maintain
#' the state of the ZEST after each presentation, and should be used. If only a single ZEST is
#' required, then the simpler \code{ZEST} can be used, which is a wrapper for the four functions
#' that maintain state. See examples below.
#' @return
#' ## Single location
#' \code{ZEST} returns a list containing
#' * \code{npres} Total number of presentations used.
#' * \code{respSeq} Response sequence stored as a matrix: row 1 is dB values of stimuli, row 2
#' * is 1/0 for seen/not-seen, row 3 is fixated 1/0 (always 1 if \code{checkFixationOK} not
#' * present in stim objects returned from \code{makeStim}).
#' * \code{pdfs} If \code{verbose} is bigger than 0, then this is a list of the pdfs used for each presentation, otherwise NULL.
#' * \code{final} The mean/median/mode of the final pdf, depending on \code{stimChoice}, which is the determined threshold.
#' * \code{opiResp} A list of responses received from each successful call to \code{opiPresent} within \code{ZEST}.
#'
#' ## Multilple locations
#' \code{ZEST.start} returns a list that can be passed to \code{ZEST.step}, \code{ZEST.stop}, and
#' \code{ZEST.final}. It represents the state of a ZEST at a single location at a point in time
#' and contains the following.
#' * \code{name} \code{ZEST}.
#' * \code{pdf} Current pdf: vector of probabilities the same length as \code{domain}.
#' * \code{numPresentations} The number of times \code{ZEST.step} has been called on this state.
#' * \code{stimuli} A vector containing the stimuli used at each call of \code{ZEST.step}.
#' * \code{responses} A vector containing the responses received at each call of \code{ZEST.step}.
#' * \code{responseTimes} A vector containing the response times received at each call of \code{ZEST.step}.
#' * \code{fixated} A vector containing TRUE/FALSE if fixation was OK according to
#' \code{checkFixationOK} for each call of \code{ZEST.step} (defaults to TRUE if
#' \code{checkFixationOK} not present).
#' * \code{opiResp} A list of responses received from each call to \code{opiPresent} within \code{ZEST.step}.
#' * A copy of all of the parameters supplied to ZEST.start: \code{domain},
#' \code{likelihood}, \code{stopType}, \code{stopValue}, \code{minStimulus}, \code{maxStimulus},
#' \code{maxSeenLimit}, \code{minNotSeenLimit}, \code{maxPresentations}, \code{makeStim},
#' \code{stimChoice}, \code{currSeenLimit}, \code{currNotSeenLimit}, and \code{opiParams}.
#'
#' \code{ZEST.step} returns a list containing
#' * \code{state} The new state after presenting a stimuli and getting a response.
#' * \code{resp} The return from the \code{opiPresent} call that was made.
#'
#' \code{ZEST.stop} returns \code{TRUE} if the ZEST has reached its stopping criteria, and \code{FALSE} otherwise.
#'
#' \code{ZEST.final} returns an estimate of threshold based on state. If \code{state$stimChoice}
#' is \code{mean} then the mean is returned. If \code{state$stimChoice} is \code{mode} then the
#' mode is returned. If \code{state$stimChoice} is \code{median} then the median is returned.
#'
#' @references
#' P.E. King-Smith, S.S. Grigsny, A.J. Vingrys, S.C. Benes, and A. Supowit. "Efficient and Unbiased
#' Modifications of the QUEST Threshold Method: Theory, Simulations, Experimental Evaluation and
#' Practical Implementation", Vision Research 34(7) 1994. Pages 885-912.
#'
#' A.B. Watson and D.G. Pelli. "QUEST: A Bayesian adaptive psychophysical method", Perception and
#' Psychophysics 33 1983. Pages 113-l20.
#'
#' A. Turpin, P.H. Artes and A.M. McKendrick "The Open Perimetry Interface: An enabling tool for
#' clinical visual psychophysics", Journal of Vision 12(11) 2012.
#' @seealso \code{\link{dbTocd}}, \code{\link{opiPresent}}
#' @examples
#' chooseOpi("SimHenson")
#' if(!is.null(opiInitialize(type="C", cap=6)))
#' stop("opiInitialize failed")
#'
#' ##############################################
#' # This section is for single location ZESTs
#' ##############################################
#' # Stimulus is Size III white-on-white as in the HFA
#' makeStim <- function(db, n) {
#' s <- list(x=9, y=9, level=dbTocd(db), size=0.43, color="white",
#' duration=200, responseWindow=1500, checkFixationOK=NULL)
#' class(s) <- "opiStaticStimulus"
#' return(s)
#' }
#'
#' repp <- function(...) sapply(1:50, function(i) ZEST(makeStim=makeStim, ...))
#' a <- repp(stopType="H", stopValue= 3, verbose=0, tt=30, fpr=0.03)
#' b <- repp(stopType="S", stopValue=1.5, verbose=0, tt=30, fpr=0.03)
#' c <- repp(stopType="S", stopValue=2.0, verbose=0, tt=30, fpr=0.03)
#' d <- repp(stopType="N", stopValue= 50, verbose=0, tt=30, fpr=0.03)
#' e <- repp(prior=dnorm(0:40,m=0,s=5), tt=30, fpr=0.03)
#' f <- repp(prior=dnorm(0:40,m=10,s=5), tt=30, fpr=0.03)
#' g <- repp(prior=dnorm(0:40,m=20,s=5), tt=30, fpr=0.03)
#' h <- repp(prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03)
#'
#' layout(matrix(1:2,1,2))
#' boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["final",])))
#' boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["npres",])))
#'
#' ##############################################
#' # This section is for multiple ZESTs
#' ##############################################
#' makeStimHelper <- function(db,n, x, y) { # returns a function of (db,n)
#' ff <- function(db, n) db+n
#' body(ff) <- substitute({
#' s <- list(x=x, y=y, level=dbTocd(db), size=0.43, color="white",
#' duration=200, responseWindow=1500, checkFixationOK=NULL)
#' class(s) <- "opiStaticStimulus"
#' return(s)
#' }, list(x=x,y=y))
#' return(ff)
#' }
#'
#' # List of (x, y, true threshold) triples
#' locations <- list(c(9,9,30), c(-9,-9,32), c(9,-9,31), c(-9,9,33))
#'
#' # Setup starting states for each location
#' states <- lapply(locations, function(loc) {
#' ZEST.start(
#' domain=-5:45,
#' minStimulus=0,
#' maxStimulus=40,
#' makeStim=makeStimHelper(db,n,loc[1],loc[2]),
#' stopType="S", stopValue= 1.5, tt=loc[3], fpr=0.03, fnr=0.01)})
#'
#' # Loop through until all states are "stop"
#' while(!all(st <- unlist(lapply(states, ZEST.stop)))) {
#' i <- which(!st) # choose a random,
#' i <- i[runif(1, min=1, max=length(i))] # unstopped state
#' r <- ZEST.step(states[[i]]) # step it
#' states[[i]] <- r$state # update the states
#' }
#'
#' finals <- lapply(states, ZEST.final) # get final estimates of threshold
#' for(i in 1:length(locations)) {
#' #cat(sprintf("Location (%+2d,%+2d) ",locations[[i]][1], locations[[i]][2]))
#' #cat(sprintf("has threshold %4.2f\n", finals[[i]]))
#' }
#'
#' if (!is.null(opiClose()))
#' warning("opiClose() failed")
#' @export
ZEST <- function(domain = 0:40, prior = rep(1 / length(domain),length(domain)),
likelihood = sapply(domain, function(tt) 0.03 + (1-0.03-0.03)*(1-stats::pnorm(domain, tt, 1))),
stopType="S",
stopValue = 1.5,
minStimulus = utils::head(domain, 1),
maxStimulus = utils::tail(domain, 1),
maxSeenLimit = 2,
minNotSeenLimit = 2,
maxPresentations = 100,
minInterStimInterval = NA,
maxInterStimInterval = NA,
verbose = 0, makeStim,
stimChoice="mean",
...) {
state <- ZEST.start(domain, prior, likelihood, stopType, stopValue,
minStimulus, maxStimulus,
maxSeenLimit,minNotSeenLimit,maxPresentations,
makeStim,stimChoice, ...)
pdfs <- NULL
while(!ZEST.stop(state)) {
r <- ZEST.step(state)
state <- r$state
if (verbose == 2) {
cat(sprintf("Presentation %2d: ", state$numPresentations))
cat(sprintf("stim= %5s response=%s ", utils::tail(state$stimuli,1), utils::tail(state$responses,1)))
cat(sprintf("fixation= %1.0g ", utils::tail(state$fixated,1)))
cat(sprintf("stdev= %8.4g H= %8.4g\n", ZEST.stdev(state), ZEST.entropy(state)))
}
if (verbose > 0)
pdfs <- c(pdfs, list(state$pdf))
if (!is.na(minInterStimInterval) && !is.na(maxInterStimInterval))
Sys.sleep(stats::runif(1, min = minInterStimInterval, max = maxInterStimInterval)/1000)
}
return(list(
npres = utils::tail(state$numPresentations,1), # number of presentations
respSeq = mapply(c, state$stimuli, state$responses, state$fixated), # response sequence (list of triples)
pdfs = pdfs, # list of pdfs used (if verbose > 0)
final = ZEST.final(state), # final threshold estimate
opiResp = state$opiResp # list of all responses from opiPresent
))
}#ZEST
#' @rdname ZEST
#' @export
ZEST.start <- function(domain = 0:40, prior = rep(1 / length(domain),length(domain)),
likelihood = sapply(domain, function(tt) 0.03 + (1-0.03-0.03)*(1-stats::pnorm(domain, tt, 1))),
stopType = "S",
stopValue = 1.5,
minStimulus = utils::head(domain, 1),
maxStimulus = utils::tail(domain, 1),
maxSeenLimit = 2,
minNotSeenLimit = 2,
maxPresentations = 100,
makeStim,
stimChoice = "mean",
...) {
##########################
# Validate params
##########################
if (!is.element(stopType, c("S", "H", "N")))
stop("ZEST.start: stopType must be one of 'S', 'N', or 'H'")
if (nrow(likelihood) != length(domain))
stop(paste("ZEST.start: not enough rows in likelihood. Expect", length(domain)))
if (ncol(likelihood) != length(domain))
stop(paste("ZEST.start: not enough cols in likelihood. Expect", length(domain)))
if (!is.element(minStimulus, domain))
stop(paste("ZEST.start: you specified minStimulus =", minStimulus, "but it is not in 'domain'."))
if (!is.element(maxStimulus, domain))
stop(paste("ZEST.start: you specified maxStimulus =", maxStimulus, "but it is not in 'domain'."))
pdf <- prior/sum(prior)
return(list(name = "ZEST",
domain = domain,
pdf = pdf,
likelihood = likelihood,
stopType = stopType,
stopValue = stopValue,
minStimulus = minStimulus,
maxStimulus = maxStimulus,
maxSeenLimit = maxSeenLimit,
minNotSeenLimit = minNotSeenLimit,
maxPresentations = maxPresentations,
makeStim = makeStim,
stimChoice = stimChoice,
currSeenLimit = 0, # number of times maxStimulus seen
currNotSeenLimit = 0, # number of times minStimulus not seen
numPresentations = 0, # number of presentations so far
stimuli = NULL, # vector of stims shown
responses = NULL, # vector of responses (1 seen, 0 not)
responseTimes = NULL, # vector of response times
fixated = NULL, # vector of true/false for fixated one per stimuli
opiResp = NULL, # list of opiPresent return values
opiParams = list(...) # the extra params
))
}# ZEST.start
#' @rdname ZEST
#' @param state Current state of the ZEST returned by \code{ZEST.start} and \code{ZEST.step}.
#' @param nextStim A valid object for \code{opiPresent} to use as its \code{nextStim}.
#' @param fixedStimValue A number in \code{state$domain} that, is \code{!is.na}, will be used as the stimulus value
#' overriding \code{state$minStimulus}, \code{state$maxStimulus} and \code{state$stimChoice}.
#' @export
ZEST.step <- function(state, nextStim = NULL, fixedStimValue = NA) {
if (!is.na(fixedStimValue)) {
if (fixedStimValue %in% state$domain) {
stim <- fixedStimValue
} else {
stop(paste("ZEST.step: fixedStimValue = ", fixedStimValue,"is not in 'domain'."))
}
} else {
if (state$stimChoice == "mean") {
stimIndex <- which.min(abs(state$domain - sum(state$pdf * state$domain)))
} else if (state$stimChoice == "mode") {
stimIndex <- which.max(state$pdf)
} else if (state$stimChoice == "median") {
stimIndex <- which.min(abs(cumsum(state$pdf) - 0.5))
} else {
stop(paste("ZEST.step: stimChoice = ",state$stimChoice," not implemented."))
}
stim <- state$domain[stimIndex]
if (stim < state$minStimulus)
stim <- state$minStimulus
if (stim > state$maxStimulus)
stim <- state$maxStimulus
}
stimIndex <- which(stim == state$domain) # in case it changed via fixed, min or max
params <- c(list(stim = state$makeStim(stim, state$numPresentations), nextStim = nextStim), state$opiParams)
opiResp <- do.call(opiPresent, params)
if (!is.null(opiResp$err))
return(list(state = state, resp = opiResp))
fixation_is_good <- TRUE
if (!is.null(params$stim$checkFixationOK)) {
fixation_is_good <- params$stim$checkFixationOK(opiResp)
}
state$stimuli <- c(state$stimuli, stim)
state$responses <- c(state$responses, opiResp$seen)
state$responseTimes <- c(state$responseTimes, opiResp$time)
state$numPresentations <- state$numPresentations + 1
state$fixated <- c(state$fixated, fixation_is_good)
state$opiResp <- c(state$opiResp, list(opiResp))
if (fixation_is_good) { # update the pdf
if(opiResp$seen) {
if (stim == state$maxStimulus) state$currSeenLimit <- state$currSeenLimit + 1
state$pdf <- state$pdf * state$likelihood[stimIndex, ]
} else {
if (stim == state$minStimulus) state$currNotSeenLimit <- state$currNotSeenLimit + 1
state$pdf <- state$pdf * (1 - state$likelihood[stimIndex, ])
}
state$pdf <- state$pdf/sum(state$pdf)
} else {
warning("ZEST.step: fixation lost during presentation, pdf not updated")
}
return(list(state = state, resp = opiResp))
}#ZEST.step()
#' @rdname ZEST
#' @export
ZEST.stop <- function(state) {
keepGoing <- (
(state$numPresentations < state$maxPresentations) &&
(state$currNotSeenLimit < state$minNotSeenLimit) &&
(state$currSeenLimit < state$maxSeenLimit) &&
(
((state$stopType == "S") && (ZEST.stdev(state) > state$stopValue))
|| ((state$stopType == "H") && (ZEST.entropy(state) > state$stopValue))
|| ((state$stopType == "N") && (state$numPresentations < state$stopValue))
)
)
return(!keepGoing)
}#ZEST.stop
#' @rdname ZEST
#' @export
ZEST.final <- function(state) {
if (state$stimChoice == "mean") {
final <- sum(state$pdf*state$domain)
} else if (state$stimChoice == "mode") {
final <- state$domain[which.max(state$pdf)]
} else if (state$stimChoice == "median") {
final <- state$domain[which.min(abs(cumsum(state$pdf) - 0.5))]
}
return(final)
}#ZEST.final
############################################################
# Tests
############################################################
#require(OPI)
#chooseOpi("SimHenson")
##chooseOpi("SimYes")
#opiInitialize("C",6)
#
#makeStim <- function(db, n) {
# s <- list(x=9, y=9, level=dbTocd(db,10000/pi), size=0.43,
# color="white",
# duration=200, responseWindow=1500,
# checkFixationOK=NULL)
# class(s) <- "opiStaticStimulus"
#
# return(s)
# }
#makeNextStim <- function(x,y) {
# s <- list(x=9, y=9, level=dbTocd(db,10000/pi), size=0.43, color="white",
# duration=200, responseWindow=1500, checkFixationOK=NULL)
# class(s) <- "opiStaticStimulus"
#
# return(s)
# }
#
#state <- ZEST.start(domain=-5:45, maxStimulus=40, minStimulus=0, makeStim=makeStim, stopType="S", stopValue= 1.5, tt=0, fpr=0.30)
#while(!ZEST.stop(state)) {
# r <- ZEST.step(state)
# cat(sprintf("%2d %s\n",tail(r$state$stimuli,1), r$resp$seen))
# state <- r$state
#}
#print(ZEST.final(state))
#
#print("########################################################################")
#
#makeStimHelper <- function(db,n, x, y) {
# ff <- function(db, n) db+n
#
# body(ff) <- substitute(
# {s <- list(x=x, y=y, level=dbTocd(db,10000/pi), size=0.43, color="white",
# duration=200, responseWindow=1500, checkFixationOK=NULL)
# class(s) <- "opiStaticStimulus"
# return(s)
# }
# , list(x=x,y=y))
# return(ff)
#}
#
# # list of (x, y, true threshold) triples
#locations <- list(c(9,9,30), c(-9,-9,32), c(9,-9,31), c(-9,9,33))
#
# # setup starting states for each location
#states <- lapply(locations, function(loc) {
# ZEST.start(domain=-5:45,
# makeStim=makeStimHelper(db,n,loc[1],loc[2]),
# maxStimulus=40, minStimulus=0,
# stopType="S", stopValue= 1.5, tt=loc[3], fpr=0.03, fn=0.01)
#})
#
# # loop through until all states are "stop"
#while(!all(st <- unlist(lapply(states, ZEST.stop)))) {
# i <- sample(which(!st), 1) # choose a random, unstopped state
# r <- ZEST.step(states[[i]]) # step it
# states[[i]] <- r$state # update the states
#}
#
#finals <- lapply(states, ZEST.final) # get final estimates of threshold
#for(i in 1:length(locations))
# cat(sprintf("Location (%+2d,%+2d) has threshold %4.2f\n",locations[[i]][1], locations[[i]][2], finals[[i]]))
#
#print("########################################################################")
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="H", stopValue= 3, verbose=0, tt=20, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="S", stopValue=1.5, verbose=0, tt=20, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="S", stopValue=2.0, verbose=0, tt=20, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="N", stopValue= 50, verbose=0, tt=20, fpr=0.03))
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="mean", tt=20, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="mode", tt=20, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="median", tt=20, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="mean", tt=20, fpr=0.03))
#
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["npres",])))
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=0,s=5), tt=30, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=10,s=5), tt=30, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=20,s=5), tt=30, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03))
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["npres",])))
#
#repp <- function(...) sapply(1:100, function(i) ZEST(makeStim=makeStim, ...))
#a <- repp(stopType="H", stopValue= 3, verbose=0, tt=30, fpr=0.03)
#b <- repp(stopType="S", stopValue=1.5, verbose=0, tt=30, fpr=0.03)
#c <- repp(stopType="S", stopValue=2.0, verbose=0, tt=30, fpr=0.03)
#d <- repp(stopType="N", stopValue= 50, verbose=0, tt=30, fpr=0.03)
#e <- repp(prior=dnorm(0:40,m=0,s=5), tt=30, fpr=0.03)
#f <- repp(prior=dnorm(0:40,m=10,s=5), tt=30, fpr=0.03)
#g <- repp(prior=dnorm(0:40,m=20,s=5), tt=30, fpr=0.03)
#h <- repp(prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03)
#
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["npres",])))
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=00, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=10, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=20, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03))
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["npres",])))
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Defines functions ZEST.final ZEST.stop ZEST.step ZEST.start ZEST ZEST.entropy ZEST.stdev
Documented in ZEST ZEST.final ZEST.start ZEST.step ZEST.stop
#
# ZEST algorithm that maintains state in a list - good for interleaved.
# Includes
# ZEST # just for a single location
# ZEST.start # initialise list state
# ZEST.step # take state, present stim, update and return state
# ZEST.stop # boolean - true if state is finished
# ZEST.final # return final estimate from state
#
# Author: Andrew Turpin
# Date: August 2012
# Modified Tue 21 Mar 2023: changed licence from gnu to Apache 2.0
#
# Copyright [2022] [Andrew Turpin]
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
require(stats)
require(utils)
##########################
# little helper functions
##########################
ZEST.stdev <- function(state) { sqrt(sum(state$pdf*state$domain*state$domain) - sum(state$pdf * state$domain)^2) }
ZEST.entropy <- function(state) {
z <- which(state$pdf > 0)
return(-sum(state$pdf[z] * log2(state$pdf[z])))
}
#' @rdname ZEST
#' @title ZEST
#' @description An implementation of the Bayesian test procedures of King-Smith et al.
#' and Watson and Pelli. Note that we use the term \code{pdf} throughout as in the
#' original paper, even though they are discrete probability functions in this
#' implementation.
#' @param domain Vector of values over which pdf is kept.
#' @param prior Starting probability distribution over domain. Same length as \code{domain}.
#' @param likelihood Matrix where \code{likelihood[s,t]} is likelihood of seeing \code{s}
#' given \code{t} is the true threshold. That is, Pr(s|t) where \code{s} and \code{t} are
#' indexes into \code{domain}.
#' @param stopType \code{N}, for number of presentations; \code{S}, for standard deviation
#' of the pdf; and \code{H}, for the entropy of the pdf.
#' @param stopValue Value for number of presentations (\code{stopType=N}), standard deviation
#' (\code{stopType=S)} or Entropy (\code{stopType=H}).
#' @param minStimulus The smallest stimuli that will be presented. Could be different from
#' \code{domain[1]}.
#' @param maxStimulus The largest stimuli that will be presented. Could be different from
#' \code{tail(domain,1)}.
#' @param minNotSeenLimit Will terminate if \code{minStimulus} value is not seen this many times.
#' @param maxSeenLimit Will terminate if \code{maxStimulus} value is seen this many times.
#' @param maxPresentations Maximum number of presentations regardless of \code{stopType}.
#' @param minInterStimInterval If both \code{minInterStimInterval} and \code{maxInterStimInterval}
#' are not \code{NA}, then between each stimuli there is a random wait period drawn uniformly
#' between \code{minInterStimInterval} and \code{maxInterStimInterval}.
#' @param maxInterStimInterval \code{minInterStimInterval}.
#' @param verbose \code{verbose=0} does nothing, \code{verbose=1} stores pdfs for returning,
#' and \code{verbose=2} stores pdfs and also prints each presentation.
#' @param makeStim A function that takes a dB value and numPresentations and returns an OPI datatype
#' ready for passing to opiPresent. See examples.
#' @param stimChoice A true ZEST procedure uses the \code{"mean"} of the current pdf as the stimulus,
#' but \code{"median"} and \code{"mode"} (as used in a QUEST procedure) are provided for your
#' enjoyment.
#' @param ... Extra parameters to pass to the opiPresent function
#' @details This is an implementation of King-Smith et al.'s ZEST procedure and Watson and Pelli's
#' QUEST procedure. All presentations are rounded to an element of the supplied domain.
#'
#' Note this function will repeatedly call \code{opiPresent} for a stimulus until \code{opiPresent}
#' returns \code{NULL} (ie no error occurred).
#'
#' The \code{checkFixationOK} function is called (if present in stim made from \code{makeStim})
#' after each presentation, and if it returns FALSE, the pdf for that location is not changed
#' (ie the presentation is ignored), but the stim, number of presentations etc is recorded in
#' the state.
#'
#' If more than one ZEST is to be interleaved (for example, testing multiple locations), then the
#' \code{ZEST.start}, \code{ZEST.step}, \code{ZEST.stop} and \code{ZEST.final} calls can maintain
#' the state of the ZEST after each presentation, and should be used. If only a single ZEST is
#' required, then the simpler \code{ZEST} can be used, which is a wrapper for the four functions
#' that maintain state. See examples below.
#' @return
#' ## Single location
#' \code{ZEST} returns a list containing
#' * \code{npres} Total number of presentations used.
#' * \code{respSeq} Response sequence stored as a matrix: row 1 is dB values of stimuli, row 2
#' * is 1/0 for seen/not-seen, row 3 is fixated 1/0 (always 1 if \code{checkFixationOK} not
#' * present in stim objects returned from \code{makeStim}).
#' * \code{pdfs} If \code{verbose} is bigger than 0, then this is a list of the pdfs used for each presentation, otherwise NULL.
#' * \code{final} The mean/median/mode of the final pdf, depending on \code{stimChoice}, which is the determined threshold.
#' * \code{opiResp} A list of responses received from each successful call to \code{opiPresent} within \code{ZEST}.
#'
#' ## Multilple locations
#' \code{ZEST.start} returns a list that can be passed to \code{ZEST.step}, \code{ZEST.stop}, and
#' \code{ZEST.final}. It represents the state of a ZEST at a single location at a point in time
#' and contains the following.
#' * \code{name} \code{ZEST}.
#' * \code{pdf} Current pdf: vector of probabilities the same length as \code{domain}.
#' * \code{numPresentations} The number of times \code{ZEST.step} has been called on this state.
#' * \code{stimuli} A vector containing the stimuli used at each call of \code{ZEST.step}.
#' * \code{responses} A vector containing the responses received at each call of \code{ZEST.step}.
#' * \code{responseTimes} A vector containing the response times received at each call of \code{ZEST.step}.
#' * \code{fixated} A vector containing TRUE/FALSE if fixation was OK according to
#' \code{checkFixationOK} for each call of \code{ZEST.step} (defaults to TRUE if
#' \code{checkFixationOK} not present).
#' * \code{opiResp} A list of responses received from each call to \code{opiPresent} within \code{ZEST.step}.
#' * A copy of all of the parameters supplied to ZEST.start: \code{domain},
#' \code{likelihood}, \code{stopType}, \code{stopValue}, \code{minStimulus}, \code{maxStimulus},
#' \code{maxSeenLimit}, \code{minNotSeenLimit}, \code{maxPresentations}, \code{makeStim},
#' \code{stimChoice}, \code{currSeenLimit}, \code{currNotSeenLimit}, and \code{opiParams}.
#'
#' \code{ZEST.step} returns a list containing
#' * \code{state} The new state after presenting a stimuli and getting a response.
#' * \code{resp} The return from the \code{opiPresent} call that was made.
#'
#' \code{ZEST.stop} returns \code{TRUE} if the ZEST has reached its stopping criteria, and \code{FALSE} otherwise.
#'
#' \code{ZEST.final} returns an estimate of threshold based on state. If \code{state$stimChoice}
#' is \code{mean} then the mean is returned. If \code{state$stimChoice} is \code{mode} then the
#' mode is returned. If \code{state$stimChoice} is \code{median} then the median is returned.
#'
#' @references
#' P.E. King-Smith, S.S. Grigsny, A.J. Vingrys, S.C. Benes, and A. Supowit. "Efficient and Unbiased
#' Modifications of the QUEST Threshold Method: Theory, Simulations, Experimental Evaluation and
#' Practical Implementation", Vision Research 34(7) 1994. Pages 885-912.
#'
#' A.B. Watson and D.G. Pelli. "QUEST: A Bayesian adaptive psychophysical method", Perception and
#' Psychophysics 33 1983. Pages 113-l20.
#'
#' A. Turpin, P.H. Artes and A.M. McKendrick "The Open Perimetry Interface: An enabling tool for
#' clinical visual psychophysics", Journal of Vision 12(11) 2012.
#' @seealso \code{\link{dbTocd}}, \code{\link{opiPresent}}
#' @examples
#' chooseOpi("SimHenson")
#' if(!is.null(opiInitialize(type="C", cap=6)))
#' stop("opiInitialize failed")
#'
#' ##############################################
#' # This section is for single location ZESTs
#' ##############################################
#' # Stimulus is Size III white-on-white as in the HFA
#' makeStim <- function(db, n) {
#' s <- list(x=9, y=9, level=dbTocd(db), size=0.43, color="white",
#' duration=200, responseWindow=1500, checkFixationOK=NULL)
#' class(s) <- "opiStaticStimulus"
#' return(s)
#' }
#'
#' repp <- function(...) sapply(1:50, function(i) ZEST(makeStim=makeStim, ...))
#' a <- repp(stopType="H", stopValue= 3, verbose=0, tt=30, fpr=0.03)
#' b <- repp(stopType="S", stopValue=1.5, verbose=0, tt=30, fpr=0.03)
#' c <- repp(stopType="S", stopValue=2.0, verbose=0, tt=30, fpr=0.03)
#' d <- repp(stopType="N", stopValue= 50, verbose=0, tt=30, fpr=0.03)
#' e <- repp(prior=dnorm(0:40,m=0,s=5), tt=30, fpr=0.03)
#' f <- repp(prior=dnorm(0:40,m=10,s=5), tt=30, fpr=0.03)
#' g <- repp(prior=dnorm(0:40,m=20,s=5), tt=30, fpr=0.03)
#' h <- repp(prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03)
#'
#' layout(matrix(1:2,1,2))
#' boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["final",])))
#' boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["npres",])))
#'
#' ##############################################
#' # This section is for multiple ZESTs
#' ##############################################
#' makeStimHelper <- function(db,n, x, y) { # returns a function of (db,n)
#' ff <- function(db, n) db+n
#' body(ff) <- substitute({
#' s <- list(x=x, y=y, level=dbTocd(db), size=0.43, color="white",
#' duration=200, responseWindow=1500, checkFixationOK=NULL)
#' class(s) <- "opiStaticStimulus"
#' return(s)
#' }, list(x=x,y=y))
#' return(ff)
#' }
#'
#' # List of (x, y, true threshold) triples
#' locations <- list(c(9,9,30), c(-9,-9,32), c(9,-9,31), c(-9,9,33))
#'
#' # Setup starting states for each location
#' states <- lapply(locations, function(loc) {
#' ZEST.start(
#' domain=-5:45,
#' minStimulus=0,
#' maxStimulus=40,
#' makeStim=makeStimHelper(db,n,loc[1],loc[2]),
#' stopType="S", stopValue= 1.5, tt=loc[3], fpr=0.03, fnr=0.01)})
#'
#' # Loop through until all states are "stop"
#' while(!all(st <- unlist(lapply(states, ZEST.stop)))) {
#' i <- which(!st) # choose a random,
#' i <- i[runif(1, min=1, max=length(i))] # unstopped state
#' r <- ZEST.step(states[[i]]) # step it
#' states[[i]] <- r$state # update the states
#' }
#'
#' finals <- lapply(states, ZEST.final) # get final estimates of threshold
#' for(i in 1:length(locations)) {
#' #cat(sprintf("Location (%+2d,%+2d) ",locations[[i]][1], locations[[i]][2]))
#' #cat(sprintf("has threshold %4.2f\n", finals[[i]]))
#' }
#'
#' if (!is.null(opiClose()))
#' warning("opiClose() failed")
#' @export
ZEST <- function(domain = 0:40, prior = rep(1 / length(domain),length(domain)),
likelihood = sapply(domain, function(tt) 0.03 + (1-0.03-0.03)*(1-stats::pnorm(domain, tt, 1))),
stopType="S",
stopValue = 1.5,
minStimulus = utils::head(domain, 1),
maxStimulus = utils::tail(domain, 1),
maxSeenLimit = 2,
minNotSeenLimit = 2,
maxPresentations = 100,
minInterStimInterval = NA,
maxInterStimInterval = NA,
verbose = 0, makeStim,
stimChoice="mean",
...) {
state <- ZEST.start(domain, prior, likelihood, stopType, stopValue,
minStimulus, maxStimulus,
maxSeenLimit,minNotSeenLimit,maxPresentations,
makeStim,stimChoice, ...)
pdfs <- NULL
while(!ZEST.stop(state)) {
r <- ZEST.step(state)
state <- r$state
if (verbose == 2) {
cat(sprintf("Presentation %2d: ", state$numPresentations))
cat(sprintf("stim= %5s response=%s ", utils::tail(state$stimuli,1), utils::tail(state$responses,1)))
cat(sprintf("fixation= %1.0g ", utils::tail(state$fixated,1)))
cat(sprintf("stdev= %8.4g H= %8.4g\n", ZEST.stdev(state), ZEST.entropy(state)))
}
if (verbose > 0)
pdfs <- c(pdfs, list(state$pdf))
if (!is.na(minInterStimInterval) && !is.na(maxInterStimInterval))
Sys.sleep(stats::runif(1, min = minInterStimInterval, max = maxInterStimInterval)/1000)
}
return(list(
npres = utils::tail(state$numPresentations,1), # number of presentations
respSeq = mapply(c, state$stimuli, state$responses, state$fixated), # response sequence (list of triples)
pdfs = pdfs, # list of pdfs used (if verbose > 0)
final = ZEST.final(state), # final threshold estimate
opiResp = state$opiResp # list of all responses from opiPresent
))
}#ZEST
#' @rdname ZEST
#' @export
ZEST.start <- function(domain = 0:40, prior = rep(1 / length(domain),length(domain)),
likelihood = sapply(domain, function(tt) 0.03 + (1-0.03-0.03)*(1-stats::pnorm(domain, tt, 1))),
stopType = "S",
stopValue = 1.5,
minStimulus = utils::head(domain, 1),
maxStimulus = utils::tail(domain, 1),
maxSeenLimit = 2,
minNotSeenLimit = 2,
maxPresentations = 100,
makeStim,
stimChoice = "mean",
...) {
##########################
# Validate params
##########################
if (!is.element(stopType, c("S", "H", "N")))
stop("ZEST.start: stopType must be one of 'S', 'N', or 'H'")
if (nrow(likelihood) != length(domain))
stop(paste("ZEST.start: not enough rows in likelihood. Expect", length(domain)))
if (ncol(likelihood) != length(domain))
stop(paste("ZEST.start: not enough cols in likelihood. Expect", length(domain)))
if (!is.element(minStimulus, domain))
stop(paste("ZEST.start: you specified minStimulus =", minStimulus, "but it is not in 'domain'."))
if (!is.element(maxStimulus, domain))
stop(paste("ZEST.start: you specified maxStimulus =", maxStimulus, "but it is not in 'domain'."))
pdf <- prior/sum(prior)
return(list(name = "ZEST",
domain = domain,
pdf = pdf,
likelihood = likelihood,
stopType = stopType,
stopValue = stopValue,
minStimulus = minStimulus,
maxStimulus = maxStimulus,
maxSeenLimit = maxSeenLimit,
minNotSeenLimit = minNotSeenLimit,
maxPresentations = maxPresentations,
makeStim = makeStim,
stimChoice = stimChoice,
currSeenLimit = 0, # number of times maxStimulus seen
currNotSeenLimit = 0, # number of times minStimulus not seen
numPresentations = 0, # number of presentations so far
stimuli = NULL, # vector of stims shown
responses = NULL, # vector of responses (1 seen, 0 not)
responseTimes = NULL, # vector of response times
fixated = NULL, # vector of true/false for fixated one per stimuli
opiResp = NULL, # list of opiPresent return values
opiParams = list(...) # the extra params
))
}# ZEST.start
#' @rdname ZEST
#' @param state Current state of the ZEST returned by \code{ZEST.start} and \code{ZEST.step}.
#' @param nextStim A valid object for \code{opiPresent} to use as its \code{nextStim}.
#' @param fixedStimValue A number in \code{state$domain} that, is \code{!is.na}, will be used as the stimulus value
#' overriding \code{state$minStimulus}, \code{state$maxStimulus} and \code{state$stimChoice}.
#' @export
ZEST.step <- function(state, nextStim = NULL, fixedStimValue = NA) {
if (!is.na(fixedStimValue)) {
if (fixedStimValue %in% state$domain) {
stim <- fixedStimValue
} else {
stop(paste("ZEST.step: fixedStimValue = ", fixedStimValue,"is not in 'domain'."))
}
} else {
if (state$stimChoice == "mean") {
stimIndex <- which.min(abs(state$domain - sum(state$pdf * state$domain)))
} else if (state$stimChoice == "mode") {
stimIndex <- which.max(state$pdf)
} else if (state$stimChoice == "median") {
stimIndex <- which.min(abs(cumsum(state$pdf) - 0.5))
} else {
stop(paste("ZEST.step: stimChoice = ",state$stimChoice," not implemented."))
}
stim <- state$domain[stimIndex]
if (stim < state$minStimulus)
stim <- state$minStimulus
if (stim > state$maxStimulus)
stim <- state$maxStimulus
}
stimIndex <- which(stim == state$domain) # in case it changed via fixed, min or max
params <- c(list(stim = state$makeStim(stim, state$numPresentations), nextStim = nextStim), state$opiParams)
opiResp <- do.call(opiPresent, params)
if (!is.null(opiResp$err))
return(list(state = state, resp = opiResp))
fixation_is_good <- TRUE
if (!is.null(params$stim$checkFixationOK)) {
fixation_is_good <- params$stim$checkFixationOK(opiResp)
}
state$stimuli <- c(state$stimuli, stim)
state$responses <- c(state$responses, opiResp$seen)
state$responseTimes <- c(state$responseTimes, opiResp$time)
state$numPresentations <- state$numPresentations + 1
state$fixated <- c(state$fixated, fixation_is_good)
state$opiResp <- c(state$opiResp, list(opiResp))
if (fixation_is_good) { # update the pdf
if(opiResp$seen) {
if (stim == state$maxStimulus) state$currSeenLimit <- state$currSeenLimit + 1
state$pdf <- state$pdf * state$likelihood[stimIndex, ]
} else {
if (stim == state$minStimulus) state$currNotSeenLimit <- state$currNotSeenLimit + 1
state$pdf <- state$pdf * (1 - state$likelihood[stimIndex, ])
}
state$pdf <- state$pdf/sum(state$pdf)
} else {
warning("ZEST.step: fixation lost during presentation, pdf not updated")
}
return(list(state = state, resp = opiResp))
}#ZEST.step()
#' @rdname ZEST
#' @export
ZEST.stop <- function(state) {
keepGoing <- (
(state$numPresentations < state$maxPresentations) &&
(state$currNotSeenLimit < state$minNotSeenLimit) &&
(state$currSeenLimit < state$maxSeenLimit) &&
(
((state$stopType == "S") && (ZEST.stdev(state) > state$stopValue))
|| ((state$stopType == "H") && (ZEST.entropy(state) > state$stopValue))
|| ((state$stopType == "N") && (state$numPresentations < state$stopValue))
)
)
return(!keepGoing)
}#ZEST.stop
#' @rdname ZEST
#' @export
ZEST.final <- function(state) {
if (state$stimChoice == "mean") {
final <- sum(state$pdf*state$domain)
} else if (state$stimChoice == "mode") {
final <- state$domain[which.max(state$pdf)]
} else if (state$stimChoice == "median") {
final <- state$domain[which.min(abs(cumsum(state$pdf) - 0.5))]
}
return(final)
}#ZEST.final
############################################################
# Tests
############################################################
#require(OPI)
#chooseOpi("SimHenson")
##chooseOpi("SimYes")
#opiInitialize("C",6)
#
#makeStim <- function(db, n) {
# s <- list(x=9, y=9, level=dbTocd(db,10000/pi), size=0.43,
# color="white",
# duration=200, responseWindow=1500,
# checkFixationOK=NULL)
# class(s) <- "opiStaticStimulus"
#
# return(s)
# }
#makeNextStim <- function(x,y) {
# s <- list(x=9, y=9, level=dbTocd(db,10000/pi), size=0.43, color="white",
# duration=200, responseWindow=1500, checkFixationOK=NULL)
# class(s) <- "opiStaticStimulus"
#
# return(s)
# }
#
#state <- ZEST.start(domain=-5:45, maxStimulus=40, minStimulus=0, makeStim=makeStim, stopType="S", stopValue= 1.5, tt=0, fpr=0.30)
#while(!ZEST.stop(state)) {
# r <- ZEST.step(state)
# cat(sprintf("%2d %s\n",tail(r$state$stimuli,1), r$resp$seen))
# state <- r$state
#}
#print(ZEST.final(state))
#
#print("########################################################################")
#
#makeStimHelper <- function(db,n, x, y) {
# ff <- function(db, n) db+n
#
# body(ff) <- substitute(
# {s <- list(x=x, y=y, level=dbTocd(db,10000/pi), size=0.43, color="white",
# duration=200, responseWindow=1500, checkFixationOK=NULL)
# class(s) <- "opiStaticStimulus"
# return(s)
# }
# , list(x=x,y=y))
# return(ff)
#}
#
# # list of (x, y, true threshold) triples
#locations <- list(c(9,9,30), c(-9,-9,32), c(9,-9,31), c(-9,9,33))
#
# # setup starting states for each location
#states <- lapply(locations, function(loc) {
# ZEST.start(domain=-5:45,
# makeStim=makeStimHelper(db,n,loc[1],loc[2]),
# maxStimulus=40, minStimulus=0,
# stopType="S", stopValue= 1.5, tt=loc[3], fpr=0.03, fn=0.01)
#})
#
# # loop through until all states are "stop"
#while(!all(st <- unlist(lapply(states, ZEST.stop)))) {
# i <- sample(which(!st), 1) # choose a random, unstopped state
# r <- ZEST.step(states[[i]]) # step it
# states[[i]] <- r$state # update the states
#}
#
#finals <- lapply(states, ZEST.final) # get final estimates of threshold
#for(i in 1:length(locations))
# cat(sprintf("Location (%+2d,%+2d) has threshold %4.2f\n",locations[[i]][1], locations[[i]][2], finals[[i]]))
#
#print("########################################################################")
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="H", stopValue= 3, verbose=0, tt=20, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="S", stopValue=1.5, verbose=0, tt=20, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="S", stopValue=2.0, verbose=0, tt=20, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stopType="N", stopValue= 50, verbose=0, tt=20, fpr=0.03))
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="mean", tt=20, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="mode", tt=20, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="median", tt=20, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, stimChoice="mean", tt=20, fpr=0.03))
#
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["npres",])))
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=0,s=5), tt=30, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=10,s=5), tt=30, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=20,s=5), tt=30, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03))
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["npres",])))
#
#repp <- function(...) sapply(1:100, function(i) ZEST(makeStim=makeStim, ...))
#a <- repp(stopType="H", stopValue= 3, verbose=0, tt=30, fpr=0.03)
#b <- repp(stopType="S", stopValue=1.5, verbose=0, tt=30, fpr=0.03)
#c <- repp(stopType="S", stopValue=2.0, verbose=0, tt=30, fpr=0.03)
#d <- repp(stopType="N", stopValue= 50, verbose=0, tt=30, fpr=0.03)
#e <- repp(prior=dnorm(0:40,m=0,s=5), tt=30, fpr=0.03)
#f <- repp(prior=dnorm(0:40,m=10,s=5), tt=30, fpr=0.03)
#g <- repp(prior=dnorm(0:40,m=20,s=5), tt=30, fpr=0.03)
#h <- repp(prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03)
#
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d,e,f,g,h), function(x) unlist(x["npres",])))
#
#a <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=00, fpr=0.03))
#b <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=10, fpr=0.03))
#c <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=20, fpr=0.03))
#d <- sapply(1:100, function(i) ZEST(makeStim=makeStim, prior=dnorm(0:40,m=30,s=5), tt=30, fpr=0.03))
#layout(matrix(1:2,1,2))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["final",])))
#boxplot(lapply(list(a,b,c,d), function(x) unlist(x["npres",])))
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