Model-free estimation of a psychometric function

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[sd,th0] = bootstrap_sd_th(TH,r,m,x,N,h0,X,link,guessing,lapsing,K,p,ker,maxiter,tol)

Bootstrap estimate of the standard deviation of the estimated threshold for the local polynomial estimate of the psychometric function with guessing and lapsing rates.

Input:

TH: required threshold level

r: number of successes at points x

m: number of trials at points x

x: stimulus levels

N: number of bootstrap replications; N should be at least 200 for reliable results

h0: bandwidth

Optional input:

X: set of values at which estimates of the psychometric function for the threshold estimation are to be obtained; if not given, 1000 equally spaced points from minimum to maximum of x are used

link: name of the link function; default is 'logit'

guessing: guessing rate; default is 0

lapsing: lapsing rate; default is 0

K: power parameter for Weibull and reverse Weibull link; default is 2

p: degree of the polynomial; default is 1

ker: kernel function for weights; default is 'normpdf'

maxiter: maximum number of iterations in Fisher scoring; default is 50

tol: tolerance level at which to stop Fisher scoring; default is 1e-6

Output:

sd: bootstrap estimate of the standard deviation of the threshold estimator

th0: threshold estimate

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