bootstrap_ci_sl: Bootstrap estimate of confidence interval for slope...

In modelfree: Model-free estimation of a psychometric function

Description

Finds bootstrap estimate of a confidence interval at a significant level alpha for the estimated slope for the local polynomial estimation of psychometric function (PF) with guessing and lapsing rates specified in lims. Confidence interval is based on bootstrap percentiles

Usage

bootstrap_ci_sl( TH, r, m, x, N, h0, alpha = 0.05, X = (max(x)-min(x))*(0:999)/999+min(x), link = c( "logit" ), guessing = 0, lapsing = 0, K = 2, p = 1, ker = c( "dnorm" ), maxiter = 50, tol = 1e-6 )

Arguments

TH	required threshold level
r	number of successes in points x
m	number of trials in points x
x	stimulus levels
N	number of bootstrap replications
h0	pilot bandwidth; if not specified, then the scaled plug-in bandwidth is used
alpha	significance level of the confidence interval
X	set of value for which to calculate the estimates of PF for the thresholdestimation; if not given 1000 equally spaced points from min to max of xdes are used
link	name of the link function to be used; 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	order of the polynomial; default is 1
ker	kernel function for weights; default "dnorm"
maxiter	maximum number of iterations in Fisher scoring; default is 50
tol	tolerance level at which to stop Fisher scoring; default is 1e-6

Value

value

Object with 2 components: ci: confidence interval based on bootstrap percentiles sl0: slope estimate

Examples

data( "01_Miranda" )
bwd <- 0.2959
value <- bootstrap_ci_sl( 0.5, example01$r, example01$m, example01$x, 10, bwd )

modelfree documentation built on May 2, 2019, 6:07 p.m.