bootstrap_sd_sl: Bootstrap estimate the standard deviation of slope estimation

Description Usage Arguments Value Examples

View source: R/bootstrap_sd_sl.R

Description

Finds bootstrap estimate of the standard deviation of the estimated slope for the local polynomial estimation of psychometric function (PF) with guessing and lapsing rates as specified

Usage

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bootstrap_sd_sl( TH, r, m, x, N, h0, 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

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: sd: bootstrap estimate of the standard deviation of the slope estimate sl0: slope estimate

Examples

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

modelfree documentation built on May 31, 2017, 4:28 a.m.