locglmfit_sparse_private: Local generalized linear fitting with sparse matrices

In modelfree: Model-free estimation of a psychometric function

Description

THIS IS AN INTERNAL FUNCTION: USE LOCGLMFIT FOR BEST RESULTS. Fisher scoring method for local polynomial estimator of a psychometric function (PF).

Usage

locglmfit_sparse_private( xfit, r, m, x, h, returnH, link, guessing, lapsing, K, p, ker, maxiter, tol )

Arguments

xfit	points in which to calculate the estimate
r	number of successes in points x
m	number of trials in points x
x	stimulus values
h	bandwidths
returnH	Boolean; Return or not the hat matrix H? default is TRUE
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	degree of the polynomial; default p = 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 or 3 components: pfit: value of the local polynomial estimate at points xfit etafit: estimate of eta (link of pfit) H: hat matrix (OPTIONAL)

Examples

data( "01_Miranda" )
xnew = 1.2 * (0:99)/99+0.1
h <- 0.2959
fit <- locglmfit_sparse_private( xnew,  example01$r,  example01$m, example01$x, h, FALSE, "logit_link", 0, 0, 2, 1, "dnorm", 50, 1e-6)

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