modelfree (version 1.0-2)

locglmfit_sparse_private: Local generalized linear fitting with sparse matrices

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)