mixedlearningcurve

mixedlearningcurve(N, Z, background_prob, rhog, alphag, betag, sig2eg, sig2vg)

MIXEDLEARNINGCURVE EM learning-curve estimation for mixed binary/continuous data

Usage:

[alph, beta, gamma, rho, sig2e, sig2v, xnew, signewsq, muone, a] = …

mixedlearningcurve(N, Z, background_prob, rhog, alphag, betag, …

sig2eg, sig2vg)

Inputs:

N : 2xK double - row1: number correct per trial; row2: max possible – required Z : 1xK double - continuous observation (e.g. reaction time) – required background_prob : double - chance-level probability of correct response – required rhog : double - initial guess for state AR(1) coefficient rho – required alphag : double - initial guess for RT intercept alpha – required betag : double - initial guess for RT slope beta – required sig2eg : double - initial guess for RT noise variance sig2e – required sig2vg : double - initial guess for state variance sig2v – required

Outputs:

alph : double - EM estimate of RT intercept beta : double - EM estimate of RT slope gamma : double - binary observation state weight (fixed to 0 here) rho : double - EM estimate of state AR(1) coefficient sig2e : double - EM estimate of RT noise variance sig2v : double - EM estimate of state variance xnew : 1xK+1 double - smoothed state estimate x{k|K} signewsq : 1xK+1 double - smoothed state variance SIG^2{k|K} muone : double - logit of background probability a : 1xK+1 double - smoother gain A{k}

Notes

Iterates forward filter (recfilter), backward smoother (backest), and M-step (m_step) for up to 3000 iterations with convergence criterion 1e-6 on the mean absolute change in [alpha, beta, sig2e, sig2v]. Originally authored by Anne Smith (Oct 15, 2003); updated by Anne Smith (Nov 29, 2010) and Michael Prerau.

See also: mixedlearningcurve2, recfilter, backest, m_step

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