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SupplementaryCode_HoopStats.zip (19.16 kB)
Dissociating memory accessibility and precision in forgetting [ Registered Report Stage 1 - Code]
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posted on 2019-06-05, 11:07 authored by Sam Berens, Blake A. Richards, Aidan HornerAidan HornerSupplementary Matlab Code for the Registered Report Stage 1 Protocol "Dissociating memory accessibility and precision in forgetting"
1) HoopStats_CalcParams.m
MATLAB function: Computes various statistics for a sample of angles in radians. If optional Weights
argument is supplied, angles are weighted to have varying influences on the statistics.
2) HoopStats_EstimMixtureModel.m
MATLAB function: Estimates a mixture model for circularly distributed data. Components within the
model can include a uniform distribution and arbitrarily many target/fixed position von Mises
distributions. This function calls both “HoopStats_RunEM.m” and “HoopStats_HardCluster.m” in an
attempt to find the best fitting mixture model that is more parsimonious than a reduced model with
a single uniform distribution.
3) HoopStats_HardCluster.m
MATLAB function: Estimates a mixture model for circularly distributed accuracy data using a hard
clustering method.
4) HoopStats_InfoContent.m
MATLAB function: Computes the information content of a von Mises distribution with a prior weight
of p and a concentration parameter of k.
5) HoopStats_K2H.m
MATLAB function: Computes the information entropy (H) of a von Mises distribution with a
concentration parameter of k. If k is 0, H is maximal and reflects the information entropy of a
uniform distribution.
6) HoopStats_K2R.m
MATLAB function: Converts the von Mises concentration parameter (k) to a resultant (mean) vector
length r.
7) HoopStats_KCorrection.m
MATLAB function: Implements the Best & Fisher (1981) correction to reduce bias in estimates of k
when they based on fewer than 15 data points.
8) HoopStats_R2K.m
MATLAB function: Converts a resultant (mean) vector length (r) into a von Mises concentration
parameter (k).
9) HoopStats_RunEM.m
MATLAB function: Estimates a circular mixture model using Expectation Maximization (EM).
10) HoopStats_VonmFit.m
MATLAB function: Compute values of the von Mises probability density function (Pd) and the
associated negative log-likelihood (Nll) for angles (in radians) drawn from a distribution with mean
Mu and concentration k.
