Hidden Markov Models for Molecular Motors: [sigma nu]=EstSigmaAndNu(Y,I0) - File Exchange

Code covered by the BSD License

Highlights from
Hidden Markov Models for Molecular Motors

DisplayModel(M, L, iterations... Display the HMM described by M, the log likelihood value,
ForBackF(M,Y) Forward-backward algorithm for step-HMM
ForwardBackward_3(M,Y,I0,test... function [LL newM gamma] = ForwardBackward_3(M,Y,I0,test,BigB)
M=MakeMonotonicModel(nu, yQua... function M=MakeMonotonicModel(nu, yQuantum, noiseSigma, transProb, stepSizes, stepSigma)
ModelChange(M1,M0) Check the changes in the transition probabilities. Looks at the
PlotAndLabelPeaks(x,y,minsize... function PlotAndLabelPeaks(x,y,minsize,xscale)
RestorationPlot(Y, EstY, wrap... function RestorationPlot(Y, EstY, wrap, yquantum, EYbase,markerstr) Fancy
WtConvol(a,b,m)
[EstY EstI LP Wrap]=ViterbiRe... % Viterbi algorithm for step-HMM
[X,Y]=StepSimulator(M,N) function [X,Y]=StepSimulator(M,N)
[X,Y]=StepSimulatorC(M,N) Given the number of time points N and the model M, simulate a staircase
[sigma nu]=EstSigmaAndNu(Y,I0) Find reasonable values for these variables based on the step data vector
b=Makeb(nu,sigma,DutyCycle,Er...
ms=QuickShift(m,shift) Faster replacement for circshift for time-critical applications.
ms=QuickShiftMex(m,shift) Faster replacement for circshift for time-critical applications.
n=NextNiceNumber(x,f) function n=NextNiceNumber(x)
xs=Step125(x)
MotorHMM1.m
MotorHMM2.m
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from Hidden Markov Models for Molecular Motors by Fred Sigworth
A set of functions for analysing noisy recordings of the random stepping of molecular motors

[sigma nu]=EstSigmaAndNu(Y,I0)

function [sigma nu]=EstSigmaAndNu(Y,I0)
% Find reasonable values for these variables based on the step data vector
% Y.

if nargin<2
    I0=1;
end;
nt=numel(Y);
d=abs(diff(Y));
sigma=median(d./sqrt(I0));          % Works for either vector or scalar I0

dx1=max(d);
dx2=max(abs(Y(1:nt-2)-Y(3:nt)));

nu=2.4*max(dx1,dx2);  % 20% margin.

Highlights from Hidden Markov Models for Molecular Motors

Highlights from
Hidden Markov Models for Molecular Motors