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/* This is an example HY-PHY Batch File.
In this file, we will illustrate how to define category
variables sampled from continuous distribution to model rate heterogeneity
and spatially correlated via a hidden markov chain
Sergei L. Kosakovsky Pond and Spencer V. Muse
June 2001.
*/
/* 1.
Refer to definition1.bf and definition2.bf for
examples on how to define a distribution of rates.
To introduce space correlation via a hidden markov model
we need to specify an additional parameter in the call
to "category": the transition matrix for the HMM.
The following example defines a simple HMM model
following the paper of Felsenstein and Churchill (1996 J Mol Evol).
*/
#include "displayFunction.bf";
global lambda = .5;
lambda :< 1;
hiddenMarkovM = {10,10};
hiddenMarkovF = {10,1};
for (k=0; k<10; k=k+1)
{
hiddenMarkovM [k][k] := lambda+(1-lambda)/10;
hiddenMarkovF [k][1] := 1/10;
for (l=k+1;l<10;l=l+1)
{
hiddenMarkovM [k][l] := (1-lambda)/10;
hiddenMarkovM [l][k] := (1-lambda)/10;
}
}
Model HMM = (hiddenMarkovM,hiddenMarkovF,false);
global alpha = 1;
alpha:>0.01;alpha:<100;
category catVar = (10, /* number of rates */
hiddenMarkovF, /* probs. of rates */
MEAN, /* sampling method */
GammaDist(_x_,alpha,alpha), /* density */
CGammaDist(_x_,alpha,alpha), /*CDF*/
0, /*left bound*/
1e25, /*right bound*/
CGammaDist(_x_,alpha+1,alpha),
/* "antiderivative" of x f(x) */
HMM /* transition matrix for the hidden Markov model */
);
GetInformation (catInfo,catVar);
catInfo = echoCatVar (catInfo);
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