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/* This is an example HY-PHY Batch File.
It reads in two NEXUS nucleotide dataset data/aspectrin1.nuc
and data/aspectrin2.nuc and performs
the relative ratio test on the 4-taxa tree, using F81 model.
Output is printed out as a Newick Style tree with branch lengths
representing the number of expected substitutions per branch (which
is the default setting for nucleotide models w/o rate variation).
Also, the likelihood ratio statistic is evaluated and the P-value
for the test is reported.
Sergei L. Kosakovsky Pond and Spencer V. Muse
December 1999.
*/
/* 1. Read in the data and store the result in DataSet variables.*/
DataSet nucleotideSequence1 = ReadDataFile ("data/aspectrin1.nuc");
DataSet nucleotideSequence2 = ReadDataFile ("data/aspectrin2.nuc");
/* 2. Filter the data, specifying that all of the data is to be used
and that it is to be treated as nucleotides. */
DataSetFilter filteredData1 = CreateFilter (nucleotideSequence1,1);
DataSetFilter filteredData2 = CreateFilter (nucleotideSequence2,1);
/* 3. Collect observed nucleotide frequencies from the filtered data. observedFreqs will
store the vector of frequencies. */
HarvestFrequencies (observedFreqs1, filteredData1, 1, 1, 1);
HarvestFrequencies (observedFreqs2, filteredData2, 1, 1, 1);
/* 4. Define the F81 substitution matrix. '*' is defined to be -(sum of off-diag row elements) */
F81RateMatrix =
{{*,mu,mu,mu}
{mu,*,mu,mu}
{mu,mu,*,mu}
{mu,mu,mu,*}};
/*5. Define the F81 models, by combining the substitution matrix with the vector of observed
(equilibrium) frequencies. We define one for each block, since the equilibrium
frequencies will differ. */
Model F81Block1 = (F81RateMatrix, observedFreqs1);
Model F81Block2 = (F81RateMatrix, observedFreqs2);
/*6. Now we can define 2 4-taxa trees - one for each data block. We use appropriate models for each one.
Note the use of TRY_NUMERIC_SEQUENCE_MATCH constant to instruct HyPhy to map sequence numbers to
tree leaves (by default, HyPhy expects sequence names and leaf names to match).*/
TRY_NUMERIC_SEQUENCE_MATCH = 1;
UseModel (F81Block1);
Tree fourTaxaTree1 = ((1,2),3,4);
UseModel (F81Block2);
Tree fourTaxaTree2 = ((1,2),3,4);
/*7. Since all the likelihood function ingredients (data, tree, equilibrium frequencies)
have been defined we are ready to construct the likelihood function. We
combine both datasets into one likelihood function. */
LikelihoodFunction theLnLik = (filteredData1, fourTaxaTree1, filteredData2, fourTaxaTree2);
/*8. Maximize the likelihood function, storing parameter values in the matrix paramValues.
We also store the resulting ln-lik and the number of model parameters. */
Optimize (paramValues, theLnLik);
unconstrainedLnLik = paramValues[1][0];
paramCount = paramValues[1][1];
/*9. Print the tree with optimal branch lengths to the console. */
fprintf (stdout, "\n0). UNCONSTRAINED MODEL:", theLnLik);
/*10. We now constrain the rate of evolution along each branch to be proportional in both trees.
R will represent the ratio. We use ReplicateConstraint to automatically
attach the same constraint to all branches of the tree. */
global R;
ReplicateConstraint ("this1.?.mu:=R*this2.?.mu", fourTaxaTree2, fourTaxaTree1);
Optimize (paramValues, theLnLik);
/*11. Now we compute the ln-lik ratio statistic and the P-Value, using the Chi^2 dist'n
with an appropriate degree of freedom. */
lnlikDelta = 2 (unconstrainedLnLik-paramValues[1][0]);
pValue = 1-CChi2 (lnlikDelta, paramCount - paramValues[1][1]);
fprintf (stdout, "\n\n1). Relative ratio constraint; the P-value is:", pValue, "\n", theLnLik);
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