pydigree.stats package¶

Subpackages¶

pydigree.stats.mixedmodel package

Submodules¶

pydigree.stats.mathfuncs module¶

Misc math functions

pydigree.stats.mathfuncs.grid(func, nargs, low, high, ntests=10, predicate=None)¶

Evaluates a function over a range of argument values.

This can be time consuming, especially if the function to be evaluated is particularly intensive: for m tests over n arguments, the function will be evaluated m**n times

Parameters:	func (callable) – The function to be grid searched low – The lowest value to test high – The highest value to test ntests – Number of argument values to test between low and high predicate (callable) – a function that returns True if the configuration of arguments should be evaluated.

pydigree.stats.mathfuncs.is_positive_definite(mat)¶

Evaluates if a matrix is positive definite (all eigvals > 0)

Parameters:	mat (matrix) – Matrix to test
Returns:	positive-definiteness
Return type:	bool

pydigree.stats.stattests module¶

Methods for statistical testing

pydigree.stats.stattests.LikelihoodRatioTest(null_model, alt_model)¶

Compares two nested models by likelihood ratio test

Returns:	Result of test
Return type:	LikelhoodRatioTestResult

class pydigree.stats.stattests.LikelihoodRatioTestResult(statistic, df, distribution, n)¶

Bases: object

The result of a likelihood ratio test.

Variables:	statistic – test statistic df – degrees of freedom distribution – distribution of test statistic n – sample size

lod¶

LOD score (log10 LR) of the result

Rtype float:

pvalue¶

P-value for the test

Return type:	float

pydigree.stats.variancecomponents module¶

class pydigree.stats.variancecomponents.VarianceComponentsLinkage(pedigrees, outcome=None, fixed_effects=None, ibd_matrix=None, null_model=None, joint=False, verbose=False, maximization='Average Information')¶