v2.14.3 released 20 April 2016

2.14.3

NEW

• Fuzzy classification of genetic groups to construct A^-1.
  • Allows individuals' phantom parents to be assigned to genetic groups with a probability. Meaning, they can be assigned to more than one genetic group.
  • To implement, the pedigree must have phantom parent identities as unique rows and a matrix of probabilities of group membership for every phantom parent in every genetic group has to be supplied to the fuzz argument.
  • Examples can be seen in the makeAinv.Rd help file or by running the following commands in R:

?makeAinv              # launches the help documentation
example(makeAinv)      # runs the examples in the help documentation

* Notably, fuzzy classification can be set to 'null', where each phantom parent is assigned to one genetic group with probability=1. This produces the same **Astar** matrix as regular genetic group methods (without fuzzy classification). See this demonstrated in the examples of the help documentation.

• Add the makeAstarMult() function to create the inverse numerator relationship matrix with genetic groups (and possibly also fuzzy classification of genetic groups) through matrix multiplication instead of using direct algorithms to set this up.
  • Uses ggcontrib() and makeAinv() to create Q and A^-1 directly, then multiplies these in such a way as to obtain Astar.
  • Examples using the two different types of pedigree formats and either with or without fuzzy classication can be seen in the makeAstarMult.Rd help file or run them in R with the command:

?makeAstarMult      # launches the help documentation
example(makeAstarMult)  # runs the examples in the help documentation

• Add the F2009 dataset
  • This dataset can be used as an example for fuzzy classification of genetic groups when constructing a numerator relationship matrix with groups (i.e., with makeAinv())
  • See a description in F2009.Rd or in R type:

?F2009

• Add the simGG() function to simulate pedigree and phenotype when immigration occurs in a focal population
  • Allows fairly fine control over a simulation. For example, the function is flexible in the: population size, number of immigrants per generation, number of generations, and both spatial and temporal trends in both focal and immigrant populations.
  • This is the function used to simulate the new ggTutorial dataset (below)
• Added the ggTutorial dataset
  • This is a simulated dataset to be used in analyses with genetic group animal model methods.
  • See a description in ggTutorial.Rd or in R type:

?ggTutorial

• LRTest() is now an exported function to do log-likelihood ratio tests

Small changes

• new S3 generic and methods for makeAinv().
  • method dispatch is based on class of the fuzz argument
    • if fuzz == NULL then dispatch the method makeAinv.default()
    • if fuzz == "matrix" | fuzz == "Matrix" then dispatch makeAinv.fuzzy()
• fix issue with proLik() and the confidence interval estimation
  • use LRTest() as basis of constrainFun() within proLik() so consistently define log-likelihood ratio test statistics
  • close issue #4 with commit 978ad610198398848d97e90c4eb57f4834a4c278