makeDiiF()
can now be given coefficients of inbreeding. Facilitates either:f
in a new generation of the pedigree if f
has already been calculated for previous generationsproLik()
(and proLik4()
) REMOVED from the package.
asreml
R package (which requires purchasing an expensive license) and due to the unstable behavior of asreml
discovered when revising proLik()
it was decided that nadiv
should no longer support this other package in this way.gremlin
R package for a replacement to asreml
.makeA()
was affected by a bug in Matrix
<1.6-0
Matrix::chol2inv()
bug highlighted a messy order of operations in nadiv
tcrossprod()
and
solve()
which are just what chol2inv()
usesnadiv
and take 1 step away from future bugs like thischol2inv()
and tcrossprod(solve())
allocated same amount of memory according to profmem
packagebug in makeAinv()
, makeGGAinv()
, and makeDiiF()
caused coefficients of inbreeding (f
) and Mendelian sampling variances (dii
) to be ordered incorrectly
dii
to be incorrect - namely the A matrix from makeA()
and so consequently anything built from A (i.e., dominance and epistatic relatedness matrices through makeD()
etc.)nadiv
version 2.17.2 caused CRAN to archive due to error induced by Matrix
updatesMatrix
author) helpfully provided excellent explanations and patchesnadiv
code caused by new methods for all.equal()
and rbind2()
is.numPed()
and is.proLik()
inherits()
pin()
does not work with asreml 4 (should still work with asreml 3 model objects)nadiv
will not support this in the future as asreml 4 has vpredict()
proLik4()
, essentially the same as proLik()
, but works on asreml
v4
proLik()
is kept to retain compatibility with asreml v3 model objectsmakeM()
creates mutational effects relatedness matrix M directly
brms
/JAGS
etc. that require relatedness matrices (not their inverse) in mixed modelsfixed deprecated use of Matrix
non-virtual subclasses
Matrix
1.4-2 and specifically >=1.5-0new c++ routine to calculate coefficients of inbreeding and D of Cholesky decomposed A matrix
prepPed()
as often encountered/reported from use in optiSel
makeMinv()
creates the inverse of the (additive) mutational effects relatedness matrix.makeT()
creates the lower triangle of the cholesky factor of the additive genetic numerator relatedness matrix.sparseMatrix()
instead of Matrix()
ggcontrib()
to internally use makeT()
to directly create a subset of the T matrix (which the subset is Q for the special setup for genetic groups).geneDrop()
conducts a gene dropping simulation down a user-supplied pedigree.simGG()
now simulates phenotypes with heterogeneous additive genetic variances among the genetic groups (i.e., immigrant and resident groups)makeGGAinv()
added as a new function to construct genetic group-specific inverse relatedness matrices (Ainv).?makeGGAinv
), but below is a basic example:ggPed <- Q1988[-c(3:7), c("id", "damGG", "sireGG")]
AinvOut <- makeGGAinv(ggPed, ggroups = 2)$Ainv #<-- list with 2 Ainv matrices
makeTinv()
and makeDiiF()
functionsf
makeGGAinv()
.makeDiiF()
creates the D matrix of the Cholesky factorization of the relatedness matrix below (i.e., A and the coefficients of inbreeding (diagonals-1 of A)makeTinv()
creates Tinv of the Cholesky factorization of the inverse relatedness matrix below (i.e., Ainv)1 / d_ii
Dinv <- D
followed by Dinv@x <- 1 / D@x
makeTinv(Mrode2)
makeDiiF(Mrode2)
simPedDFC()
to allow more flexibility in designing pedigreesroxygen2
documentationmakeAinv()
and makeS()
makeSd()
and makeSdsim()
makeD()
and makeDsim()
accomplish for autosomesproLik()
improved/bug fixed to find confidence limitsoptimize()
quitting too early with default tol
argumentNA
if confidence limits are not, in fact, found (e.g., for boundary parameters, variances that are not significantly greater than zero)plot.proLik()
now includes vertical lines to better visualize CIsmakeAinv()
and makeD()
genAssign()
and prunePed()
genAssign.numPed()
code (and to some extent prunePed.numped()
)genAssign()
and prunePed()
are frequently called in many nadiv functions which operate on class 'numPed', this will have modest, but significant performance increasesprofvis
for bringing my attention to this!fuzz
argument.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.
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.ggcontrib()
and makeAinv()
to create Q and A^-1 directly, then multiplies these in such a way as to obtain Astar.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
F2009
datasetmakeAinv()
)F2009.Rd
or in R type:?F2009
Add the simGG()
function to simulate pedigree and phenotype when immigration occurs in a focal population
ggTutorial
dataset (below)Added the ggTutorial
dataset
ggTutorial.Rd
or in R type:?ggTutorial
LRTest()
is now an exported function to do log-likelihood ratio testsnew S3 generic and methods for makeAinv()
.
fuzz
argumentfuzz == NULL
then dispatch the method makeAinv.default()
fuzz == "matrix" | fuzz == "Matrix"
then dispatch makeAinv.fuzzy()
fix issue with proLik()
and the confidence interval estimation
LRTest()
as basis of constrainFun()
within proLik()
so consistently define log-likelihood ratio test statisticsggcontrib()
can now incorporate fuzzy classification of genetic groupsggcontrib()
have been changed. For more information and examples, read the help documentation ggcontrib.Rd
or in R
type:?ggcontrib # launches the help documentation
example(ggcontrib) # runs the examples in the help documentation
makeAinv()
makeAinv()
now can construct the augmented A-inverse matrix for genetic groupsmakeAinv()
, however, the defaults are set to produce the normal A-inverse. For more information and examples, read the help documentation makeAinv.Rd
or in R type:?makeAinv # launches the help documentation
example("makeAinv") # runs the examples in the help documentation
makeAinv()
- significant speed-up!numPed
for pedigrees constructed by numPed()
.is.numPed()
) and re-ordering rows (ronPed()
) currently availablenumPed
, use ronPed()
instead of typical subsetting operators (e.g., '['
) to retain the class attribute numPed
. For example:nPed <- numPed(Mrode2)
is.numPed(nPed) # TRUE
# re-order using typical R functions
nPed_sub <- nPed[order(nPed[, 2], nPed[, 3]), ]
is.numPed(nPed_sub) # FALSE; see help via ?'[' about dropping attributes
class(nPed_sub) # matrix
nPed_subnadiv <- ronPed(nPed, order(nPed[, 2], nPed[, 3]))
is.numPed(nPed_subnadiv) # TRUE
class(nPed_subnadiv) # numPed
warcolak
dataset.sex
are now "M"
and "F"
instead of 0
& 1
.warcolak.Rd
. Q1988
from Quaas 1988 and (2) Mrode3
from Mrode (2005) chapter 3. See their descriptions in Q1988.Rd
and Mrode3.Rd
or in R type:?Q1988
?Mrode3
heterogametic
argument in makeS()
.makeS(FG90)
won't work! Must now type:
makeS(FG90, heterogametic = "0")
as a minimum
TDtT()
, a function to take the TDT' Cholesky decomposition of a matrix (not currently exported).founderLine()
which traces all individuals back to either the paternal or maternal foundergrfx()
now has a new argument to allow user to supply the standard normal deviates instead of generating them within the function.incidence = NULL
...
argument to drfx()
so that arguments for the internal use of grfx()
can be supplied to drfx()
.simPedHS()
or simPedDFC()
.ggcontrib()
, default is "matrix"pcc()
return FALSE
if the object (asreml) shows the log-likelihood did not convergesilent = FALSE
argument to pcc()
so that the default can be changed to not show messagesproLik()
so that profile likelihoods should be "valleys" (instead of "hills", as they were in versions previous to 2.13.3)makeAinv()
makeA()
to use inverse of cholesky factorization of the A-inverse matrixbase::chol2inv()
to obtain AprepPed()
to prepare pedigrees for use in other functionsmakeAinv()
ggcontrib()
so that genetic group contributions can be calculatedpin()
and pcc()
functions for the delta method and parameter value convergence checking, respectively, for asreml type REML models. Also added the pin.Rd and pcc.Rd help/documentation files.pin()
is not exported in this version (need nadiv:::pin()
to use it)makeDufam()
, but did not export it.makeDomEpi()
argument "Dinverse" to "invertD" to be similar to makeD()pcc()
checks to constrainFun()
so that only likelihood ratio test statistics of the constrained model returned if both the loglikelihood & parameter estimates have convergedfindDFC()
, makeD()
, makeDsim()
FindDFC()
to findDFC()
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