phenoregressor.BGLR.multikinships {GROAN} | R Documentation |
Multi-matrix GBLUP using BGLR
Description
This regressor implements Genomic BLUP using Bayesian methods from BGLR package, but allows to use more than one covariance matrix.
Usage
phenoregressor.BGLR.multikinships(
phenotypes,
genotypes = NULL,
covariances,
extraCovariates,
type = "RKHS",
...
)
Arguments
phenotypes |
phenotypes, a numeric array (n x 1), missing values are predicted |
genotypes |
added for compatibility with the other GROAN regressors, must be NULL |
covariances |
square matrix (n x n) of covariances. |
extraCovariates |
the extra covariance matrices to be added in the GBLUP model, collated in a single matrix-like structure, with optionally first column as an ignored intercept (supported for compatibility). See details, below. |
type |
character literal, one of the following: FIXED (Flat prior), BRR (Gaussian prior), BL (Double-Exponential prior), BayesA (scaled-t prior), BayesB (two component mixture prior with a point of mass at zero and a scaled-t slab), BayesC (two component mixture prior with a point of mass at zero and a Gaussian slab), RKHS (Gaussian processes, default) |
... |
extra parameters are passed to |
Details
In its simplest form, GBLUP is defined as:
with
Where is the overall mean,
is the incidence matrix
relating individual weights
to
, and
is a
vector of residuals with zero mean and covariance matrix
It is possible to extend the above model to include different types of kinship matrices, each capturing different links between genotypes and phenotypes:
with
This function receives the first kinship matrix via the
covariances
argument and an arbitrary number of extra matrices via the extraCovariates
built as follow:
#given the following defined variables y = <some values, Nx1 array> K1 = <NxN kinship matrix> K2 = <another NxN kinship matrix> K3 = <a third NxN kinship matrix> #invoking the multi kinship GBLUP y_hat = phenoregressor.BGLR.multikinships( phenotypes = y, covariances = K1, extraCovariates = cbind(K2, K3) )
Value
The function returns a list with the following fields:
-
predictions
: an array of (n) predicted phenotypes, with NAs filled and all other positions repredicted (useful for calculating residuals) -
hyperparams
: empty, returned for compatibility -
extradata
: list with information on trained model, coming fromBGLR
See Also
Other phenoRegressors:
phenoRegressor.BGLR()
,
phenoRegressor.RFR()
,
phenoRegressor.SVR()
,
phenoRegressor.dummy()
,
phenoRegressor.rrBLUP()