phenoregressor.BGLR.multikinships | R Documentation |
This regressor implements Genomic BLUP using Bayesian methods from BGLR package, but allows to use more than one covariance matrix.
phenoregressor.BGLR.multikinships( phenotypes, genotypes = NULL, covariances, extraCovariates, type = "RKHS", ... )
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 |
In its simplest form, GBLUP is defined as:
y = 1μ + Z u + e
with
var(y) = K σ_u^2 + Iσ_e^2
Where μ is the overall mean, K is the incidence matrix relating individual weights u to y, and e is a vector of residuals with zero mean and covariance matrix Iσ_e^2
It is possible to extend the above model to include different types of kinship matrices, each capturing different links between genotypes and phenotypes:
y = 1μ + Z1 u1 + Z2 u2 + … + e
with
var(y) = K1 σ_u1^2 + K2 σ_u2^2 + … + Iσ_e^2
This function receives the first kinship matrix K1 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) )
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 from BGLR
BGLR
Other phenoRegressors:
phenoRegressor.BGLR()
,
phenoRegressor.RFR()
,
phenoRegressor.SVR()
,
phenoRegressor.dummy()
,
phenoRegressor.rrBLUP()
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