WGR2 (EM) {bWGR}

Expectation-Maximization WGR

Description

Univariate models to find breeding values through regression fitted via expectation-maximization implemented in C++.

Usage

emRR(y, gen, df = 10, R2 = 0.5)
emBA(y, gen, df = 10, R2 = 0.5)
emBB(y, gen, df = 10, R2 = 0.5, Pi = 0.75)
emBC(y, gen, df = 10, R2 = 0.5, Pi = 0.75)
emBCpi(y, gen, df = 10, R2 = 0.5, Pi = 0.75)
emBL(y, gen, R2 = 0.5, alpha = 0.02)
emEN(y, gen, R2 = 0.5, alpha = 0.02)
emDE(y, gen, R2 = 0.5)
emML(y, gen, D = NULL)
lasso(y, gen)

emCV(y, gen, k = 5, n = 5, Pi = 0.75, alpha = 0.02,
     df = 10, R2 = 0.5, avg=TRUE, llo=NULL, tbv=NULL, ReturnGebv = FALSE)

Arguments

y

Numeric vector of response variable (n). NA is not allowed.

gen

Numeric matrix containing the genotypic data. A matrix with n rows of observations and m columns of molecular markers.

df

Hyperprior degrees of freedom of variance components.

R2

Expected R2, used to calculate the prior shape (de los Campos et al. 2013).

Pi

Value between 0 and 1. Expected probability pi of having null effect (or 1-Pi if Pi>0.5).

alpha

Value between 0 and 1. Intensity of L1 variable selection.

D

NULL or numeric vector with length p. Vector of weights for markers.

k

Integer. Folding of a k-fold cross-validation.

n

Integer. Number of cross-validation to perform.

avg

Logical. Return average across CV, or correlations within CV.

llo

NULL or a vector (numeric or factor) with the same length as y. If provided, the cross-validations are performed as Leave a Level Out (LLO). This argument allows the user to predefine the splits. This argument overrides k and n.

tbv

NULL or numeric vector of 'true breeding values' (n) to use to compare cross-validations to. If NULL, the cross-validations will have the phenotypes as prediction target.

ReturnGebv

Logical. If TRUE, it returns a list with the average marker values and fitted values across all cross-validations, in addition to the regular output.

Details

The model for the whole-genome regression is as follows:

y = mu + Xb + e

where y is the response variable, mu is the intercept, X is the genotypic matrix, b is the effect of an allele substitution (or regression coefficient) and e is the residual term. A k-fold cross-validation for model evaluation is provided by emCV.

Value

The EM functions returns a list with the intercept (mu), the regression coefficient (b), the fitted value (hat), and the estimated intraclass-correlation (h2).

The function emCV returns the predictive ability of each model, that is, the correlation between the predicted and observed values from k-fold cross-validations repeated n times.

Author(s)

Alencar Xavier

Examples

     ## Not run: 

data(tpod)
emCV(y,gen,3,3)
          
 
## End(Not run)