WGR2 (EM) | R Documentation |
Univariate models to find breeding values through regression fitted via expectation-maximization implemented in C++.
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)
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)
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)
y |
Numeric vector of response variable ( |
gen |
Numeric matrix containing the genotypic data. A matrix with |
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 |
tbv |
NULL or numeric vector of 'true breeding values' ( |
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. |
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
.
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.
Alencar Xavier
## Not run:
data(tpod)
emCV(y,gen,3,3)
## End(Not run)
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