Download Package `synbreed`

gpMod
21
random
A formula for random effects of the model. Specifies the matrices to include
in the covariance structure. Each term is either a symmetric matrix, or a factor.
Independent Gaussian random effects are included by passing the corresponding
block factor. For mor details see regress. Only for model="BLUP"
...
further arguments to be used by the genomic prediction models, i.e. prior values
and MCMC options for the BLR function (see BLR) or parameters for the REML
algorithm in regress.
Details
By default, an overall mean is added to the model. If no kin is specified and model = "BLUP", a
G-BLUP model will be fitted. For BLUP, further fixed and random effects can be added through
the arguments fixed and random.
Only a subset of the individuals - the training set - is used to fit the model. This contains all
individuals with phenotypes and genotypes. If kin does not match the dimension of the training set
(if, e.g. ancestors are included), the respective rows and columns from the trainings set are choosen.
Marker effects for model=BLUP are extracted from the corresponding G-BLUP model using their
functional relationship. In this case, fit reports the G-BLUP model.
Value
Object of class gpMod which is a list of
fit
The model fit returned by the genomic prediction method
model
The model type, see ’Arguments’
y
The phenotypic records for the individuals in the training set
g
The predicted genetic values for the individuals in the training set
m
Predicted SNP effects (if available)
kin
Matrix kin
Note
The verbose output of the BLR function is written to a file BLRout.txt in the working directory to
prevent the screen output from overload.
Author(s)
Valentin Wimmer, Hans-Juergen Auinger and Theresa Albrecht
References
Clifford D, McCullagh P (2012). regress: Gaussian Linear Models with Linear Covariance Structure. R package version 1.3-8, URL http://www.csiro.au.
Gustavo de los Campos and Paulino Perez Rodriguez, (2010). BLR: Bayesian Linear Regression.
R package version 1.2. http://CRAN.R-project.org/package=BLR
See Also
kin, crossVal