lrqmm: Fitting Linear Quantile Regression Mixed Models With...

In LRQMM: Fitting Linear Quantile Regression Mixed Models with Relationship Matrix

Description

Fit a quantile regression mixed model involved Relationship Matrix using a sparse implementation of the Frisch-Newton interior-point algorithm.

Usage

lrqmm(id, sire, dam, X, Y,cova=NULL , alpha = 0 , tau = 0.5)

Arguments

id	The number form animal record as column matrix
sire	The number form father's animal record as column matrix
dam	The number form mother's animal record as column matrix
X	fixed effect(s) as column matrix that will change to factor variable in this function
Y	a response column matrix
cova	covariate effect(s) column matrix
alpha	a parameter for raite error's varince to variance of random effects dependent on statistical model (Animal model, Sire model, etc.)
tau	desired quantile

Details

The function computes an estimate on the tau-th quantile effects of the linear mixed model. This is a sparse implementation of the Frisch-Newton algorithm for quantile regression described in Portnoy and Koenker (1997).

We used "GeneticsPed", "Matrix", "kinship2", "MCMCglmm", "rsvd", "SparseM" and "quantreg" packages in this function. befor using "lrqmm" function be sure from installation this packages.

"GeneticsPed" available in

<https://bioconductor.org/packages/release/bioc/src/contrib/GeneticsPed_1.46.0.tar.gz> or orders in <http://bioconductor.org/packages/release/bioc/html/GeneticsPed.html>.

other packages are available in CRAN.

Value

Fixed effects	estimate for fixed effect(s) from linear quantile regression mixed model with its standard error
cova effects	estimate for covariate effect(s) from linear quantile regression mixed model with its standard error
Random effects	estimate for random effect(s) from linear quantile regression mixed model with its standard error
residuals	estimate for model residuals from linear quantile regression mixed model
Time_between_start_to_end	execution time of linear quantile regression mixed model
MAE	mean absolute error for fitted model
summary	reporting quantile for effects estimation, variance of response variable, variance of pedigree's random.effect, variance of record's random.effect, number of observations, pedigree's length, fix effect lavels and random effect lavels

Author(s)

Sayyed Reza Alavian

References

[1]Alavian, S. R. (2019). Creating LRQMM package for predicting the breeding value of animals by corrected mixed quantile regression (Unpublished master's thesis). Ferdowsi University Of Mashhad. Mashhad. Iran.[Persian].

[2]Koenker, R. and S. Portnoy (1997). The Gaussian Hare and the Laplacean Tortoise: Computability of Squared-error vs Absolute Error Estimators, (with discussion). Statistical Science, 12, 279-300. <https://www.jstor.org/stable/2246216>

[3]Koenker, R. W. (2005). Quantile Regression, Cambridge U. Press. ISBN: 0521608279.

[4]Mrode, R. A. (2005). Linear Models for the Prediction of Animal Breeding Values. 3rd edition. CABI International. ISBN: 9781780643915.

Examples

#Start(not run)


data(Cow)
with(lrqmm(id=REGNO,sire=FREG,dam=MREG,X=HYS,Y=HL,cova=AGECAL,alpha=1,tau=0.5)
,data=Cow)


#End(not run)

LRQMM documentation built on Oct. 4, 2021, 9:08 a.m.