Description Usage Arguments Details Value Note Author(s) References Examples

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

1 2 |

`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 |

`maxTries` |
The maximum number of times the connection is check for an answer from the MATLAB server before giving up. Default values is 3000 times. |

`interval` |
The interval in seconds between each poll for an answer. Default interval is 30 (second). |

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", "R.matlab", "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.

`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 |

When this function stops abnormally (due an error or warning in MATLAB), you should close the MATLAB software window and disconnect the software. By performing this function again, the connection will be established. When more times need to the connection check for an answer from the MATLAB server before giving up, "maxTries" can be increase. When more times need to increase seconds between each poll for an answer, "interval" can be increase.

Sayyed Reza Alavian and Hani Rezaee[ctb]

[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.

1 2 3 4 5 6 7 8 9 | ```
#Start(not run)
#before running this code, be sure for Matlab installation in your system.
#
# >data(Cow)
# >with(lrqmm_m(id=REGNO,sire=FREG,dam=MREG,X=HYS,Y=HL,cova=AGECAL,alpha=1,tau=0.5)
# ,data=Cow)
#
#
#End(not run)
``` |

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