Exemplos de uso de Random regression em Inglês e suas traduções para o Português
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Saanen goats? milk yield evaluation using random regression models.
The random regression models have as main advantage a better adjustment of the longitudinal data.
Eight mathematical models were selected, six non-linear models and two models of random regression.
Random regression models were employed, once the database has repeated measurements of animals within the same lactation.
Evaluation of the production andcomposition of milk in the alpine breed goats using multiple-trait random regression models.
For random regression models was used legendre polynomials to model the average growth trajectory.
The genetics value(vg)of animals were predicted with the utilization of random regression models(rrm) animal model below.
The random regression models(rrm) differ by the function used to describe the shape of the lactation curve of the animals.
The main objective of thepresent study was to conduct a genetic evaluation of test-day milk yield(tdmy) using multi-trait random regression models mrrm.
Identity of non-linear models and random regression models for the study of growth curve of meat type quails in different generations in selection.
Genetic evaluation of growth traits and resistance to verminoses in Santa InÃas sheep using random regression models and cluster analysis, BE.EP. MS Abstract.
This work aimed to identify random regression models for genetic evaluation of weight and resistance to tick, gastrointestinal nematodes and eimeria spp.
Data from 10,238 weekly milk yield records from 388 first lactations of Saanen goats were used to evaluate the persistency of lactation under a random regression model.
This study aimed to verify the applicability of reaction norms models(random regression) via reml/blup to evaluate the genotype by environment interaction in plants.
To adjust the random regression models it is necessary to use a covariance matrix of infinite dimension, so an efficient alternative is the use of covariance functions.
The objective of this study was to estimate covariance functions using random regression models for repeated measures analysis of weights of brahman cattle in brazil.
Under this scenario, individual sensitivity of genetic effects to continuous changes of environment can be expressed with reaction norms,which can be evaluated using random regression models.
The analyzes were performed using random regression models considering fixed sex effect and direct additive genetic random effect as functions of the tryptophan: lysine ratios of the diet.
The objective of this was to perform a genetic analysis of growth development in murrah buffaloes from a dairy production system until the age of 600 days using random regression models.
The aim of this study was to compare random regression models under different functions to describe the lactation of animals from holsteins herds in the state of minas gerais.
This study aimed to estimate genetic parameters for milk production on the control day of primiparous holstein cattle raised in rio grande do sul, through random regression models.
Genetic parameters were determined by random regression, and the direct additive genetic effects, maternal genetic and direct permanent environment were modeled by legendre polynomials.
This study aimed to estimate covariance components and genetic parameters for weights and along the growth curve,using bi-trait models(bt) and random regression models(rrm) for polled nellore cattle in northeastern brazil.
The first goal of this research compare non-linear models and random regression, and identify the one that best represents nellore cattle growth curves, and estimate genetic parameters using random regression models.
The aim this study was describe and identify through cluster analysis the different genetic profile of animals gyr andgyr polled breeds for characteristics related to shape of lactation curve obtained with random regression models.
In order toidentify clones of rubber tougher in each environment we tested different random regression models to indicate that best describes the genetic variation of resistance over time considering different environments through a bayesian approach.
The objectives of this study were estimate breeding value under heat stress and heritability coefficients, and also to compare models of different adjustment orders through legendre polynomials,using random regression models for fat, protein, saturated fatty acid, unsaturated fatty acid and milk production.
Among the various methods for the genetic evaluation of livestock,the use of random regression models in the evaluation of the growth curve is efficient by eliminating the need to pre-settings and the ability to use all weights of individual information.
The study aimed was to compare different models of adjusting orders by legendre polynomials and estimate variance components and genetic parameters,for single and multiple-trait random regression models for production milk, protein, fat and lactose.
The relation between the fetal head cephalic circumference and gestational age, measured by the ultrasound,were analyzed using logistic random regression effects models based on fractional polynomials, being used to analyze longitudinal data, the polynomials curve is allowed under different structures of temporal dependence among the observations.