Refer to the README for a brief description of the contents. All input and output is provided in DataandCode.zip. The contents are layed out according to the order presented in the methods section of the paper. There are also several options for Bayesian approaches, but that will be another post. I will only mention nlme (Non-Linear Mixed Effects), lme4 (Linear Mixed Effects) and asreml (average spatial reml).
#Predict asreml code
All the code is presented in Rmarkdown, and the ‘.as’ files used for ASREML are included as snippets in the. As for many other problems, there are several packages in R that let you deal with linear mixed models from a frequentist (REML) point of view. Alternativly, you can just use the “.sln” files that are provided. To predict the breeding values, users should have ASREML installed. Breifly, this approach estimates marker effects from breeding values obtained using a random regression model, and estimates the marker variance and corresponding p-values. This study builds off the approach used for our 2018 Plant Direct paper where we used RR for genomic prediction of shoot growth trajectories. Refer to the README for a brief description of the contents. ASReml: AN OVERVIEW Rajender Parsad1, Jose Crossa2 and Juan Burgueno2 1I.A.S.R.I., Library Avenue, New Delhi - 110 012, India 2Biometrics and statistics Unit, CIMMYT, Mexico ASReml is a statistical package that fits linear mixed effects models using Residual Maximum Likelihood (REML). The contents are layed out according to the order presented in the paper. All the code is presented in Rmarkdown, and the ‘.as’ files used for ASREML are included as snippets in the. To run the genetic analyses, users should have ASREML installed. This repo contains all the code and data used for the manuscript: “Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping”. The objective of this study was to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). It is not available in a public repository and because of this, when checking it for CRAN, it always gives the following NOTE, which is acceptable to CRAN: > Suggests or Enhances not in mainstream repositories: > asreml, asreml4 > Now asreml has three functions summary.asreml, fitted.asreml and predict.asreml that are (i) not exported and (ii) not declared to be S3 methods. pred <- predict.asreml( asr, classify'DPP', presentlist('Genotype', prwtsc(-.5,1, -.5))) according to your suggestion. Data and Research Resources Random regression genomic prediction