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Some munging was required to split and combine data in different ways for different inoculation procedures, and dply methods (mostly) were used for that. The Cullis heritability estimator was used. Some useful aspects of this code are running ASReml analyses within functions, which requires some trickery. To be able to fit an animal model, Asreml-r needs (the inverse of) the relationship matrix using. The objective of the analysis was to explore data, transform as necessary to stabilize residuals, and compare inoculation methods and assays in terms of correlations and heritabilites. First we need to load the asreml library: library(asreml). Ears were scored for percent kernel rot at maturity, then grain was bulked and assayed for fumonisin content by two different students at two different universities (LM and TM). Each plot of ~ 25 plants was split in half, with different inoculation treatments applied to each half. Please note that ASReml-R is a commercial package that requires a license.Įxp35_2015_data_analysis.Rmd and Exp35_2015_data_analysis.pdf are R markdown and knitted PDF versions of an analysis of a field experiment with two replications of an alpha incomplete block design of different corn variaties evaluated for Fusarium ear rot and fumonisin content in the grain.

Helper functions for installing or updating the ASReml-R package, intended to reduce the difficulty of finding the correct version for your operating system and R version. ASReml-R is powerful statistical analysis software specially designed for mixed models using Residual Maximum Likelihood (REML) to estimate the parameters in an R environment. For each simulated data set we then fitted a linear mixed effect model (using ASReml-R) containing individual as a random effect resulting in an estimate of.

Some examples of ASReml-R use for mixed models in R. Install or Update the ASReml-R package Description.