#ASREML WORKSHOP SOFTWARE#
It is flexible statistical software and has applications in (un) balanced longitudinal data, repeated measures data and most importantly in univariate and multivariate plant, animal and fish breeding and genetic data with a relationship matrix for correlated traits. Professor NSW DPI, Australia, and one of the developers of the AsREML software will be the key resource person for this workshop.ĪsREML is a statistical package that fits linear mixed models using residual Maximum Likelihood (REML). The workshop will be conducted under the overall guidance of Dr. The present workshop focuses on teaching the requisite principles involved in mixed models and their analysis by hands on training employing the AsREML. Analysis of a mixed model is a formidable task, however the NARS scientists can adapt mixed model analyses with little difficulty if equipped with the appropriate software and a solid understanding of some basic principles of mixed model. Ignoring or mistreating random effects inadvertently leads to inappropriate analyses and thus to dubious conclusions. Despite this many researchers continue to use of the conventional analysis of variance (ANOVA) model or general linear model (GLM) leading to wrong analyses.
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In a majority of agricultural, animal and fisheries experiments both fixed and random effects are found invariably. Yari Road, Versova, Andheri (west), Mumbai 400 061.
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Copy ASREML.Venue: ICAR-CIFE, Panch Marg, Off.
#ASREML WORKSHOP INSTALL#
Install ConText using ContextSetup.exe 2. Basic Concept of Breeding Value Basic Concept of Breeding Value Basic Concept of Breeding Value Basic Concept of Breeding Value Basic Concept of Breeding Value 4.
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vc’s) Bayes Information Criterion – Minimise BIC = -2*LogL+p*log(dfe) 3. Very powerful at dealing with unbalanced data What are some fixed and random effects? An Example of Mixed Linear Model Mixed Linear Model Solution of Mixed Linear Model Traditional Mixed Linear Model in Tree Breeding Complex Mixed Linear Model Solution of Mixed Linear Model REML ASReml Likelihood Ratio Test Fixed effects must be the same in both models Hierarchical models only For single variances 2 * D Log Likelihood ~ where D Log Likelihood is the LL difference with and without the effect (Section 2.5) For multiple variances For correlations against 0 against 1 Other Model Comparators Non-hierarchical models Akaike Information Criterion – Minimise AIC = -2*LogL+2p (p=no. We are interested in the variances (although we might want prediction for the levels).
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– Random: the levels are random samples from one population. We are mostly interested in estimating the means. – Fixed: where there are different populations (levels), each with its own mean. What Is Mixed Linear Model A combination of fixed effects and random effects. What Is a Linear Model? Put Experiment into a Linear Model Put the Linear Model into Matrix Useful Matrix Operations Transpose Multiplication Trace Determinant Inverse Direct sum ( ) Direct product ( ) 2. ARMS Fusion 2007 ASReml Workshop Harry Wu UPSC, Swedish University of Agriculture Science, Sweden CSIRO Plant Industry, Canberra, Australia Workshop Outline Linear model Mixed linear model Breeding values ASReml and ConTEXT Primer Example of full-sib mating Example of diallel mating Row-Column design Longitudinal data Spatial analysis 1.