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ASReml-R, the powerful statistical package that fits linear mixed models (LMMs) using Residual Maximum Likelihood (REML) in the R environment is now at version 4.

    This version offers a more unified framework and extended functionality for LMM analysis, particularly for large and complex data sets. New features with Version 4 include:

    • new and improved licensing
    • a simplified and more meaningful syntax for model specification:
      • rcov becomes residual and direct sum structures for residual models with data partitioned into sections to which separate variance structures are applied are now logically specified using a dsum() function
      • rarely used options are now listed externally to the model call in a new asreml.options() function
    • a more unified framework for related arguments in the call to asreml()and the output object , e.g.
      • na.action() for dealing with missing values in both the response and explanatory variables
      • formulae component of the ASReml-R object which lists the fixed, random, sparse and residual model formulae
    • improved updates in factor analytic models and a reduced rank rr() variant of the fa() variance model function
    • a simpler, more consistent specification of known variance models (including relationship matrices) through the vm() function, which also caters for known singular matrices
    • computationally efficient fitting of random regression models when there are more variables than observations – motivated by the use of SNP marker data to explain genotypes
    • more informative warning and error messages
    • improved graphics powered by ggplot2.

    More advanced functionality new with Version 4 includes:

    • introduction of the own() variance model to allow the specification of a user-defined variance structure
    • extensions to generalized linear models including threshold models and bivariate models with one variate having a normal distribution and the other variate distributed from an exponential family distribution
    • generating design matrices to allow use of derived model terms and functions; design argument to asreml.options()
    • functions to generate factors that either combine levels of a factor or use a subset of levels to allow easier prediction of models; combine, prune, gpf(), sbs().

    Also included:

    • computing functions of variance components and their approximate standard errors; vpredict()
    • calculating information criteria including AIC and BIC.

    This is just a selection from the full set of new features available with ASReml-R Version 4. A detailed account of the new functionality is presented in a navigation guide which guides existing users in transitioning from Version 3 to Version 4.

    ASReml-R in industry

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