What is ASReml-R?
ASReml-R, the powerful statistical package that fits mixed linear models (LMMs) using maximum residual likelihood (REML) in the R environment.
This release offers a more unified framework and extended functionality for LMM analysis, particularly for large and complex datasets. You have the following features:
- new and improved licensing
- a simplified and more significant syntax for the specification of the model:
- rcov becomes residual and direct sum structures for residual models with partitioned data in sections to which separate variance structures are applied are now logically specified using a dsum function ()
- the rarely used options are now listed externally for the model call in a new function (asreml.options)
- a more unified structure for related arguments in the call to asreml () and the output object, for example
- the na.action () to deal with missing values in the response and explanatory variables
- o a formula component of the ASReml-R object that lists the formulas of the fixed, random, sparse, and residual model
- improved updates on analytical factor models and an rr () variant of reduced classification of the fa variance model function ()
- a simpler and more consistent specification of known variance models (including relationship matrices) through the vm (function), which also meets known singular arrays
- computationally efficient adjustment of random regression models when there are more variables than observations - motivated by the use of SNP marker data to explain genotypes
- more informative warnings and error messages
- improved graphics with ggplot2.
New more advanced features include:
- introduction of the variance model itself () to allow the specification of a user-defined variance structure
- extensions for generalized linear models, including threshold models and bivariate models with one variable having a normal distribution and the other variable distributed from an exponential family distribution
- generate design matrices to allow the use of derived model terms and functions; design argument for asreml.options ()
- functions to generate factors that combine levels of a factor or use a subset of levels to allow easier prediction of models; combine, prune, gpf (), sbs ().
Also included:
- compute functions of variance components and their approximate standard errors; vpredict ()
- calculation of information criteria, including CBI and BIC.
System requirements:
CPU: 1.8 GHz dual core processor
RAM: 2 GB
HDD / SSD: 50 GB of free space on the main drive
Operating system: Microsoft Windows 8 32-bit, Microsoft Windows 7 32-bit and Microsoft Windows Vista 32-bit
Resolution: 1024×768
Supported operating systems
Mac OS X 10.5 x (Leopard), Mac OS X 10.6 (Snow Leopard), Mac OS X 10.7 (Lion), Mac OS X 10.7 (Mountain Lion), OS X 10.9 (Mavericks) and Mac OS X 10.10 (Yosemite)
Hardware requirements
Memory - 1 GB of RAM and more
Free disk space - 50 MB
System type - 32-bit and 64-bit OS
