REML analysis of linear mixed models
The REML algorithm allows you to analyse linear mixed models i.e. linear models that can contain
both fixed and random effects. In some applications these are known as "multi-level" models. It
can thus be used to analyse unbalanced designs with several error terms (which cannot be
analysed by ANOVA). It can also fit random correlation models to describe the
covariances between random effects as can arise, for example, in the analysis of repeated
measurements or spatial data.
fits a variance-component model by residual (or restricted) maximum
likelihood
defines the model for
REML
controls advanced aspects of the
REML algorithm
displays further output from a
REML analysis
copies information from a
REML analysis into GenStat
data structures
defines a variance structure for random effects in a
REML model
generates an inverse relationship matrix for use when fitting
animal or plant breeding models by
REML
forms predictions from a
REML model
defines the residual term for a
REML model
prints the current model settings for
REML
There are several procedures that may be useful during a REML analysis.
modifies a model formula to contain contrasts of factors
forms the components of a diallel model for REML or
regression
calculates the Akaike and Schwarz information coefficients for
REML
calculates functions of variance components from a
REML analysis
plots one- or two-way tables of means from
REML
prints approximate least significant differences for
REML
means
performs pairwise comparisons between
REML
means
plots residuals from a
REML analysis
There is also a suite of procedures that use REML to estimate QTLs from single
environment or multi-environment trials
displays a genetic map
plots a grid of marker scores for genotypes and indicates
missing data
plots the results of a genome-wide scan for QTL effects in
multi-environment trials
plots the results of a genome-wide scan for QTL effects in
single-environment trials
selects QTLs on the basis of a test statistic profile along the
genome
prints summary statistics of genotypes
exports genotypic and phenotypic data for QTL analysis
reads molecular marker data and calculates IBD
probabilities
imports genotypic and phenotypic data for QTL analysis
performs a QTL backward selection for loci in multi-environment trials
calculates QTL effects in multi-environment trials
performs a genome-wide scan for QTL effects (Simple and
Composite Mapping) in multi-environment trials
performs a backward selection for loci in single-environment trials
calculates QTL effects in single-environment trials
performs a genome-wide scan for QTL effects (Simple and
Composite Mapping) in single-environment trials
calculates a threshold to identify a significant QTL
selects the best variance-covariance model for a set of
environments