Most of programs use the same
parameter file. See BLUPF90 manual for details on this file. Some programs have
restrictions on parameter files and some programs use optional parameters. Details
on these can be find in Readme* files in directories
of specific programs.
Below are comments on
specific programs and descriptions of some options.
BLUP
BLUPF90 calculates BLUP with 3 solvers. PCG is the default solver and is usually the fastest
one. There is an option in the PCG module to use the block preconditioner
at a cost of higher memory requirements.
Solving with
SOR would require less memory but
usually would converge slower. In pathological cases
SOR may be more reliable as it does not have an error accumulation properties
of PCG.
FSPAK is usually the most
accurate method and usually uses the most memory.
With SOR, one can convert the
program to using single precision (rh=r4) and use
only half of memory.
Variance component estimation
REMLF90 uses EM REML. For
most problems it is the most reliable algorithm but
can take hundreds of rounds of iteration. REMLF90 was found to have problems
converging with random regression models. In this case, using parameter files
that are too lare tan too small usually helps.
AIREMLF90 usually converges
much faster but sometimes does not converge. Very slow convergence usually
indicates that the model is over parameterized and
there is insufficient information to estimate some variances.
GIBBSF90 is a simple Gibbs
sampler. It is very easy to change but is slow as it recreates LHS and RHS
every round. Use postgibbsf90 to analyzes samples from
this and other Gibbs samplers. In practical cases, results from Gibbs samplers
and REML are similar. One would one or the other based on computing
feasibility. If there are large differences beyond sampling errors, this indicates
problems usually with the Gibbs sampler. Try longer chains or different priors.
Gibbs samplers may be slow to
achieve convergence is initial values are far away from those at convergence,
e.g., 100 times too low or too high. Before using more
complicated models, Karin Meyer advocates using a series of simpler
models.
GIBBS1F90 is usually much
faster than GIBBSF90 because LHS is stored only once and separate from
relationship matrices. Successful analyses were made
with over 20 traits. However, if models are different per trait, the lines due
to effects need to be modified. Also, with too many differences
in models among traits, the program becomes increasingly slower.
GIBBS2F90 adds joint sampling
of correlated effects. This results in faster mixing with random regression and
maternal models.
Gibbs3F90 adds estimation of heterogeneous
residual covariances in classes. The computing costs usually
increase with the number of classes.