1.0.1
NEW
deltaSE()
function to calculate approximate standard
errors for functions of (co)variance parameters (e.g., h2,
standard deviations of variances, or correlations)
- this can take a formula for the function or a character
expression
- also allows for a list of formulas or character expressions e.g.,
calculate all variance components as proportions of total variance
- Introduce
Gcon
and Rcon
arguments to
gremlin()
for constraining parameters
- enables parameters to be fixed or otherwise constrained
- works in conjunction with the
Gstart
and
Rstart
arguments
- For example in a simple
sire
model, we could restrain
the sire
variance =0.38
. ``` grSf <-
gremlin(WWG11 ~ sex, random= ~ sire, data = Mrode11, Gstart =
list(matrix(0.38)), Gcon = list(“F”), control = gremlinControl(lambda =
FALSE))
```
- Similar to above change (
Gcon
/Rcon
),
introduced steps to deal with parameters outside of the boundaries of
their parameter space (e.g., variance < 0).
- restrain these parameters to near their boundaries (after trying
step-reduction calculation)
- re-calculate Average Information, conditional on restrained
parameters
- See Gilmour. 2019. J. Anim. Breed. Genet. for specifics
- change version numbering to just 3 numbers (instead of 4)
- just dropping last number
Minor Changes
- create new c++ function to handle quasi Newton-Rhapson algorithm
- allows secondary checks of appropriateness/naughtiness for proposed
parameters based on a conditional AI algorithm (conditional on
parameters restrained to boundary condition)
1.0.0.1
NEW
update()
function
- can now continue a model where it left off or change the structure
(e.g., drop a single variance component for likelihood ratio test)
- Implement “step-halving” algorithm for AI updates
- restricts parameter updates if AI algorithm proposes a change of
>80% of original parameter value
- amount by which a parameter change is restricted can be set in
gremlinControl()
using the step
argument
Minor Changes
- Implement more efficient algorithms in the c++ code, that were
developed in the R code for version 1.0.0.0.
- Add
gremlinControl()
function for advanced
changes to the way gremlin runs
- Begin major improvements to speed of gradient calculation function
- changes to be incorporated in
em
, ai
, and
elsewhere (where relevant) in next version
- implements calculations that take advantage of sparsity (i.e., don’t
calculate values where there are zeroes)
1.0.0.0
NEW
- Completely revised way models are built and called
- made a “modular” series of functions for setting up the model and
optimizing the REML likelihood
- new
grMod
and gremlinR
classes.
grMod
is the model structure for which a log-likelihood
can be calculated
gremlinR
class distinguishes from gremlin
class in that gremlinR
objects will only use R
code written by the package in order to run the model. Class
gremlin
will execute underlying c++ code written in the
package.
- Average Information algorithm has been
vastly improved
ai()
efficiently calculates the AI matrix without
directly computing several matrix inverses (as previously coded)
lambda
and alternative parameterizations now possible
and executed by the same code
lambda
parameterization is the REML likelihood of the
variance ratios after factoring out a residual variance from the
Mixed Model Equations.
- the alternative does not have a special name, this is just
a model of all (co)variance parameters as (co)variance parameters (as
opposed to ratios, as in the
lambda
models).
- instead of completely separate functions for these two
parameterizations, there is an argument that runs alternative lines of
code, wherever the calculations differ for these two different
parameterizations
Minor Changes
- No long construct Mixed Model Array (
M
) matrix from
which the Cholesky factorization (and logDetC
and
tyPy
calculations are made)
- Changed to directly construct coefficient matrix of mixed model
equations (
C
) and obtain tyPy
and
logDetC
using this
- Previously had to store Cholesky factorizations of both
M
and C
, now do a solve
with
Cholesky of C
(sLc
/Lc
in
R
/c++
code) to calculate tyPy
based off Boldman and Van Vleck
0.1.0.0
NEW
- methods for
gremlin
objects
- notably,
AIC
, residuals
,
anova
, and nobs
- updated the
summary
, print
, and
logLik
methods as well
0.0.2.0
Improved algorithm that reduces computational resources and time!
Also implemented c++ code in gremlin()
, while keeping
gremlinR()
purely the R implementation (at least from the
package writing standpoint).
0.0.1.0
NEW
Documentation has switched from filling out the .Rd
files manually to providing documentation next to the function code in
the .R
files using roxygen2
0.0.0.1 April 2017
gremlin
is born!
Congratulations, its a gremlin!