deltaSE()
function to calculate approximate standard errors for functions of (co)variance parameters (e.g., h2, standard deviations of variances, or correlations)
Introduce Gcon
and Rcon
arguments to gremlin()
for constraining parameters
Gstart
and Rstart
argumentssire
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).
change version numbering to just 3 numbers (instead of 4)
update()
function
Implement "step-halving" algorithm for AI updates
gremlinControl()
using the step
argumentgremlinControl()
function for advanced changes to the way gremlin runsem
, ai
, and elsewhere (where relevant) in next versionCompletely revised way models are built and called
grMod
and gremlinR
classes.grMod
is the model structure for which a log-likelihood can be calculatedgremlinR
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.lambda
models).M
) matrix from which the Cholesky factorization (and logDetC
and tyPy
calculations are made)C
) and obtain tyPy
and logDetC
using thisM
and C
, now do a solve
with Cholesky of C
(sLc
/Lc
in R
/c++
code) to calculate tyPy
based off Boldman and Van Vleckgremlin
objectsAIC
, residuals
, anova
, and nobs
summary
, print
, and logLik
methods as wellImproved 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).
Documentation has switched from filling out the .Rd
files manually to providing
documentation next to the function code in the .R
files using roxygen2
gremlin
is born!Congratulations, its a gremlin!
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