gic_criterion | R Documentation |
The GIC allows comparing models fit by Maximum Likelihood (ML) or Penalized Likelihood (PL).
gic_criterion(Y, tree, model="BM", method=c("RidgeAlt", "RidgeArch", "LASSO", "ML",
"RidgeAltapprox", "LASSOapprox"), targM=c("null",
"Variance", "unitVariance"), param=NULL,
tuning=0, REML=TRUE, ...)
Y |
A matrix of phenotypic traits values (the variables are represented as columns) |
tree |
An object of class 'phylo' (see ape documentation) |
model |
The evolutionary model, "BM" is Brownian Motion, "OU" is Ornstein-Uhlenbeck, "EB" is Early Burst, and "lambda" is Pagel's lambda transformation. |
method |
The penalty method. "RidgeArch": Archetype (linear) Ridge penalty, "RidgeAlt": Quadratic Ridge penalty, "LASSO": Least Absolute Selection and Shrinkage Operator, "ML": Maximum Likelihood. |
targM |
The target matrix used for the Ridge regularizations. "null" is a null target, "Variance" for a diagonal unequal variance target, "unitVariance" for an equal diagonal target. Only works with "RidgeArch","RidgeAlt" methods. |
param |
Parameter for the evolutionary model (see "model" above). |
tuning |
The tuning/regularization parameter. |
REML |
Use REML (default) or ML for estimating the parameters. |
... |
Additional options. Not used yet. |
gic_criterion
allows comparing the fit of various models estimated by Penalized Likelihood (see ?fit_t_pl). Use the wrapper GIC
instead for models fit with fit_t_pl
.
a list with the following components
LogLikelihood |
the log-likelihood estimated for the model with estimated parameters |
GIC |
the GIC criterion |
bias |
the value of the bias term estimated to compute the GIC |
The tuning parameter is assumed to be zero when using the "ML" method.
J. Clavel
Konishi S., Kitagawa G. 1996. Generalised information criteria in model selection. Biometrika. 83:875-890.
Clavel, J., Aristide, L., Morlon, H., 2019. A Penalized Likelihood framework for high-dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution. Syst. Biol. 68: 93-116.
GIC.fit_pl.rpanda
,
fit_t_pl
if(test){
if(require(mvMORPH)){
set.seed(123)
n <- 32 # number of species
p <- 2 # number of traits
tree <- pbtree(n=n) # phylogenetic tree
R <- Posdef(p) # a random symmetric matrix (covariance)
# simulate a dataset
Y <- mvSIM(tree, model="BM1", nsim=1, param=list(sigma=R))
# Compute the GIC for ML
gic_criterion(Y, tree, model="BM", method="ML", tuning=0) # ML
# Compare with PL?
#test <- fit_t_pl(Y, tree, model="BM", method="RidgeAlt")
#GIC(test)
}
}
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