phyloglmstep: Stepwise model selection for Phylogenetic Generalized Linear...

In phylolm: Phylogenetic Linear Regression

Stepwise model selection for Phylogenetic Generalized Linear Model

Description

Performs stepwise model selection for phylogenetic generalized linear models, using the criterion -2*log-likelihood + k*npar, where npar is the number of estimated parameters and k=2 for the usual AIC.

Usage

phyloglmstep(formula, starting.formula = NULL, data=list(), phy, 
       method = c("logistic_MPLE","logistic_IG10", "logistic_MLE", "poisson_GEE"),
       direction = c("both", "backward", "forward"), trace = 2,
       btol = 10, log.alpha.bound = 4, start.beta=NULL, 
       start.alpha=NULL, boot = 0, full.matrix = TRUE,
       k=2, ...)

Arguments

formula	formula of the full model.
starting.formula	optional formula of the starting model.
data	a data frame containing variables in the model. If not found in data, the variables are taken from current environment.
phy	a phylogenetic tree of type phylo with branch lengths.
method	The "logistic_IG10" method optimizes a GEE approximation to the penalized likelihood of the logistic regression. "logistic_MPLE" maximizes the penalized likelihood of the logistic regression. In both cases, the penalty is Firth's correction.
direction	direction for stepwise search, can be both, forward, and backward.
trace	if positive, information on each searching step is printed. Larger values may give more detailed information.
btol	bound on the linear predictor to bound the searching space.
log.alpha.bound	bound for the log of the parameter alpha.
start.beta	starting values for beta coefficients.
start.alpha	starting values for alpha (phylogenetic correlation).
boot	number of independent bootstrap replicates, 0 means no bootstrap.
full.matrix	if TRUE, the full matrix of bootstrap estimates (coefficients and alpha) will be returned.
k	optional weight for the penalty.
...	further arguments to be passed to the function optim.

Details

The default k=2 corresponds to the usual AIC penalty. Use k=\log(n) for the usual BIC, although it is unclear how BIC should be defined for phylogenetic regression.

See phyloglm for details on the possible phylogenetic methods for the error term, for default bounds on the phylogenetic signal parameters, or for matching tip labels between the tree and the data.

Value

A phyloglm object correponding to the best model is returned.

Author(s)

Rutger Vos

See Also

phyloglm.

Examples

set.seed(123456)
tre = rcoal(60)
taxa = sort(tre$tip.label)
b0=0; b1=1;
x1 = rTrait(phy=tre,model="BM",
           parameters=list(ancestral.state=0,sigma2=10))
x2 = rTrait(phy=tre,model="BM",
            parameters=list(ancestral.state=0,sigma2=10))
x3 = rTrait(phy=tre,model="BM",
            parameters=list(ancestral.state=0,sigma2=10))
X = cbind(rep(1,60), x1)
y = rbinTrait(n=1,phy=tre, beta=c(-1,0.5), alpha=1 ,X=X)
dat = data.frame(trait=y[taxa],pred1=x1[taxa],pred2=x2[taxa],pred3=x3[taxa])
fit = phyloglmstep(trait~pred1+pred2+pred3,data=dat,phy=tre,method="logistic_MPLE",direction="both")
summary(fit)

phylolm documentation built on Oct. 1, 2024, 1:09 a.m.