Generic Methods for S3 class cauphyfit.

Generic Methods for S3 class cauphyfit.

# S3 method for class 'cauphyfit'
print(x, digits = max(3, getOption("digits") - 3), ...)

# S3 method for class 'cauphyfit'
vcov(object, ...)

# S3 method for class 'cauphyfit'
logLik(object, ...)

# S3 method for class 'logLik.cauphyfit'
AIC(object, k = 2, ...)

# S3 method for class 'cauphyfit'
AIC(object, k = 2, ...)

# S3 method for class 'cauphyfit'
confint(object, parm, level = 0.95, ...)

# S3 method for class 'cauphyfit'
coef(object, ...)

Arguments

x

an object of class "phylolm".

digits

number of digits to show in summary method.

...

further arguments to methods.

object

an object of class cauphyfit.

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

parm

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

level

the confidence level required.

Value

Same value as the associated methods from the stats package:

vcov

an estimated covariance matrix, see compute_vcov;

logLik

an object of class logLik;

AIC

a numeric value;

confint

a matrix (or vector) with columns giving lower and upper confidence limits for each parameter;

coef

coefficients extracted from the model;

See also

fitCauchy, vcov, logLik AIC, confint, coef, predict, predict.phylolm

Examples

# Simulate tree and data
set.seed(1289)
phy <- ape::rphylo(20, 0.1, 0)
dat <- rTraitCauchy(n = 1, phy = phy, model = "cauchy",
                    parameters = list(root.value = 10, disp = 0.1))
# Fit the data
fit <- fitCauchy(phy, dat, model = "cauchy", method = "reml")
fit
#> Call:
#> fitCauchy(phy = phy, trait = dat, model = "cauchy", method = "reml")
#> 
#>                 AIC logLik (restricted) 
#>               74.06              -36.03 
#> 
#> Parameter estimate(s) using REML:
#> dispersion: 0.06255127 
# vcov matrix
vcov(fit)
#>              disp
#> disp 0.0003866757
# Approximate confidence intervals
confint(fit)
#> Approximated asymptotic confidence interval using the Hessian.
#>          2.5 %    97.5 %
#> disp 0.0240104 0.1010921
# log likelihood of the fitted object
logLik(fit)
#> 'log Lik.' -36.02821 (df=1)
# AIC of the fitted object
AIC(fit)
#> [1] 74.05642
# coefficients
coef(fit)
#>       disp 
#> 0.06255127