loglkd: S3 method for mixture model : log-likelihood

In Riemann: Learning with Data on Riemannian Manifolds

S3 method for mixture model : log-likelihood

Description

Given a fitted mixture model f(x) and observations x_1, …, x_n \in \mathcal{M}, compute the log-likelihood

L = \log ∏_{i=1}^n f(x_i) = ∑_{i=1}^n \log f(x_i)

.

Usage

loglkd(object, newdata)

Arguments

object	a fitted mixture model of riemmix class.
newdata	data of n objects (vectors, matrices) that can be wrapped by one of wrap.* functions in the Riemann package.

Value

the log-likelihood.

Examples

# ---------------------------------------------------- #
#            FIT A MODEL & APPLY THE METHOD
# ---------------------------------------------------- #
# Load the 'city' data and wrap as 'riemobj'
data(cities)
locations = cities$cartesian
embed2    = array(0,c(60,2)) 
for (i in 1:60){
   embed2[i,] = sphere.xyz2geo(locations[i,])
}

# Fit a model
k3 = moSN(locations, k=3)

# Evaluate
newloglkd = round(loglkd(k3, locations), 3)
print(paste0("Log-likelihood for K=3 model fit : ", newloglkd))

Riemann documentation built on March 18, 2022, 7:55 p.m.