Given a fitted mixture model \(f(x)\) and observations \(x_1, \ldots, x_n \in \mathcal{M}\), compute the log-likelihood $$L = \log \prod_{i=1}^n f(x_i) = \sum_{i=1}^n \log f(x_i)$$.
Arguments
- object
a fitted mixture model of
riemmixclass.- newdata
data of \(n\) objects (vectors, matrices) that can be wrapped by one of
wrap.*functions in the Riemann package.
Examples
# \donttest{
# ---------------------------------------------------- #
# 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))
#> [1] "Log-likelihood for K=3 model fit : 88.582"
# }