Description Usage Arguments Details Value Examples
View source: R/ebpmf_exponential_mixture.R
Uses Empirical Bayes to fit the model
X_{ij} ~ Poi(∑_k L_{ik} F_{jk})
with
L_{.k} ~ g_k()
with g_k being either Mixture of Exponential, or Point Gamma
1 2 | ebpmf_exponential_mixture(X, K, m = 2, maxiter.out = 10,
maxiter.int = 1, seed = 123)
|
X |
count matrix (dim(X) = c(n, p)). |
K |
number of topics |
m |
multiplicative parameter for selecting grid in "ebpm::ebpm_exponential_mixture" |
maxiter.out |
maximum iterations in the outer loop |
maxiter.int |
maximum iterations in the inner loop |
seed |
random seed |
The model is fit in 2 stages: i) estimate g by maximum likelihood (over pi_k) ii) Compute posterior distributions for λ_j given x_j,\hat{g}.
A list containing elements:
qls_mean
A n by k matrix: Approximate posterior mean for L
qls_mean_log
A n by k matrix: Approximate posterior log mean for L
gls
A list of K elements, each element is the estimated prior for the kth column of L
qfs_mean
A p by k matrix: Approximate posterior mean for F
qfs_mean_log
A p by k matrix: Approximate posterior log mean for F
gfs
A list of K elements, each element is the estimated prior for the kth column of F
1 | To add
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