clvm: Fit a CLVM Model

In phenopath: Genomic trajectories with heterogeneous genetic and environmental backgrounds

Description

Fit a covariate latent variable model using coordinate ascent variational inference.

Usage

clvm(y, x, maxiter = 10000, elbo_tol = 1e-05, thin = 1, verbose = TRUE,
  z_init = 1, tau_q = 1, tau_mu = 1, tau_c = 1, a = 2, b = 2,
  tau_alpha = 1, a_beta = 10, b_beta = 1, q = rep(0, nrow(y)),
  model_mu = FALSE, scale_y = TRUE)

Arguments

y	A N-by-G (dynamic) input matrix
x	A N-by-P (static) input matrix
maxiter	Maximum number of CAVI iterations
elbo_tol	The (percent) change in the ELBO below which it is considered converged
thin	The number of iterations to wait each time before re-calculating the elbo
verbose	Print convergence messages
z_init	The initialisation of the latent trajectory. Should be one of A positive integer describing which principal component of the data should be used for initialisation (default 1), or A numeric vector of length number of samples to be used directly for initialisation, or The text character "random", for random initialisation from a standard normal distribution.
tau_q	Hyperparameter tau_q
tau_mu	Hyperparameter tau_mu
tau_c	Hyperparameter tau_c
a	Hyperparameter a
b	Hyperparameter b
tau_alpha	Hyperparameter tau_alpha
a_beta	Hyperparameter a_beta
b_beta	Hyperparameter b_beta
q	Priors on the latent variables
model_mu	Logical - should a gene-specific intercept term be modelled?
scale_y	Logical - should the expression matrix be centre scaled?

Value

A list whose entries correspond to the converged values of the variational parameters along with the ELBO.

Examples

sim <- simulate_phenopath()
fit <- clvm(sim$y, matrix(sim$x))

phenopath documentation built on Nov. 8, 2020, 6:53 p.m.