clvm {phenopath} | R Documentation

## Fit a CLVM Model

### 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))

[Package phenopath version 1.8.0 Index]