countprop (version 1.1.1)

mleLR: Maximum Likelihood Estimate for multinomial logit-normal model

Description

Returns the maximum likelihood estimates of multinomial logit-normal model parameters given a count-compositional dataset. The MLE procedure is based on the multinomial logit-Normal distribution, using the EM algorithm from Hoff (2003).

Usage

mleLR(
  y,
  max.iter = 10000,
  max.iter.nr = 100,
  tol = 1e-06,
  tol.nr = 1e-06,
  lambda.gl = 0,
  gamma = 0.1,
  lr.penalty = c("alr", "clr"),
  verbose = FALSE
)

Value

The additive log-ratio of y (v); maximum likelihood estimates of mu, Sigma, and Sigma.inv (e.g. the covariance and precision matrices) on the ALR scale. The CLR scale versions are Sigma.clr and Sigma.inv.clr, respectively; the log-likelihood (log.lik); the EBIC (extended Bayesian information criterion) of the log-likelihood of the multinomial logit-Normal model with the graphical lasso penalty (ebic); degrees of freedom of the Sigma.inv

matrix (df).

Arguments

y

Matrix of counts; samples are rows and features are columns.

max.iter

Maximum number of iterations

max.iter.nr

Maximum number of Newton-Raphson iterations

tol

Stopping rule

tol.nr

Stopping rule for the Newton-Raphson algorithm

lambda.gl

Penalization parameter lambda, for the graphical lasso penalty. Controls the sparsity of Sigma

gamma

Gamma value for EBIC calculation of the log-likelihood

lr.penalty

Should the precision matrix be penalized on the ALR or CLR scale?

verbose

If TRUE, print information as the functions run

Examples

data(singlecell)
mle <- mleLR(singlecell)

mle$mu
mle$Sigma
mle$ebic