Estimate unknown parameters with constrained approach.
constrained.estimate(m, p, q, n, betastart, bvarstart, psi.start,
eta.start, gamma.start, Y, X, Y.c, ziMatrix,
gn = 3)Number of OTUs.
Number of covariates for count model (e.g., beta-binomial).
Number of covariates for zero model.
Number of samples.
Matrix of estimated betas, which are the effects/coefficients for the count model, with dimension p by m. It is used as initial values for the optimization procedure to estimate betas.
Matrix of variance of estimated betas with dimension p by m.
Estimated vector of logit of overdispersion parameters with length m. And psi.start will be used as initial values for the optimization procedure to estimate psi.
Matrix of estimated etas, which are the effects/coefficients for the zero model, with dimension q by m. It is used as initial values for the optimization procedure to estimate etas.
Estimation vector of the coefficients in the polynomial mean-overdispersion relationship in constrained approach.
Count matrix with dimension n by m.
The design matrix (n by p, p is the number of covariates) for the count model (e.g., beta-binomial), and intercept is included.
Vector of library size with length n.
The design matrix (n by q) for the zero model, and intercept is included.
We use a polynomial with degree of freedom gn to fit the mean-overdispersion relationship.
Estimation matrix of beta (p by m).
Estimation matrix of the variance of estimated betahat (p by m).
Estimation vector of the logit of the overdispersion parameters (with length m).
Estimation matrix of eta (q by m).