Uses maximum likelihood to fit
Y|X ~ Lognormal(beta_0 + beta_x^T X, sigsq). Y
can be precisely measured or subject to multiplicative mean-1 lognormal
errors, in which case replicates can be incorporated by specifying y
as a list).
lognormalreg(y, x = NULL, merror = FALSE, estimate_var = TRUE,
fix_posdef = FALSE, ...)Numeric vector or list.
Numeric vector or matrix. If NULL, model reduces to marginal
lognormal model Y ~ Lognormal(beta_0, sigsq).
Logical value for whether to model multiplicative lognormal measurement errors in Y.
Logical value for whether to return Hessian-based variance-covariance matrix.
Logical value for whether to repeatedly reduce
integrate_tol_hessian by factor of 5 and re-estimate Hessian to try
to avoid non-positive definite variance-covariance matrix.
Additional arguments to pass to nlminb.
List containing:
Numeric vector of parameter estimates.
Variance-covariance matrix (if estimate_var = TRUE).
Returned nlminb object from maximizing the
log-likelihood function.
Akaike information criterion (AIC).