lognormalreg: Linear Regression of log(Y) vs. Covariates with Y Potentially...

In dvmisc: Convenience Functions, Moving Window Statistics, and Graphics

Description

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).

Usage

lognormalreg(y, x = NULL, merror = FALSE, estimate_var = TRUE,
  fix_posdef = FALSE, ...)

Arguments

y	Numeric vector or list.
x	Numeric vector or matrix. If NULL, model reduces to marginal lognormal model Y ~ Lognormal(beta_0, sigsq).
merror	Logical value for whether to model multiplicative lognormal measurement errors in Y.
estimate_var	Logical value for whether to return Hessian-based variance-covariance matrix.
fix_posdef	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.

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

dvmisc documentation built on Dec. 18, 2019, 1:35 a.m.