gammareg {dvmisc} | R Documentation |
Uses maximum likelihood to fit
Y|X ~ Gamma(exp(beta_0 + beta_x^T X), b), with the
shape-scale (as opposed to shape-rate) parameterization described in
GammaDist
. 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.
gammareg(y, x = NULL, merror = FALSE, integrate_tol = 1e-08,
integrate_tol_hessian = integrate_tol, estimate_var = TRUE,
fix_posdef = FALSE, ...)
y |
Numeric vector. |
x |
Numeric vector or matrix. If |
merror |
Logical value for whether to model multiplicative lognormal measurement errors in Y. |
integrate_tol |
Numeric value specifying the |
integrate_tol_hessian |
Same as |
estimate_var |
Logical value for whether to return Hessian-based variance-covariance matrix. |
fix_posdef |
Logical value for whether to repeatedly reduce
|
... |
Additional arguments to pass to |
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).