Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model with varying dispersion.

This is a workhorse function in which glm.cmp to call upon to fit a mean-parametrized Conway-Maxwell Poisson generalized linear model with varying dispersion.

fit_glm_cmp_vary_nu(
  y = y,
  X = X,
  S = S,
  offset = offset,
  betastart = betastart,
  gammastart = gammastart,
  lambdalb = lambdalb,
  lambdaub = lambdaub,
  maxlambdaiter = maxlambdaiter,
  tol = tol
)

Arguments

y

the response y vector.

X

the design matrix for regressing the mean

S

the design matrix for regressing the dispersion

offset

this can be used to specify an a priori known component to be included in the linear predictor for mean during fitting. This should be NULL or a numeric vector

betastart

starting values for the parameters in the linear predictor for mu.

gammastart

starting values for the parameters in the linear predictor for nu.

lambdalb, lambdaub

numeric: the lower and upper end points for the interval to be searched for lambda(s). The default value for lambdaub should be sufficient for small to moderate size nu. If nu is large and required a larger lambdaub, the algorithm will scale up lambdaub accordingly.

maxlambdaiter

numeric: the maximum number of iterations allowed to solve for lambda(s).

tol

numeric: the convergence threshold. A lambda is said to satisfy the mean constraint if the absolute difference between the calculated mean and a fitted values is less than tol.

Value

A fitted model object of class cmp similar to one obtained from glm or glm.nb.

Examples

## For examples see example(glm.cmp)