GRCglm {GRCRegression}

Maximum likelihood estimation for modified Poisson regression of GRC data

Description

This function implements maximum likelihood estimation of modified Poisson regression of grouped and right-censored counts.

Usage

  GRCglm.P(y, x1, scheme, link.lambda = link.log, weights = rep(1, nrow(x1)),
    num.intercept = 1, xtol_rel = 1e-08, maxit = 100)

Arguments

y

A vector of the GRC outcome.

x1

The design matrix.

scheme

A vector (sorted) of the starting integers of all GRC groups.

link.lambda

The link function for \lambda

weights

The weight vector used to consider sampling weights.

num.intercept

Presence of the regression intercept. Can be set to 0 or 1. This is also used to calculate the null deviance.

xtol_rel, maxit

The tolerancethreshold and maximum number of iteration of the algorithm.

Value

coefficients

The inferred coefficients.

beta

An alias of coefficients.

fitting

The call.

log.likelihood

Value of the log likelihood function.

df.null

The residual degrees of freedom of the null model.

df.residual

The residual degrees of freedom.

null.deviance

Null deviance.

deviance

The deviance.

aic, bic

The AIC and BIC scores.

McFaddenR2, McFaddenAdjR2

The (rep. Adjusted) McFadden R-square.

Examples

set.seed(123)
tp <- genData.P(beta = c(0.5, -1, 1), data.size = 120, scheme = c(0:3, 5, 8),
  scope.lambda = c(1, 10))
a <- GRCglm.P(y = tp$y, x1 = tp$x, scheme = c(0:3, 5, 8))