GRCglm: Maximum likelihood estimation for modified Poisson regression...

In GRCRegression: Modified Poisson Regression of Grouped and Right-Censored Counts

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))

GRCRegression documentation built on July 9, 2023, 5:52 p.m.