family {gss}R Documentation

Utility Functions for Error Families

Description

Utility functions for fitting Smoothing Spline ANOVA models with non-Gaussian responses.

Usage

mkdata.binomial(y, eta, wt, offset)
dev.resid.binomial(y, eta, wt)
dev.null.binomial(y, wt, offset)
cv.binomial(y, eta, wt, hat, alpha)
y0.binomial(y, eta0, wt)
proj0.binomial(y0, eta, offset)
kl.binomial(eta0, eta1, wt)
cfit.binomial(y, wt, offset)

mkdata.poisson(y, eta, wt, offset)
dev.resid.poisson(y, eta, wt)
dev.null.poisson(y, wt, offset)
cv.poisson(y, eta, wt, hat, alpha, sr, q)
y0.poisson(eta0)
proj0.poisson(y0, eta, wt, offset)
kl.poisson(eta0, eta1, wt)
cfit.poisson(y, wt, offset)

mkdata.Gamma(y, eta, wt, offset)
dev.resid.Gamma(y, eta, wt)
dev.null.Gamma(y, wt, offset)
cv.Gamma(y, eta, wt, hat, rss, alpha)
y0.Gamma(eta0)
proj0.Gamma(y0, eta, wt, offset)
kl.Gamma(eta0, eta1, wt)
cfit.Gamma(y, wt, offset)

mkdata.inverse.gaussian(y, eta, wt, offset)
dev.resid.inverse.gaussian(y, eta, wt)
dev.null.inverse.gaussian(y, wt, offset)

mkdata.nbinomial(y, eta, wt, offset, nu)
dev.resid.nbinomial(y, eta, wt)
dev.null.nbinomial(y, wt, offset)
cv.nbinomial(y, eta, wt, hat, alpha)
y0.nbinomial(y,eta0,nu)
proj0.nbinomial(y0, eta, wt, offset)
kl.nbinomial(eta0, eta1, wt, nu)
cfit.nbinomial(y, wt, offset, nu)

mkdata.weibull(y, eta, wt, offset, nu)
dev.resid.weibull(y, eta, wt, nu)
dev.null.weibull(y, wt, offset, nu)
cv.weibull(y, eta, wt, hat, nu, alpha)
y0.weibull(y, eta0, nu)
proj0.weibull(y0, eta, wt, offset, nu)
kl.weibull(eta0, eta1, wt, nu, int)
cfit.weibull(y, wt, offset, nu)

mkdata.lognorm(y, eta, wt, offset, nu)
dev.resid.lognorm(y, eta, wt, nu)
dev0.resid.lognorm(y, eta, wt, nu)
dev.null.lognorm(y, wt, offset, nu)
cv.lognorm(y, eta, wt, hat, nu, alpha)
y0.lognorm(y, eta0, nu)
proj0.lognorm(y0, eta, wt, offset, nu)
kl.lognorm(eta0, eta1, wt, nu, y0)
cfit.lognorm(y, wt, offset, nu)

mkdata.loglogis(y, eta, wt, offset, nu)
dev.resid.loglogis(y, eta, wt, nu)
dev0.resid.loglogis(y, eta, wt, nu)
dev.null.loglogis(y, wt, offset, nu)
cv.loglogis(y, eta, wt, hat, nu, alpha)
y0.loglogis(y, eta0, nu)
proj0.loglogis(y0, eta, wt, offset, nu)
kl.loglogis(eta0, eta1, wt, nu, y0)
cfit.loglogis(y, wt, offset, nu)

Arguments

y Model response.
eta Fitted values on link scale.
wt Model weights.
offset Model offset.
nu Size for nbinomial. Inverse scale for log life time.

Details

These are not to be called by the user.

mkdata.x create the pseudo data to be used in iterated penalized least squares fitting. dev.resid.x calculate the deviance residuals. dev.null.x calculate the deviance of the constant null model.

See Also

gssanova.


[Package gss version 0.9-3 Index]