family {gss} | R Documentation |
Utility functions for fitting Smoothing Spline ANOVA models with non-Gaussian responses.
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)
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. |
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.