The deviance score
Returns the deviance of a fitted model object by GCV score.
dev.S( y, S, obs, family = gaussian(), off, offdf, criteria = "GCV", W = diag(1, ncol = ncol(S), nrow = nrow(S)), trim = 0, draw = FALSE, ... )
y |
Matrix of set cases with dimension ( |
S |
Smoothing matrix. |
obs |
observed response. |
family |
a description of the error distribution and link function to
be used in the model. This can be a character string naming a family
function, a family function or the result of a call to a family function.
(See |
off |
off |
offdf |
off, degrees of freedom |
criteria |
The penalizing function. By default "Rice" criteria. Possible values are "GCV", "AIC", "FPE", "Shibata", "Rice". |
W |
Matrix of weights. |
trim |
The alpha of the trimming. |
draw |
=TRUE, draw the curves, the sample median and trimmed mean. |
... |
Further arguments passed to or from other methods. |
Up to a constant, minus twice the maximized log-likelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero.
Where
and penalty
function
can be selected from the following criteria:
Generalized Cross-validation (GCV):
Akaike's
Information Criterion (AIC):
Finite Prediction Error (FPE)
Shibata's model selector (Shibata):
Rice's bandwidth selector (Rice):
Returns GCV score calculated for input parameters.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Wasserman, L. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.
Hardle, W. Applied Nonparametric Regression. Cambridge University Press, 1994.
Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/
data(phoneme) mlearn<-phoneme$learn np<-ncol(mlearn) tt<-mlearn[["argvals"]] S1 <- S.NW(tt,2.5) gcv1 <- dev.S(mlearn$data[1,],obs=(sample(150)), S1,off=rep(1,150),offdf=3) gcv2 <- dev.S(mlearn$data[1,],obs=sort(sample(150)), S1,off=rep(1,150),offdf=3)
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