The gss Package - NexTag Supports Open Source Initiatives

More documents

Recommendations

Info

14 gssanova0 id.basis=logit.fit$id.basis) ## Obtain estimates and standard errors on a grid est
gssanova0 15 data weights subset Optional data frame containing the variables in the model. Optional vector of weights to be used in the fitting process. Optional vector specifying a subset of observations to be used in the fitting process. offset Optional offset term with known parameter 1. na.action partial method varht nu Details Value prec maxiter Function which indicates what should happen when the data contain NAs. Optional extra unpenalized terms in partial spline models. Score used to drive the performance-oriented iteration. Supported are method="v" for GCV, method="m" for GML, and method="u" for Mallow’s CL. Dispersion parameter needed for method="u". Ignored when method="v" or method="m" are specified. Inverse scale parameter in accelerated life model families. Ignored for exponential families. Precision requirement for the iterations. Maximum number of iterations allowed for performance-oriented iteration, and for inner-loop multiple smoothing parameter selection when applicable. The model specification via formula is intuitive. For example, y~x1*x2 yields a model of the form y = C + f 1 (x1) + f 2 (x2) + f 12 (x1, x2) + e with the terms denoted by "1", "x1", "x2", and "x1:x2". The model terms are sums of unpenalized and penalized terms. Attached to every penalized term there is a smoothing parameter, and the model complexity is largely determined by the number of smoothing parameters. Only one link is implemented for each family. It is the logit link for "binomial", and the log link for "poisson", "Gamma", and "inverse.gaussian". For "nbinomial", the working parameter is the logit of the probability p; see NegBinomial. For "weibull", "lognorm", and "loglogis", it is the location parameter for the log lifetime. The models are fitted by penalized likelihood method through the performance-oriented iteration as described in the reference; the O(n 3 ) algorithms of RKPACK are used for numerical calculations. For family="binomial", "poisson", "nbinomial", "weibull", "lognorm", and "loglogis", the score driving the performance-oriented iteration defaults to method="u" with varht=1. For family="Gamma" and "inverse.gaussian", the default is method="v". gssanova0 returns a list object of class c("gssanova0","ssanova0","gssanova"). The method summary.gssanova0 can be used to obtain summaries of the fits. The method predict.ssanova0 can be used to evaluate the fits at arbitrary points along with standard errors, on the link scale. The methods residuals.gssanova and fitted.gssanova extract the respective traits from the fits.
Page 1 and 2: The gss Package February 16, 2008 V
Page 3 and 4: aids 3 Source Douglas, A. and Delam
Page 5 and 6: cdssden 5 Format Source A vector of
Page 7 and 8: dssden 7 References Gu, C. (2002),
Page 9 and 10: family 9 cfit.nbinomial(y, wt, offs
Page 11 and 12: gauss.quad 11 Source Moreau, T., O
Page 13: gssanova 13 Value gssanova returns
Page 17 and 18: hzdrate.sshzd 17 ## Not run: rm(tes
Page 19 and 20: mkfun.tp 19 See Also mkterm, mkfun.
Page 21 and 22: mkran 21 The Z matrix is determined
Page 23 and 24: mkterm 23 mkterm Assembling Model T
Page 25 and 26: nlm0 25 fit1
Page 27 and 28: ozone 27 Format A data frame contai
Page 29 and 30: predict.ssanova 29 Value For se.fit
Page 31 and 32: project 31 project Projecting Smoot
Page 33 and 34: kpk0 33 nu varht random dc sr y0 Op
Page 35 and 36: ssanova 35 Arguments d k Dimension
Page 37 and 38: ssanova0 37 References Gu, C. (2002
Page 39 and 40: ssanova0 39 Note For complex models
Page 41 and 42: ssden 41 The selective term elimina
Page 43 and 44: sshzd 43 Description Estimate hazar
Page 45 and 46: summary.gssanova 45 ## Not run: dat
Page 47 and 48: summary.gssanova0 47 References Gu,
Page 49 and 50: wesdr 49 Value summary.ssanova retu
Page 51 and 52: Index ∗Topic datasets aids, 2 bac
Page 53: INDEX 53 mkrk.sphere, 22 mkrk.spher

The gss Package - NexTag Supports Open Source Initiatives

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?