The gss Package - NexTag Supports Open Source Initiatives

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6 drkpk Note If variables other than factors or numerical vectors are involved in x, the normalizing constant can not be computed. See Also cpssden and cqssden can be very slow. Fitting function ssden and dssden. drkpk Numerical Engine for ssden and sshzd Description Usage Perform numerical calculations for the ssden and sshzd suites. sspdsty(s, r, q, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha) mspdsty(s, r, q, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha) msphzd(s, r, q, Nobs, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha) Arguments s r q Nobs cnt qd.s qd.r qd.wt prec maxiter alpha Unpenalized terms evaluated at data points. Basis of penalized terms evaluated at data points. Penalty matrix. Total number of lifetime observations. Bin-counts for histogram data. Unpenalized terms evaluated at quadrature nodes. Basis of penalized terms evaluated at quadrature nodes. Quadrature weights. Precision requirement for internal iterations. Maximum number of iterations allowed for internal iterations. Parameter defining cross-validation score for smoothing parameter selection. Details sspdsty is used by ssden to compute cross-validated density estimate with a single smoothing parameter. mspdsty is used by ssden to compute cross-validated density estimate with multiple smoothing parameters. msphzd is used by sshzd to compute cross-validated hazard estimate with single or multiple smoothing parameters.
dssden 7 References Gu, C. (2002), Smoothing Spline ANOVA Models. New York: Springer-Verlag. Gu, C. and Wang, J. (2003), Penalized likelihood density estimation: Direct cross-validation and scalable approximation. Statistica Sinica, 13, 811–826. dssden Evaluating PDF, CDF, and Quantiles of Smoothing Spline Density Estimates Description Evaluate pdf, cdf, and quantiles for smoothing spline density estimates. Usage dssden(object, x) pssden(object, q) qssden(object, p) Arguments object x q p Object of class "ssden". Data frame or vector of points on which density is to be evaluated. Vector of points on which cdf is to be evaluated. Vector of probabilities for which quantiles are to be calculated. Details <strong>The</strong> argument x in dssden is of the same form as the argument newdata in predict.lm, but can take a vector for 1-D densities. pssden and qssden naturally only work for 1-D densities. Value A vector of pdf, cdf, or quantiles. See Also Fitting function ssden and cdssden.
Page 1 and 2: The gss Package February 16, 2008 V
Page 3 and 4: aids 3 Source Douglas, A. and Delam
Page 5: cdssden 5 Format Source A vector of
Page 9 and 10: family 9 cfit.nbinomial(y, wt, offs
Page 11 and 12: gauss.quad 11 Source Moreau, T., O
Page 13 and 14: gssanova 13 Value gssanova returns
Page 15 and 16: gssanova0 15 data weights subset Op
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

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