The gss Package - NexTag Supports Open Source Initiatives

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4 buffalo Source Wang, M.-C. (1989), A semiparametric model for randomly truncated data. Journal of the American Statistical Association, 84, 742–748. bacteriuria Treatment of Bacteriuria Description Usage Format Bacteriuria patients were randomly assigned to two treatment groups. Weekly binary indicator of bacteriuria was recorded for every patient over 4 to 16 weeks. A total of 72 patients were represented in the data, with 36 each in the two treatment groups. data(bacteriuria) A data frame containing 820 observations on the following variables. id trt time infect Identification of patients, a factor. Treatments 1 or 2, a factor. Weeks after randomization. Binary indicator of bacteriuria (bacteria in urine). Source Joe, H. (1997), Multivariate Models and Dependence Concepts. London: Chapman and Hall. References Gu, C. and Ma, P. (2005), Generalized nonparametric mixed-effect models: computation and smoothing parameter selection. Journal of Computational and Graphical Statistics, 14, 485–504. buffalo Buffalo Annual Snowfall Description Annual snowfall accumulations in Buffalo, NY from 1910 to 1973. Usage data(buffalo)
cdssden 5 Format Source A vector of 64 numerical values. Scott, D. W. (1985), Average shifted histograms: Effective nonparametric density estimators in several dimensions. The Annals of Statistics, 13, 1024–1040. cdssden Evaluating Conditional PDF, CDF, and Quantiles of Smoothing Spline Density Estimates Description Usage Evaluate conditional pdf, cdf, and quantiles for smoothing spline density estimates. cdssden(object, x, cond, int=NULL) cpssden(object, q, cond, int=NULL) cqssden(object, p, cond, int=NULL) Arguments object x cond int q p Object of class "ssden". Data frame or vector of points on which conditional density is to be evaluated. One row data frame of conditioning variables. Normalizing constant. Vector of points on which conditional cdf is to be evaluated. Vector of probabilities for which conditional quantiles are to be calculated. Details Value The argument x in cdssden is of the same form as the argument newdata in predict.lm, but can take a vector for 1-D conditional densities. cpssden and cqssden naturally only work for 1-D conditional densities of a numerical variable. cdssden returns a list object with the following components. pdf int Vector of conditional pdf. Normalizing constant. cpssden and cpssden return a vector of conditional cdf or quantiles.
Page 1 and 2: The gss Package February 16, 2008 V
Page 3: aids 3 Source Douglas, A. and Delam
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 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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