sm/0000755000176200001440000000000014564167562010711 5ustar liggesuserssm/NAMESPACE0000644000176200001440000000362414563473447012136 0ustar liggesusersuseDynLib(sm, .registration = TRUE) export(binning, britmap, hcv, hnorm, hsj, nise, nmise, nnbr, pause, provide.data, sig.trace, sm.ancova, sm.autoregression, sm.binomial, sm.binomial.bootstrap, sm.density, sm.density.compare, sm.options, sm.poisson, sm.poisson.bootstrap, sm.regression, sm.regression.autocor, sm.rm, sm.script, sm.sphere, sm.survival, sm.ts.pdf, sm.weight, sm.weight2, sm.surface3d, h.select, sm.sigma, sm.sigma2.compare, sm.variogram, sm.discontinuity, sm.monotonicity, sm.pca # The following two lines should be commented out for CRAN release. # sm, s, plot.pam, anova.pam, summary.pam, predict.pam, # fitted.pam, residuals.pam ) # These lines should be commented out for CRAN release. # S3method("plot", "pam") # S3method("anova", "pam") # S3method("summary", "pam") # S3method("predict", "pam") # S3method("fitted", "pam") # S3method("residuals", "pam") importFrom("grDevices", "chull", "col2rgb", "contourLines", "rainbow", "rgb", "topo.colors") importFrom("graphics", "abline", "axis", "box", "contour", "filled.contour", "image", "lines", "par", "persp", "plot", "points", "polygon", "rect", "rug", "segments", "text", "title") importFrom("stats", "acf", "approxfun", "as.formula", "binomial", "df", "dnorm", "fitted", "glm.fit", "model.matrix", "na.omit", "nobs", "optim", "optimise", "pchisq", "poisson", "poly", "qchisq", "quantile", "rbeta", "rbinom", "residuals", "rgamma", "rpois", "sd", "terms", "uniroot", "var", "pf") importFrom("utils", "menu") # import(tcltk) # import(tkrplot) # import(rpanel) # importFrom("rpanel", "rp.plot3d", "rp.do", "rp.tkrplot", "rp.tkrreplot", # "rp.radiogroup", "rp.slider", "rp.checkbox", "rp.control") # importFrom("tkrplot", "tkrplot", "tkrreplot") sm/ChangeLog0000644000176200001440000000560714564135351012462 0ustar liggesusersVersion 2.2-6.0 2024-02-17 Minor bug fixes. Version 2.2-5.7 2021-09-13 Minor bug fixes. Package can also be installed when the tcltk package is not available. Version 2.2-5.6 2018-09-27 Minor bug fixes. Version 2.2-5.5 2018-05-06 Minor bug fixes. Version 2.2-5.4 2014-01-16 Addition of sm.pca function. Extension of sm.variogram function. Other minor bug fixes. Version 2.2-5.3 2013-05-11 Addition of lwd parameter to sm.regression. All datasets given their own help files. Minor corrections to help files and sm.options. Version 2.2-4 2010-02-26 sm.ancova returns the boundaries of the reference band. Minor corrections to sm.discontinuity, pause and help files. Version 2.2-3 2008-09-24 Minor corrections to citation(), density estimation in 2d and 3d when rpanel is used and sm.discontinuity. Periodic covariates allowed. Other small bug fixes. Version 2.2-2 2007-10-09 Removal of \non_function from geyser.Rd. Version 2.2-1 2007-09-22 Minor corrections to .onAttach and sm.options. Version 2.2-0 2007-09-12 Version 2.2 released. Version 2.1-0 2005-09-01 Version 2.1 released. Adrian Bowman takes over as maintainer. Version 2.0-14 2005-02-07 Improve messages, support translations Version 2.0-13 2004-11-11 Update sm.density.compare to pre-2.1 after bug report from Deepayan Sarkar. Scripts trwlgam[13] now work. Use "console" pager on Windows. Version 2.0-12 2004-09-04 sphimage used a[ind] <- b[ind] with NAs in ind. Added NAMESPACE. No longer use .sm.home, but system.file. Use package gam in scripts: trwlgam2 mackgam smackgam now work. Version 2.0-11 2004-08-04 One .Rd error, data -> smdata, remake INDEX Version 2.0-10 2004-07-29 Remove references to packages modreg and sm. Make provide_data more careful about where (as 'trees' duplicates a base dataset). Version 2.0-9 2003-12-18 Set seed for running scripts. Version 2.0-8 2003-09-12 Avoid 'nlevels' as var name. Version 2.0-7 2003-07-18 Documentation improvements. Version 2.0-6 2003-06-03 Avoid generating lty=NA, use PACKAGE=. Version 2.0-5 was unreleased. Version 2.0-4 2001/10/10 tree.q did not work in R, minor improvements to documentation. Version 2.0-3 2001/08/08 QA changes, e.g. T -> TRUE in scripts. Version 2.0-2 2001/06/12 Many help-file improvements. Version 2.0-1 2000/12/08 Version 2 of sm, sm.rm(optimize=TRUE) works. Version 1.0-3 1999/08/15 Moved .sm.home to package:sm, made ts examples work. Version 1.0-2 1999/04/02 Modified for 0.64, using R's chull, jitter, inst/* mechanism, data/00Index, file.* functions. Version 1.0-1 1999/02/20: sm/data/0000755000176200001440000000000014301463453011605 5ustar liggesuserssm/data/mildew.rda0000644000176200001440000000065013773656220013567 0ustar liggesusers r0b```b`gd`b2Y# 'fII-g``xUP#>.qtK-.50waӇOHG#n#C/d<[v'.wzXwZ\wjZBVB^B*b[vq1Dl _rzA:j3 pNcDm Kh9b _:̻p1q:;KB%`4$a@v+u$ꡇC0O)9 J~ Խ"sjsּb CTCJ ,#8*e &p)egY0'Df椠+9'. 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Different concentrations of potassium cyanate were applied to vials of trout eggs. The eggs in half of the vials were allowed to water-harden before the toxicant was applied. The variables are: \tabular{ll}{ \code{Concentr} \tab toxicant concentration \cr \code{Trouts} \tab number of trout eggs \cr \code{Dead} \tab number of eggs which died \cr \code{Insert} \tab an indicator of whether the eggs were allowed to water-harden } Source: O'Hara Hines & Carter (1993). Improved added variable and partial residual plots for the detection of influential observations in generalized linear models. Applied Statistics 42, 3-20. The data are also reported by Hand et al. (1994), A Handbook of Small Data Sets, data set no.418. } \keyword{smooth} \keyword{regression} sm/man/poles.Rd0000644000176200001440000000100413773656220013063 0ustar liggesusers\name{poles} \alias{poles} \title{Positions of the south pole} \description{ These data refer to positions of the south pole determined from the palaeomagnetic study of New Caledonian laterites. The variables are: \tabular{ll}{ \code{Latitude} \cr \code{Longitude} } The data were collected by Falvey and Musgrave. They are listed in Fisher, Lewis & Embleton (1987), Statistical Analysis of Spherical Data, Cambridge University Press, Cambridge, dataset B1. } \keyword{smooth} \keyword{regression} sm/man/hcv.Rd0000644000176200001440000000673713773656220012543 0ustar liggesusers\name{hcv} \alias{hcv} \title{ Cross-validatory choice of smoothing parameter } \description{ This function uses the technique of cross-validation to select a smoothing parameter suitable for constructing a density estimate or nonparametric regression curve in one or two dimensions. } \usage{ hcv(x, y = NA, hstart = NA, hend = NA, \dots) } \arguments{ \item{x}{ a vector, or two-column matrix of data. If \code{y} is missing these are observations to be used in the construction of a density estimate. If \code{y} is present, these are the covariate values for a nonparametric regression. } \item{y}{ a vector of response values for nonparametric regression. } \item{hstart}{ the smallest value of the grid points to be used in an initial grid search for the value of the smoothing parameter. } \item{hend}{ the largest value of the grid points to be used in an initial grid search for the value of the smoothing parameter. } \item{\dots}{ other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function. Those specifically relevant for this function are the following: \code{h.weights}, \code{ngrid}, \code{display}, \code{add}; see the documentation of \code{\link{sm.options}} for their description. }} \value{ the value of the smoothing parameter which minimises the cross-validation criterion over the selected grid. } \section{Side Effects}{ If the minimising value is located at the end of the grid of search positions, or if some values of the cross-validatory criterion cannot be evaluated, then a warning message is printed. In these circumstances altering the values of \code{hstart} and \code{hend} may improve performance. } \details{ See Sections 2.4 and 4.5 of the reference below. The two-dimensional case uses a smoothing parameter derived from a single value, scaled by the standard deviation of each component. This function does not employ a sophisticated algorithm and some adjustment of the search parameters may be required for different sets of data. An initial estimate of the value of h which minimises the cross-validatory criterion is located from a grid search using values which are equally spaced on a log scale between \code{hstart} and \code{hend}. A quadratic approximation is then used to refine this initial estimate. } \note{As from version 2.1 of the package, a similar effect can be obtained with the new function \code{h.select}, via \code{h.select(x, method="cv")}. Users are encouraged to adopt this route, since \code{hcv} might be not accessible directly in future releases of the package. When the sample size is large \code{hcv} uses the raw data while \code{h.select(x, method="cv")} uses binning. The latter is likely to produce a more stable choice for \code{h}. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis:} \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{h.select}}, \code{\link{hsj}}, \code{\link{hnorm}} } \examples{ # Density estimation x <- rnorm(50) par(mfrow=c(1,2)) h.cv <- hcv(x, display="lines", ngrid=32) sm.density(x, h=hcv(x)) par(mfrow=c(1,1)) # Nonparametric regression x <- seq(0, 1, length = 50) y <- rnorm(50, sin(2 * pi * x), 0.2) par(mfrow=c(1,2)) h.cv <- hcv(x, y, display="lines", ngrid=32) sm.regression(x, y, h=hcv(x, y)) par(mfrow=c(1,1)) } \keyword{nonparametric} \keyword{smooth} % Converted by Sd2Rd version 1.15. sm/man/follicle.Rd0000644000176200001440000000112113773656220013532 0ustar liggesusers\name{follicle} \alias{follicle} \title{Ovarian follicle counts} \description{ These data record the number of ovarian follicles, on a log scale, counted from sectioned ovaries of women of various ages. The variables are: \tabular{ll}{ \code{Age} \tab age of the woman \cr \code{Count} \tab follicle count \cr \code{Source} \tab an indicator of the source of the data } The data were reported by Block (1952; 1953), Richardson et al. (1987) and A Gougeon. They are analysed by Faddy & Gosden (1996) and Faddy & Jones (1997). } \keyword{smooth} \keyword{regression} sm/man/sm.discontinuity.Rd0000644000176200001440000001253014265557440015273 0ustar liggesusers\name{sm.discontinuity} \alias{sm.discontinuity} \title{The detection of discontinuities in a regression curve or surface. } \description{ This function uses a comparison of left and right handed nonparametric regression curves to assess the evidence for the presence of one or more discontinuities in a regression curve or surface. A hypothesis test is carried out, under the assumption that the errors in the data are approximately normally distributed. A graphical indication of the locations where the evidence for a discontinuity is strongest is also available. } \usage{ sm.discontinuity(x, y, h, hd, \dots) } \arguments{ \item{x}{ a vector or two-column matrix of covariate values. } \item{y}{ a vector of responses observed at the covariate locations. } \item{h}{ a smoothing parameter to be used in the construction of the nonparametric regression estimates. A normal kernel function is used and \code{h} is its standard deviation(s). However, if this argument is omitted \code{h} will be selected by an approximate degrees of freedom criterion, controlled by the \code{df} parameter. See \code{sm.options} for details. } \item{hd}{ a smoothing parameter to be used in smoothing the differences of the left and right sided nonparametric regression estimates. A normal kernel function is used and \code{hd} is its standard deviation(s). However, if this argument is omitted \code{hd} will be set to \code{h * sqrt(0.25)}, and \code{h} reset to \code{h * sqrt(0.75)}, when \code{x} is a vector When \code{x} is a matrix, \code{hd} will be set to \code{h * sqrt(0.5)} and \code{h} will be reset to the same value. } \item{\dots}{ other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function; those relevant for this function are \code{add}, \code{eval.points}, \code{ngrid}, \code{se}, \code{band}, \code{xlab}, \code{ylab}, \code{xlim}, \code{ylim}, \code{lty}, \code{col}; see the documentation of \code{\link{sm.options}} for their description. }} \value{ a list containing the following items \item{p}{the p-value for the test of the null hypothesis that no discontinuities are present.} \item{sigma}{the estimated standard deviation of the errors.} \item{eval.points}{the evaluation points of the nonparametric regression estimates. When \code{x} is a matrix, \code{eval.points} is also a matrix whose columns define the evaluation grid of each margin of the evaluation rectangle.} \item{st.diff}{a vector or matrix of standardised differences between the left and right sided estimators at the evaluation points.} \item{diffmat}{when \code{x} is a vector, this contains the locations and standardised differences where the latter are greater than 2.5.} \item{angle}{when \code{x} is a matrix, this contains the estimated angles at which the standardised differences were constructed.} \item{h}{the principal smoothing parameter.} \item{hd}{the smoothing parameter used for double-smoothing (see the reference below).} } \section{Side Effects}{ a plot on the current graphical device is produced, unless the option \code{display="none"} is set. } \details{ The reference below describes the statistical methods used in the function. There are minor differences in some computational details of the implementation. Currently duplicated rows of \code{x} cause a difficulty in the two covariate case. Duplicated rows should be removed. } \references{ Bowman, A.W., Pope, A. and Ismail, B. (2006). Detecting discontinuities in nonparametric regression curves and surfaces. \emph{Statistics & Computing}, 16, 377--390. } \seealso{ \code{\link{sm.regression}}, \code{\link{sm.options}} } \examples{ par(mfrow = c(3, 2)) with(nile, { sm.discontinuity(Year, Volume, hd = 0) sm.discontinuity(Year, Volume) ind <- (Year > 1898) plot(Year, Volume) h <- h.select(Year, Volume) sm.regression(Year[!ind], Volume[!ind], h, add = TRUE) sm.regression(Year[ ind], Volume[ ind], h, add = TRUE) hvec <- 1:15 p <- numeric(0) for (h in hvec) { result <- sm.discontinuity(Year, Volume, h, display = "none", verbose = 0) p <- c(p, result$p) } plot(hvec, p, type = "l", ylim = c(0, max(p)), xlab = "h") lines(range(hvec), c(0.05, 0.05), lty = 2) }) with(trawl, { Position <- cbind(Longitude, Latitude) ind <- (Longitude < 143.8) # Remove a repeated point which causes difficulty with sm.discontinuity ind[54] <- FALSE sm.regression(Position[ind,], Score1[ind], theta = 35, phi = 30) sm.discontinuity(Position[ind,], Score1[ind], col = "blue") }) par(mfrow = c(1, 1)) # The following example takes longer to run. # Alternative values for nside are 32 and 64. # Alternative values of yjump are 1 and 0.5. # nside <- 16 # yjump <- 2 # x1 <- seq(0, 1, length = nside) # x2 <- seq(0, 1, length = nside) # x <- expand.grid(x1, x2) # x <- cbind(x1 = x[, 1], x2 = x[, 2]) # y <- rnorm(nside * nside) # ind <- (sqrt((x[, 1] - 0.5)^2 + (x[, 2] - 0.5)^2) <= 0.25) # y[ind] <- y[ind] + yjump # image(x1, x2, matrix(y, ncol = nside)) # sm.discontinuity(x, y, df = 20, add = TRUE) } \keyword{smooth} \keyword{regression} sm/man/sm.poisson.bootstrap.Rd0000644000176200001440000000435714002272153016064 0ustar liggesusers\name{sm.poisson.bootstrap} \alias{sm.poisson.bootstrap} \title{ Bootstrap goodness-of-fit test for a Poisson regression model } \description{ This function is associated with \code{sm.poisson} for the underlying fitting procedure. It performs a Pseudo-Likelihood Ratio Test for the goodness-of-fit of a standard parametric Poisson regression of specified \code{degree} in the covariate \code{x}. } \usage{ sm.poisson.bootstrap(x, y, h, degree = 1, fixed.disp = FALSE, intercept = TRUE, ...) } \arguments{ \item{x}{ vector of the covariate values } \item{y}{ vector of the response values; they must be nonnegative integers. } \item{h}{ the smoothing parameter; it must be positive. } \item{degree}{ specifies the degree of the fitted polynomial in \code{x} on the logarithm scale (default=1). } \item{fixed.disp}{if \code{TRUE}, the dispersion parameter is kept at value 1 across the simulated samples, otherwise the dispersion parameter estimated from the sample is used to generate samples with that dispersion parameter (default=\code{FALSE}). } \item{intercept}{\code{TRUE} (default) if an intercept is to be included in the fitted model.} \item{\dots}{ additional parameters passed to \code{\link{sm.poisson}}. }} \value{ a list containing the observed value of the Pseudo-Likelihood Ratio Test statistic, its observed p-value as estimated via the bootstrap method, and the vector of estimated dispersion parameters when this value is not forced to be 1. } \section{Side Effects}{ Graphical output representing the bootstrap samples is produced on the current graphical device. The estimated dispersion parameter, the value of the test statistic and the observed significance level are printed. } \details{ see Section 5.4 of the reference below. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis:} \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{sm.poisson}}, \code{\link{sm.binomial.bootstrap}} } \examples{ ## takes a while: extend sm.script(muscle) with(muscle, { TypeI <- TypeI.P + TypeI.R + TypeI.B sm.poisson.bootstrap(log(TypeI), TypeII, h = 0.5) }) } \keyword{nonparametric} \keyword{smooth} \keyword{htest} \keyword{models} sm/man/sm.binomial.Rd0000644000176200001440000000472213773656220014163 0ustar liggesusers\name{sm.binomial} \alias{sm.binomial} \title{ Nonparametric logistic regression } \description{ This function estimates the regression curve using the local likelihood approach for a vector of binomial observations and an associated vector of covariate values. } \usage{ sm.binomial(x, y, N = rep(1, length(y)), h, \dots) } \arguments{ \item{x}{ vector of the covariate values } \item{y}{ vector of the response values; they must be nonnegative integers not larger than those of \code{N}. } \item{h}{ the smoothing parameter; it must be positive. } \item{N}{ a vector containing the binomial denominators. If missing, it is assumed to contain all 1's. } \item{\dots}{ other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function; those relevant for this function are the following: \code{add}, \code{col}, \code{display}, \code{eval.points}, \code{nbins}, \code{ngrid}, \code{pch}, \code{xlab}, \code{ylab}; see the documentation of \code{\link{sm.options}} for their description. }} \value{ A list containing vectors with the evaluation points, the corresponding probability estimates, the linear predictors, the upper and lower points of the variability bands (on the probability scale) and the standard errors on the linear predictor scale. } \section{Side Effects}{ graphical output will be produced, depending on the value of the \code{display} parameter. } \details{ see Sections 3.4 and 5.4 of the reference below. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis:} \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{sm.binomial.bootstrap}}, \code{\link{sm.poisson}}, \code{\link{sm.options}}, \code{\link{glm}}, \code{\link{binning}} } \examples{\dontrun{ # the next example assumes that all binomial denominators are 1's sm.binomial(dose, failure, h=0.5) # in the next example, (some of) the dose levels are replicated sm.binomial(dose, failure, n.trials, h=0.5) } with(birth, { sm.binomial(Lwt[Smoke=="S"], Low[Smoke=="S"], h=20, xlab='mother weight[Smoke=="S"]') x<- seq(0,1,length=30) y<- rbinom(30,10,prob=2*sin(x)/(1+x)) sm.binomial(x,y,N=rep(10,30), h=0.25) }) } \keyword{nonparametric} \keyword{smooth} \keyword{models} % Converted by Sd2Rd version 1.15. sm/man/sm.sigma.Rd0000644000176200001440000000551514265557541013475 0ustar liggesusers\name{sm.sigma} \alias{sm.sigma} \title{Estimation of the error standard deviation in nonparametric regression.} \description{This function estimates the error standard deviation in nonparametric regression with one or two covariates.} \usage{sm.sigma(x, y, rawdata = NA, weights = rep(1, length(y)), diff.ord = 2, ci = FALSE, model = "none", h = NA, \dots) } \arguments{ \item{x}{a vector or two-column matrix of covariate values.} \item{y}{a vector of responses.} \item{rawdata}{a list containing the output from a binning operation. This argument is used by \code{sm.regression} and it need not be set for direct calls of the function.} \item{weights}{a list of frequencies associated with binned data. This argument is used by \code{sm.regression} and it need not be set for direct calls of the function.} \item{diff.ord}{an integer value which determines first (1) or second (2) differencing in the estimation of sigma.} \item{ci}{a logical value which controls whether a confidence interval is produced.} \item{model}{a character variable. If this is set to \code{"constant"} then a test of constant variance over the covariates is performed (only in the case of two covariates)} \item{h}{a vector of length two defining a smoothing parameter to be used in the test of constant variance.} \item{\dots}{other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function; the only one relevant for this function is \code{nbins}.} } \value{a list containing the estimate and, in the two covariate case, a matrix which can be used by the function \code{sm.sigma2.compare}, pseudo-residuals and, if appropriate, a confidence interval and a p-value for the test of constant variance.} \section{Side Effects}{none.} \details{see the reference below.} \references{Bock, M., Bowman, A.W. & Ismail, B. (2007). Estimation and inference for error variance in bivariate nonparametric regression. \emph{Statistics & Computing}, to appear.} \seealso{\code{\link{sm.sigma2.compare}}} \examples{ \dontrun{ with(airquality, { x <- cbind(Wind, Temp) y <- Ozone^(1/3) group <- (Solar.R < 200) sig1 <- sm.sigma(x[ group, ], y[ group], ci = TRUE) sig2 <- sm.sigma(x[!group, ], y[!group], ci = TRUE) print(c(sig1$estimate, sig1$ci)) print(c(sig2$estimate, sig2$ci)) print(sm.sigma(x[ group, ], y[ group], model = "constant", h = c(3, 5))$p) print(sm.sigma(x[!group, ], y[!group], model = "constant", h = c(3, 5))$p) print(sm.sigma2.compare(x[group, ], y[group], x[!group, ], y[!group])) }) }} \keyword{nonparametric} \keyword{smooth} sm/man/sm.binomial.bootstrap.Rd0000644000176200001440000000431613773656220016176 0ustar liggesusers\name{sm.binomial.bootstrap} \alias{sm.binomial.bootstrap} \title{ Bootstrap goodness-of-fit test for a logistic regression model. } \description{ This function is associated with \code{sm.binomial} for the underlying fitting procedure. It performs a Pseudo-Likelihood Ratio Test for the goodness-of-fit of a standard parametric logistic regression of specified \code{degree} in the covariate \code{x}. } \usage{ sm.binomial.bootstrap(x, y, N = rep(1, length(x)), h, degree = 1, fixed.disp=FALSE, ...) } \arguments{ \item{x}{ vector of the covariate values } \item{y}{ vector of the response values; they must be nonnegative integers. } \item{h}{ the smoothing parameter; it must be positive. } \item{N}{ a vector containing the binomial denominators. If missing, it is assumed to contain all 1's. } \item{degree}{ specifies the degree of the fitted polynomial in \code{x} on the logit scale (default=1).} \item{fixed.disp}{if \code{TRUE}, the dispersion parameter is kept at value 1 across the simulated samples, otherwise the dispersion parameter estimated from the sample is used to generate samples with that dispersion parameter (default=\code{FALSE}). } \item{\dots}{ additional parameters passed to \code{\link{sm.binomial}}. }} \value{ a list containing the observed value of the Pseudo-Likelihood Ratio Test statistic, its observed p-value as estimated via the bootstrap method, and the vector of estimated dispersion parameters when this value is not forced to be 1. } \section{Side Effects}{ Graphical output representing the bootstrap samples is produced on the current graphical device. The estimated dispersion parameter, the value of the test statistic and the observed significance level are printed. } \details{ see Section 5.4 of the reference below. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis: } \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{sm.binomial}}, \code{\link{sm.poisson.bootstrap}} } \examples{ \dontrun{sm.binomial.bootstrap(concentration, dead, N, 0.5, nboot=50)} } \keyword{nonparametric} \keyword{smooth} \keyword{htest} \keyword{models} % Converted by Sd2Rd version 1.15. sm/man/mackerel.Rd0000644000176200001440000000141113773656220013526 0ustar liggesusers\name{mackerel} \alias{mackerel} \title{The abundance of mackerel eggs} \description{ These data record the abundance of mackerel eggs off the coast of north-western Europe, from a multi-country survey in 1992. The variables are: \tabular{ll}{ \code{Density} \tab egg density \cr \code{mack.lat} \tab latitude of sampling position \cr \code{mack.long} \tab longitude of sampling position \cr \code{mack.depth} \tab bottom depth \cr \code{Temperature} \tab surface temperature \cr \code{Salinity} \tab salinity } Background to the survey and the data are provided by Watson et al. (1992), Priede and Watson (1993) and Priede et al (1995). Borchers et al (1997) describe an analysis of the data. } \keyword{smooth} \keyword{regression} sm/man/sm.density.compare.Rd0000644000176200001440000000643614116124302015460 0ustar liggesusers\name{sm.density.compare} \alias{sm.density.compare} \title{ Comparison of univariate density estimates } \description{ This function allows a set of univariate density estimates to be compared, both graphically and formally in a permutation test of equality. } \usage{ sm.density.compare(x, group, h, model = "none", \dots) } \arguments{ \item{x}{ a vector of data. } \item{group}{ a vector of group labels. If this is not already a factor it will be converted to a factor. } \item{h}{ the smoothing parameter to be used in the construction of each density estimate. Notice that the same smoothing parameter is used for each group. If this value is omitted, the mean of the normal optimal values for the different groups is used. } \item{model}{ the default value is \code{"none"} which restricts comparison to plotting only. The alternative value \code{"equal"} can produce a bootstrap hypothesis test of equality and the display of an appropriate reference band. } \item{...}{ other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function. Relevant parameters for this function are: \code{method}, \code{df}, \code{band}, \code{test}, \code{nboot}, plus those controlling graphical display (unless \code{display = "none"} is set) such as \code{col}, \code{col.band}, \code{lty} and \code{lwd}; see the documentation of \code{\link{sm.options}} for their description. The parameter \code{nboot} controls the number of permutations used in the permutation test. }} \value{ A list is returned containing: \item{estimate}{a matrix whose rows contain the estimates for each group.} \item{eval.points}{the grid of common evaluation points for the estimates.} \item{h}{the common smoothing parameter used in the construction of the estimates.} \item{levels}{the levels of the group factor.} \item{col, lty, lwd}{plotting details which can be useful in constructing a legend for the plot; see the examples below.} When \code{"model"} is set to \code{"equal"}, the output list also contains the p-value (\code{p}) of the test. When \code{band = TRUE}, and there are only two groups to compare, the output list also contains the upper (\code{upper}) and lower (\code{lower}) edges of the reference band for equality. } \section{Side Effects}{ a plot on the current graphical device is produced, unless \code{display="none"}. } \details{ For a general description of the methods involved, see Section 6.2 of the reference below. The colours and linetypes of the density estimates are set by \code{col} and \code{lty} which can be passed as additional arguments. By default these are set to \code{1 + 1:ngroup}, where \code{ngroup} is the number of groups represented in the \code{group} variable. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{sm.density}}, \code{\link{sm.ancova}}, \code{\link{sm.options}} } \examples{ y <- rnorm(100) g <- rep(1:2, rep(50,2)) sm.density.compare(y, g) comp <- sm.density.compare(y, g, model = "equal") legend("topleft", comp$levels, col = comp$col, lty = comp$lty, lwd = comp$lwd) } \keyword{nonparametric} \keyword{smooth} % Converted by Sd2Rd version 1.15. sm/man/citrate.Rd0000644000176200001440000000170314116472317013375 0ustar liggesusers\name{citrate} \alias{citrate} \title{The relationship between plasma citrate and carbohydrate metabolites} \description{ These data were collected in an experiment to study the relationship between possible daily rhythms of plasma citrate and daily rhythms of carbohydrate metabolites during feeding with a citrate-poor diet. During the experiment, plasma citrate concentrations were determined for each of 10 subjects at 14 successive time points during the day. The measurements covered the period 8a.m. to 9p.m. at hourly intervals. Meals were given at 8a.m., noon and 5p.m. The variables are denoted by \code{C08}, ..., \code{C21} and refer to plasma citrate measurements at the indicated hours. Anderson,A.H., Jensen,E.B. & Schou,G.(1981). Two-way analysis of variance with correlated errors. Int.Stat.Rev. 49,153-67. The data were taken from a report by T.T.Nielsen, N.S.Sorensen and E.B.Jensen. } \keyword{smooth} \keyword{regression} sm/man/radioc.Rd0000644000176200001440000000117014116471445013202 0ustar liggesusers\name{radioc} \alias{radioc} \title{Radiocarbon in Irish oak} \description{ These data record high precision measurements of radiocarbon on Irish oak, used to construct a calibration curve. The variables are: \tabular{ll}{ \code{Rc.age} \tab age predicted from the radiocarbon dating process \cr \code{Precision} \tab a measure of precision of the dating process \cr \code{Cal.age} \tab true calendar age } Pearson & Qua (1993). High precision 14C measurement of Irish oaks to show the natural 14C variations from AD 1840 - 5000 BC: a correction. Radiocarbon 35, 105-123. } \keyword{smooth} \keyword{regression} sm/man/geys3d.Rd0000644000176200001440000000157013773656220013147 0ustar liggesusers\name{geys3d} \alias{geys3d} \title{Duration and the time between eruptions for the Old Faithful Geyser} \description{ These data document the duration of eruptions, and the time between eruptions, for the Old Faithful Geyser in Yellowstone National Park. The variables are: \tabular{ll}{ \code{Waiting} \tab the waiting time before each eruption (minutes) \cr \code{Next.waiting} \tab the waiting time following each eruption (minutes) \cr \code{Duration} \tab the length of an eruption ( minutes) } The data were collected by by the Park Geologist, R.A.Hutchinson. An earlier set of data is reported in Weisberg (1990), Applied Linear Regression, Wiley, New York. The later set, used here, was reported by Azzalini & Bowman (1990), "A look at some data on the Old Faithful Geyser", Applied Statistics 39, 357-65. } \keyword{smooth} \keyword{regression} sm/man/wonions.Rd0000644000176200001440000000130314116471502013425 0ustar liggesusers\name{wonions} \alias{wonions} \title{Yield-density relationship for White Imperial Spanish onion plants} \description{ These data were collected in a study of the relationship between the yield of White Imperial Spanish onion plants and the density of planting. The variables are: \tabular{ll}{ \code{Density} \tab density of planting (plants/m^2) \cr \code{Yield} \tab yield (g/plant) \cr \code{Locality} \tab a code to indicate Purnong Landing (1) or Virginia (2) } The data were collected by I.S.Rogers (South Australian Dept. of Agriculture & Fisheries). They are listed in Ratkowsky (1983), Nonlinear Regression Modeling. Dekker, New York. } \keyword{smooth} \keyword{regression} sm/man/hnorm.Rd0000644000176200001440000000312013773656220013065 0ustar liggesusers\name{hnorm} \alias{hnorm} \title{ Normal optimal choice of smoothing parameter in density estimation } \description{ This functions evaluates the smoothing parameter which is asymptotically optimal for estimating a density function when the underlying distribution is Normal. Data in one, two or three dimensions can be handled. } \usage{ hnorm(x, weights) } \arguments{ \item{x}{ a vector, or matrix with two or three columns, containing the data. } \item{weights}{ an optional vector of integer values which allows the kernel functions over the observations to take different weights when they are averaged to produce a density estimate. This is useful, in particular, for censored data and to construct an estimate from binned data. }} \value{ the value of the asymptotically optimal smoothing parameter for Normal case. } \details{ See Section 2.4.2 of the reference below. } \note{As from version 2.1 of the package, a similar effect can be obtained with the new function \code{h.select}, via \code{h.select(x, method="normal", weights=weights)} or simply \code{h.select(x)}. Users are encouraged to adopt this route, since \code{hnorm} might be not accessible directly in future releases of the package. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis: } \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{h.select}}, \code{\link{hcv}}, \code{\link{hsj}} } \examples{ x <- rnorm(50) hnorm(x) } \keyword{nonparametric} \keyword{smooth} % Converted by Sd2Rd version 1.15. sm/man/sm.ancova.Rd0000644000176200001440000000721514265557374013647 0ustar liggesusers\name{sm.ancova} \alias{sm.ancova} \title{ Nonparametric analysis of covariance } \description{ This function allows a set of nonparametric regression curves to be compared, both graphically and formally in a hypothesis test. A reference model, used to define the null hypothesis, may be either equality or parallelism. Regression surfaces can also be compared in a test but a graphical display is not produced. } \usage{ sm.ancova(x, y, group, h, model = "none", h.alpha = NA, weights=NA, covar = diag(1/weights), ...) } \arguments{ \item{x}{ a vector or two-column matrix of covariate values. } \item{y}{ a vector of response values. } \item{group}{ a vector of group indicators. } \item{h}{ the smoothing parameter to be used in the construction of each of the regression curves. If this is missing the method of smoothing parameter selection specified by \code{sm.options} will be applied. } \item{model}{ a character variable which defines the reference model. The values \code{"none"}, \code{"equal"} and \code{"parallel"} are possible. } \item{h.alpha}{ the value of the smoothing parameter used when estimating the vertical separations of the curves under the parallelism model. If this is missing, it is set to 2 * r / n, where r denotes the range of the data and n the sample size. } \item{weights}{ case weights; see the documentation of \code{\link{sm.regression}} for a full description. } \item{covar}{ the (estimated) covariance matrix of y. The default value assumes the data to be independent. Where appropriate, the covariance structure of \code{y} can be estimated by the user, externally to \code{sm.ancova}, and passed through this argument. This is used in the hypothesis tests but not in the construction of the reference band for comparing two groups (and so graphics are disabled in this case). } \item{\dots}{ other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function. Those relevant for this function are the following: \code{display}, \code{ngrid}, \code{eval.points}, \code{xlab}, \code{ylab}; see the documentation of \code{\link{sm.options}} for their description. }} \value{ a list containing an estimate of the error standard deviation and, where appropriate, a p-value and reference model. If the parallelism model has been selected then a vector of estimates of the vertical separations of the underlying regression curves is also returned. If a reference band has been requested, the upper and lower boundaries and their common evaluation points are also returned. } \section{Side Effects}{ a plot on the current graphical device is produced, unless \code{display="none"} } \details{ see Sections 6.4 and 6.5 of the book by Bowman & Azzalini, and the papers by Young & Bowman listed below. This function is a developed version of code originally written by Stuart Young. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis: } \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. Young, S.G. and Bowman, A.W. (1995). Nonparametric analysis of covariance. \emph{Biometrics} \bold{51}, 920--931. Bowman, A.W. and Young, S.G. (1996). Graphical comparison of nonparametric curves. \emph{Applied Statistics} \bold{45}, 83--98. } \seealso{ \code{\link{sm.regression}}, \code{\link{sm.density.compare}}, \code{\link{sm.options}} } \examples{ x <- runif(50, 0, 1) y <- 4*sin(6*x) + rnorm(50) g <- rbinom(50, 1, 0.5) sm.ancova(x, y, g, h = 0.15, model = "equal") } \keyword{nonparametric} \keyword{smooth} % Converted by Sd2Rd version 1.15. sm/man/mildew.Rd0000644000176200001440000000141413773656220013227 0ustar liggesusers\name{mildew} \alias{mildew} \title{Mildew control} \description{ The data refer to study of mildew control sponsored by Bainbridge, Jenkyn and Dyke at Rothamsted Experimental Station. There were four treatments, one of which was a control. There were 36 adjacent plots, with an extra plot at each end. Nine blocks were created by grouping the plots in fours. The variables are: \tabular{ll}{ \code{t1, t2, t3} \tab indicators of the four treatment groups \cr \code{p1, ..., p8} \tab indicators of the nine blocks\cr \code{Yield} \tab tons of grain per hectare } Draper & Guttman (1980). Incorporating overlap effects from neighbouring units into response surface models. Applied Statistics 29, 128-134. } \keyword{smooth} \keyword{regression} sm/man/sm.survival.Rd0000644000176200001440000000510213773656220014235 0ustar liggesusers\name{sm.survival} \alias{sm.survival} \title{ Nonparametric regression with survival data. } \description{ This function creates a smooth, nonparametric estimate of the quantile of the distribution of survival data as a function of a single covariate. A weighted product-limit estimate of the survivor function is obtained by smoothing across the covariate scale. A small amount of smoothing is then also applied across the survival time scale in order to achieve a smooth estimate of the quantile. } \usage{ sm.survival(x, y, status, h , hv = 0.05, p = 0.5, status.code = 1, \dots) } \arguments{ \item{x}{ a vector of covariate values. } \item{y}{ a vector of survival times. } \item{status}{ an indicator of a complete survival time or a censored value. The value of \code{status.code} defines a complete survival time. } \item{h}{ the smoothing parameter applied to the covariate scale. A normal kernel function is used and \code{h} is its standard deviation. } \item{hv}{ a smoothing parameter applied to the weighted to the product-limit estimate derived from the smoothing procedure in the covariate scale. This ensures that a smooth estimate is obtained. } \item{p}{ the quantile to be estimated at each covariate value. } \item{status.code}{ the value of \code{status} which defines a complete survival time. } \item{\dots}{ other optional parameters are passed to the \code{sm.options} function, through a mechanism which limits their effect only to this call of the function; those relevant for this function are \code{add}, \code{eval.points}, \code{ngrid}, \code{display}, \code{xlab}, \code{ylab}, \code{lty}; see the documentation of \code{\link{sm.options}} for their description. }} \value{ a list containing the values of the estimate at the evaluation points and the values of the smoothing parameters for the covariate and survival time scales. } \section{Side Effects}{ a plot on the current graphical device is produced, unless the option \code{display="none"} is set. } \details{ see Section 3.5 of the reference below. } \references{ Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for Data Analysis:} \emph{the Kernel Approach with S-Plus Illustrations.} Oxford University Press, Oxford. } \seealso{ \code{\link{sm.regression}}, \code{\link{sm.options}} } \examples{ x <- runif(50, 0, 10) y <- rexp(50, 2) z <- rexp(50, 1) status <- rep(1, 50) status[z