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Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("locfit-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('locfit')
Locfit for R.
August 3, 2000.  (Updated for R 1.7.0, March 21, 2003)

Attaching package: 'locfit'


	The following object(s) are masked from package:stats :

	 knots 

> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "aicplot"
> 
> ### * aicplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: aicplot
> ### Title: Compute an AIC plot.
> ### Aliases: aicplot
> ### Keywords: smooth hplot
> 
> ### ** Examples
> 
> data(morths)
> plot(aicplot(deaths~age,weights=n,data=morths,family="binomial",alpha=seq(0.2,1.0,by=0.05)))
> 
> 
> 
> cleanEx(); ..nameEx <- "ang"
> 
> ### * ang
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ang
> ### Title: Angular Term for a Locfit model.
> ### Aliases: ang
> ### Keywords: models
> 
> ### ** Examples
> 
> # generate an x variable, and a response with period 0.2
> x <- seq(0, 1, length=200)
> y <- sin(10*pi*x) + rnorm(200) / 5
> 
> # compute the periodic local fit. Note the scale argument is period/(2pi)
> fit <- locfit(y ~ ang(x), scale=0.2/(2*pi))
> 
> # plot the fit over a single period
> plot(fit)
> 
> # plot the fit over the full range of the data
> plot(fit, xlim=c(0,1))
> 
> 
> 
> cleanEx(); ..nameEx <- "cpar"
> 
> ### * cpar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cpar
> ### Title: Conditionally parametric term for a Locfit model.
> ### Aliases: cpar
> ### Keywords: models
> 
> ### ** Examples
> 
> data(ethanol)
> # fit a conditionally parametric model
> fit <- locfit(NOx~E+cpar(C),data=ethanol)
> plot(fit)
> # one way to force a parametric fit with locfit
> fit <- locfit(NOx~cpar(E),data=ethanol)
> 
> 
> 
> cleanEx(); ..nameEx <- "cpplot"
> 
> ### * cpplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cpplot
> ### Title: Compute a Cp plot.
> ### Aliases: cpplot
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> plot(cpplot(NOx~E,data=ethanol,alpha=seq(0.2,1.0,by=0.05)))
> 
> 
> 
> cleanEx(); ..nameEx <- "crit"
> 
> ### * crit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: crit
> ### Title: Compute critical values for confidence intervals.
> ### Aliases: crit
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # compute and plot 99% confidence intervals, with local variance estimate.
> data(ethanol)
> fit <- locfit(NOx~E,data=ethanol)
> crit(fit) <- crit(fit,cov=0.99)
> plot(fit,band="local")
> 
> # compute and plot 99% simultaneous bands
> crit(fit) <- kappa0(NOx~E,data=ethanol,cov=0.99)
Warning: constants are approximate for varying h
> plot(fit,band="local")
> 
> 
> 
> cleanEx(); ..nameEx <- "crita"
> 
> ### * crita
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: crita
> ### Title: Substitute critical values on ``locfit'' object.
> ### Aliases: crit<-
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # compute and plot 99% confidence intervals, with local variance estimate.
> data(ethanol)
> fit <- locfit(NOx~E,data=ethanol)
> crit(fit) <- crit(fit,cov=0.99)
> plot(fit,band="local")
> 
> # compute and plot 99% simultaneous bands
> crit(fit) <- kappa0(NOx~E,data=ethanol,cov=0.99)
Warning: constants are approximate for varying h
> plot(fit, band="local")
> 
> 
> 
> cleanEx(); ..nameEx <- "density.lf"
> 
> ### * density.lf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: density.lf
> ### Title: Density estimation using Locfit
> ### Aliases: density.lf
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(geyser)
> density.lf(geyser, window="tria")
$x
 [1] 1.564723 1.635550 1.706378 1.777206 1.848033 1.918861 1.989689 2.060516
 [9] 2.131344 2.202172 2.272999 2.343827 2.414654 2.485482 2.556310 2.627137
[17] 2.697965 2.768793 2.839620 2.910448 2.981276 3.052103 3.122931 3.193759
[25] 3.264586 3.335414 3.406241 3.477069 3.547897 3.618724 3.689552 3.760380
[33] 3.831207 3.902035 3.972863 4.043690 4.114518 4.185346 4.256173 4.327001
[41] 4.397828 4.468656 4.539484 4.610311 4.681139 4.751967 4.822794 4.893622
[49] 4.964450 5.035277

$y
 [1] 1.182697e-16 1.890007e-01 4.623314e-01 7.174016e-01 8.081279e-01
 [6] 5.571365e-01 3.739426e-01 1.256785e-01 3.316671e-03 8.002072e-02
[11] 1.963319e-01 1.132892e-01 3.419681e-02 7.653125e-02 4.129095e-02
[16] 1.046841e-02 7.019261e-02 4.762960e-02 2.512419e-02 1.445726e-01
[21] 9.107183e-02 4.838509e-02 9.322311e-02 9.322311e-02 6.792613e-02
[26] 1.022381e-01 1.911011e-01 3.630067e-01 3.338104e-01 2.442562e-01
[31] 5.441002e-01 5.917874e-01 3.663234e-01 3.868570e-01 6.537848e-01
[36] 8.292375e-01 6.788447e-01 4.238963e-01 4.480279e-01 4.545461e-01
[41] 4.034270e-01 3.677768e-01 5.616119e-01 7.364176e-01 3.963838e-01
[46] 1.409679e-01 9.322311e-02 9.322311e-02 5.972420e-02 5.913483e-17

> # the same result with density, except less precision.
> density(geyser, window="tria")

Call:
	density(x = geyser, window = "tria")

Data: geyser (107 obs.);	Bandwidth 'bw' = 0.3677

       x                y             
 Min.   :0.5668   Min.   :-1.711e-17  
 1st Qu.:1.9334   1st Qu.: 6.050e-02  
 Median :3.3000   Median : 1.560e-01  
 Mean   :3.3000   Mean   : 1.828e-01  
 3rd Qu.:4.6666   3rd Qu.: 2.568e-01  
 Max.   :6.0332   Max.   : 4.834e-01  
> 
> 
> 
> cleanEx(); ..nameEx <- "gcvplot"
> 
> ### * gcvplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gcvplot
> ### Title: Compute a generalized cross-validation plot.
> ### Aliases: gcvplot
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> plot(gcvplot(NOx~E,data=ethanol,alpha=seq(0.2,1.0,by=0.05)))
> 
> 
> 
> cleanEx(); ..nameEx <- "kappa0"
> 
> ### * kappa0
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kappa0
> ### Title: Critical Values for Simultaneous Confidence Bands.
> ### Aliases: kappa0
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # compute and plot simultaneous confidence bands
> data(ethanol)
> fit <- locfit(NOx~E,data=ethanol)
> crit(fit) <- kappa0(NOx~E,data=ethanol)
Warning: constants are approximate for varying h
> plot(fit,crit=crit,band="local")
Warning: parameter "crit" could not be set in high-level plot() function
Warning: parameter "crit" could not be set in high-level plot() function
Warning: parameter "crit" could not be set in high-level plot() function
Warning: parameter "crit" could not be set in high-level plot() function
Warning: parameter "crit" could not be set in high-level plot() function
Warning: parameter "crit" could not be set in high-level plot() function
> 
> 
> 
> cleanEx(); ..nameEx <- "km.mrl"
> 
> ### * km.mrl
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: km.mrl
> ### Title: Mean Residual Life using Kaplan-Meier estimate
> ### Aliases: km.mrl
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # censored regression using the Kaplan-Meier estimate.
> data(heart)
> fit <- locfit.censor(log10(surv+0.5)~age, cens=cens, data=heart, km=TRUE)
> plotbyfactor(heart$age, 0.5+heart$surv, heart$cens, ylim=c(0.5,16000), log="y")
> lines(fit, tr=function(x)10^x)
> 
> 
> 
> cleanEx(); ..nameEx <- "lcvplot"
> 
> ### * lcvplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lcvplot
> ### Title: Compute the likelihood cross-validation plot.
> ### Aliases: lcvplot
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> plot(lcvplot(NOx~E,data=ethanol,alpha=seq(0.2,1.0,by=0.05)))
> 
> 
> 
> cleanEx(); ..nameEx <- "left"
> 
> ### * left
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: left
> ### Title: One-sided left smooth for a Locfit model.
> ### Aliases: left
> ### Keywords: models
> 
> ### ** Examples
> 
> # compute left and right smooths
> data(penny)
> xev <- (1945:1988)+0.5
> fitl <- locfit(thickness~left(year), alpha=c(0,10), deg=1, ev=xev, data=penny)
> fitr <- locfit(thickness~right(year),alpha=c(0,10), deg=1, ev=xev, data=penny)
> # plot the squared difference, to show the change points.
> plot( xev, (predict(fitr,where="ev") - predict(fitl,where="ev"))^2 )
> 
> 
> 
> cleanEx(); ..nameEx <- "lf"
> 
> ### * lf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lf
> ### Title: Locfit term in Additive Model formula
> ### Aliases: lf
> ### Keywords: models
> 
> ### ** Examples
> 
> # fit an additive semiparametric model to the ethanol data.
> if (exists("gam")) {  # shut up R's checks
+ fit <- gam(NOx~lf(E)+C,data=ethanol)
+ plot(fit)
+ }
> 
> 
> 
> cleanEx(); ..nameEx <- "lfbas"
> 
> ### * lfbas
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lfbas
> ### Title: User-specified basis functions for Locfit.
> ### Aliases: lfbas
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # Specify a bivariate linear with interaction basis.
> data(ethanol)
> my.basis <- function(x,t)
+ {
+   u1 <- x[, 1] - t[1]
+   u2 <- x[, 2] - t[2]
+   cbind(1, u1, u2, u1 * u2)
+ }
> fit <- locfit(NOx~E+C, data=ethanol, scale=0, basis=my.basis)
> # With this basis, Locfit's standard interpolation and plot methods
> # should be reasonable.
> plot(fit,get.data=TRUE)
> 
> # Estimation of change points. This provides an alternative to using
> # left() and right(), and can easily be modified to detecting
> # a change in slopes or other parameters. Note that the first
> # component is the indicator of x>t, so the coefficient estimates
> # the size of the change, assuming the change occurs at t.
> data(penny)
> my.basis <- function(x,t) cbind(x>t,1,x-t)
> xev <- (1945:1988) + 0.5
> fit <- locfit(thickness~year, data=penny, alpha=c(0,10), ev=xev, basis=my.basis)
> # The plot will show peaks where change points are most likely.
> # in S4, S-Plus 5 etc,
> # plot(preplot(fit,where="fitp")^2, type="b") is an alternative.
> plot(xev, predict(fit,where="fitp")^2, type="b")
> 
> # Estimate the mean function using local linear regression, with
> # discontinuities at 1958.5 and 1974.5.
> # The basis functions must consist of the constant 1, the linear term
> # x-t, and indicator functions for two of the three sections.
> # Note the care taken to ensure my.basis(t,t) = c(1,0,0,0) for all t.
> my.basis <- function(x,t)
+ { ret <- NULL
+   if (t<1958.5) ret <- cbind(1, x>=1958.5, x>1974.5, x-t)
+   if (t>1974.5) ret <- cbind(1, x<=1974.5, x<1958.5, x-t)
+   if (is.null(ret))
+     ret <- cbind(1, x<1958.5, x>1974.5, x-t)
+   ret
+ }
> fit <- locfit(thickness~year, data=penny, alpha=c(0,10), ev=xev, basis=my.basis)
> plot(preplot(fit,where="fitp", get.data=TRUE))
> 
> 
> 
> cleanEx(); ..nameEx <- "locfit"
> 
> ### * locfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: locfit
> ### Title: Local Regression, Likelihood and Density Estimation.
> ### Aliases: locfit
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # fit and plot a univariate local regression
> data(ethanol)
> fit <- locfit(NOx~E,data=ethanol)
> plot(fit,get.data=TRUE)
> 
> # a bivariate local regression with smaller smoothing parameter
> fit <- locfit(NOx~E+C, data=ethanol, scale=0, alpha=0.5)
> plot(fit)
> 
> # density estimation
> data(geyser)
> fit <- locfit(~geyser, alpha=c(0.1,0.8))
> plot(fit,get.data=TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "locfit.censor"
> 
> ### * locfit.censor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: locfit.censor
> ### Title: Censored Local Regression
> ### Aliases: locfit.censor
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(heart)
> fit <- locfit.censor(log10(surv+0.5)~age, cens=cens, data=heart)
> plotbyfactor(heart$age, 0.5+heart$surv, heart$cens, ylim=c(0.5,16000), log="y")
> lines(fit, tr=function(x)10^x)
> 
> 
> 
> cleanEx(); ..nameEx <- "lscv"
> 
> ### * lscv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lscv
> ### Title: Least Squares Cross Validation Statistic.
> ### Aliases: lscv
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # approximate calculation for a kernel density estimate
> data(geyser)
> lscv(~geyser, alpha=cbind(0,1), ev="grid", mg=100, deg=0,
+   flim=c(1,6), kern="gauss")
[1] -0.3648199  3.3894323
> # same computation, exact
> lscv(geyser,alpha=1,exact=TRUE)
[1] -0.3649103  3.3871090
> 
> 
> 
> cleanEx(); ..nameEx <- "lscv.exact"
> 
> ### * lscv.exact
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lscv.exact
> ### Title: Exact LSCV Calculation
> ### Aliases: lscv.exact
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(geyser)
> lscv.exact(geyser,alpha=0.25)
[1] -0.4201239 12.7251170
> # equivalent form using lscv
> lscv(geyser,alpha=0.25,exact=TRUE)
[1] -0.4201239 12.7251170
> 
> 
> 
> cleanEx(); ..nameEx <- "panel.locfit"
> 
> ### * panel.locfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: panel.locfit
> ### Title: Locfit panel function
> ### Aliases: panel.locfit
> ### Keywords: smooth
> 
> ### ** Examples
> 
> library("lattice")
> 
> # a simple multi-panel display
> data(ethanol)
> xyplot(NOx~E|C,data=ethanol,panel=panel.locfit)
Called from: panel(x = c(0.696, 1.009, 1.142, 0.767, 1.006, 0.762, 1.144, 
1.045, 0.765, 0.878, 0.811, 0.729, 0.911, 0.808, 1.168, 0.749, 
0.892, 1.002, 0.899, 1.227, 0.795, 0.629), y = c(0.926, 2.845, 
1.013, 1.869, 2.836, 1.634, 1.021, 2.157, 1.732, 3.206, 2.471, 
1.397, 3.536, 2.202, 0.756, 1.62, 3.656, 2.964, 3.488, 0.754, 
2.064, 0.561), subscripts = c(15, 22, 23, 28, 29, 38, 39, 40, 
49, 50, 51, 57, 58, 59, 60, 61, 62, 63, 77, 78, 80, 83), panel.number = 1, 
    panel.counter = 1)
Browse[1]> 
Called from: panel(x = c(1.001, 1.231, 1.123, 0.808, 1.071, 0.893, 1.081, 
0.868, 0.637, 0.733, 0.715, 0.872, 0.696, 1.199, 1.03, 1.18, 
0.608), y = c(3.12, 0.638, 1.17, 3.148, 1.836, 3.567, 1.719, 
3.423, 0.571, 2.219, 1.419, 3.519, 1.139, 0.727, 2.581, 0.797, 
0.563), subscripts = c(6, 7, 8, 20, 21, 30, 36, 37, 45, 46, 47, 
48, 69, 70, 71, 79, 84), panel.number = 2, panel.counter = 2)
Browse[1]> # The second example uses some Locfit optional arguments.
Called from: panel(x = c(0.907, 0.761, 1.108, 1.016, 1.189, 1.042, 1.215, 
0.93, 1.152, 0.686, 1.072, 0.813, 1.002, 0.584), y = c(3.741, 
2.295, 1.498, 2.881, 0.76, 2.358, 0.606, 3.669, 1, 1.59, 1.806, 
3.517, 3.29, 0.678), subscripts = c(1, 2, 3, 4, 5, 9, 10, 11, 
12, 16, 17, 67, 68, 85), panel.number = 3, panel.counter = 3)
Browse[1]> # Note we can pass the alpha (bandwidth) and family arguments directly to
Called from: panel(x = c(1.138, 1.074, 0.934, 0.568, 0.977, 1.152, 0.693, 
1.232, 1.036, 1.125, 0.676, 0.846, 0.684, 0.602, 0.694, 0.816, 
1.037, 1.181, 0.562), y = c(0.981, 1.962, 4.028, 0.374, 3.623, 
0.866, 1.369, 0.542, 2.739, 1.2, 1.777, 3.931, 1.587, 0.923, 
1.527, 3.388, 2.085, 0.966, 0.37), subscripts = c(13, 18, 19, 
26, 27, 31, 32, 33, 34, 35, 52, 55, 56, 72, 73, 74, 75, 76, 86
), panel.number = 4, panel.counter = 4)
Browse[1]> # xyplot. The cens argument must be given in full; not as a data frame variable.
Called from: panel(x = c(0.601, 1.229, 1.175, 0.797, 1.115, 1.07, 1.219, 1.045, 
0.968, 0.812, 1.23, 0.804, 0.99, 1.201, 0.535, 0.655), y = c(1.192, 
0.414, 0.812, 3.361, 1.39, 1.947, 0.962, 2.571, 3.952, 3.76, 
0.672, 3.677, 3.732, 0.586, 0.53, 1.9), subscripts = c(14, 24, 
25, 41, 42, 43, 44, 53, 54, 64, 65, 66, 81, 82, 87, 88), panel.number = 5, 
    panel.counter = 5)
Browse[1]> # The resulting plot does not (yet) distinguish the censored points, but
> # the fit will correctly apply censoring.
> data(border)
> xyplot(runs~day,data=border,panel=panel.locfit,family="poisson",
+  alpha=0.3,cens=border$no)
Called from: panel(x = c(1978.995, 1979.003, 1979.019, 1979.03, 1979.077, 
1979.085, 1979.192, 1979.203, 1979.23, 1979.241, 1979.696, 1979.71, 
1979.718, 1979.732, 1979.756, 1979.767, 1979.789, 1979.795, 1979.819, 
1979.83, 1979.849, 1979.852, 1979.918, 1979.926, 1979.953, 1979.959, 
1979.995, 1980, 1980.014, 1980.025, 1980.074, 1980.082, 1980.09, 
1980.159, 1980.164, 1980.184, 1980.214, 1980.225, 1980.66, 1980.668, 
1980.912, 1980.951, 1980.986, 1980.995, 1981.008, 1981.063, 1981.071, 
1981.107, 1981.115, 1981.468, 1981.471, 1981.507, 1981.515, 1981.542, 
1981.553, 1981.578, 1981.586, 1981.619, 1981.627, 1981.658, 1981.666, 
1981.868, 1981.874, 1981.91, 1981.951, 1981.953, 1981.986, 1981.992, 
1982.011, 1982.016, 1982.082, 1982.09, 1982.195, 1982.205, 1982.214, 
1982.729, 1982.737, 1982.753, 1982.759, 1982.786, 1982.797, 1982.871, 
1982.879, 1982.907, 1982.918, 1982.945, 1982.989, 1982.995, 1983.008, 
1983.016, 1983.31, 1983.866, 1983.904, 1983.94, 1983.951, 1983.992, 
1984.014, 1984.17, 1984.178, 1984.208, 1984.219, 1984.244, 1984.258, 
1984.266, 1984.277, 1984.323, 1984.329, 1984.86, 1984.863, 1984.896, 
1984.901, 1984.94, 1984.945, 1984.981, 1984.989, 1985, 1985.449, 
1985.46, 1985.493, 1985.501, 1985.532, 1985.584, 1985.597, 1985.622, 
1985.633, 1985.663, 1985.671, 1985.855, 1985.863, 1985.896, 1985.904, 
1985.915, 1985.923, 1985.951, 1985.986, 1985.995, 1986.014, 1986.016, 
1986.142, 1986.162, 1986.173, 1986.197, 1986.205, 1986.718, 1986.723, 
1986.789, 1986.8, 1986.877, 1986.882, 1986.915, 1986.923, 1986.951, 
1986.959, 1986.986, 1986.992, 1987.027, 1987.033, 1987.929, 1987.951, 
1987.989, 1987.997, 1988.085, 1988.09, 1988.118, 1988.712, 1988.721, 
1988.734, 1988.767, 1988.775, 1988.882, 1988.888, 1988.926, 1988.932, 
1988.986, 1988.995, 1989.077, 1989.082, 1989.093, 1989.104, 1989.436, 
1989.447, 1989.477, 1989.488, 1989.512, 1989.575, 1989.611, 1989.647, 
1989.66, 1989.899, 1989.937, 1989.959, 1989.964, 1990.025, 1990.03, 
1990.055, 1990.063, 1990.107, 1990.203, 1990.211, 1990.899, 1990.992, 
1991.011, 1991.022, 1991.068, 1991.077, 1991.09, 1991.167, 1991.225, 
1991.238, 1991.26, 1991.274, 1991.301, 1991.312, 1991.321, 1991.326, 
1991.915, 1991.989, 1991.995, 1992.005, 1992.016, 1992.068, 1992.077, 
1992.088, 1992.096, 1992.627, 1992.638, 1992.658, 1992.668, 1992.688, 
1992.699, 1992.907, 1992.915, 1992.989, 1992.995, 1993.008, 1993.066, 
1993.071, 1993.082, 1993.088, 1993.156, 1993.181, 1993.195, 1993.2, 
1993.422, 1993.43, 1993.463, 1993.504, 1993.512, 1993.559, 1993.597, 
1993.636, 1993.644, 1993.866, 1993.907, 1993.929, 1993.995, 1994.008, 
1994.014, 1994.079, 1994.085, 1994.175, 1994.184, 1994.214, 1994.23, 
1994.241), y = c(29, 0, 60, 45, 11, 1, 20, 105, 85, 66, 162, 
50, 44, 19, 24, 8, 24, 46, 54, 6, 23, 61, 1, 7, 4, 115, 17, 15, 
15, 2, 54, 24, 63, 30, 58, 4, 150, 153, 56, 21, 36, 10, 45, 9, 
31, 57, 7, 124, 9, 63, 20, 64, 12, 8, 0, 2, 40, 11, 123, 106, 
84, 3, 37, 36, 7, 1, 4, 66, 53, 9, 78, 126, 0, 38, 6, 55, 8, 
9, 31, 9, 6, 8, 32, 0, 15, 26, 2, 62, 89, 83, 47, 32, 118, 117, 
66, 32, 64, 5, 54, 98, 100, 38, 8, 98, 19, 41, 60, 15, 6, 17, 
24, 21, 18, 35, 41, 69, 32, 8, 196, 41, 23, 8, 146, 45, 2, 38, 
58, 1, 152, 20, 11, 12, 83, 49, 11, 163, 71, 4, 13, 140, 114, 
17, 6, 106, 27, 46, 66, 7, 23, 125, 16, 70, 100, 15, 34, 34, 
49, 9, 205, 31, 43, 2, 48, 88, 4, 18, 113, 75, 20, 4, 41, 6, 
26, 0, 20, 75, 16, 64, 6, 66, 60, 35, 1, 8, 80, 65, 76, 51, 50, 
56, 24, 85, 24, 62, 13, 8, 27, 1, 78, 9, 62, 78, 20, 12, 83, 
17, 31, 47, 34, 43, 27, 29, 0, 59, 5, 28, 0, 5, 19, 53, 0, 91, 
59, 20, 3, 15, 13, 28, 106, 78, 73, 17, 110, 4, 74, 19, 1, 0, 
0, 88, 30, 0, 71, 17, 31, 77, 38, 2, 200, 3, 48, 17, 16, 60, 
105, 2, 49, 7, 84, 4, 34, 14, 45, 17, 42), subscripts = c(1, 
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 
217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 
230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 
243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 
256, 257, 258, 259, 260, 261, 262, 263, 264, 265), family = "poisson", 
    alpha = 0.3, cens = c(0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
    0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 
    0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
    0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 
    0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 
    1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 1), panel.number = 1, panel.counter = 1)
Browse[1]> 
> 
> 
> cleanEx(); ..nameEx <- "plot.eval"
> 
> ### * plot.eval
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.eval
> ### Title: Plot evaluation points from a 2-d locfit object.
> ### Aliases: plot.eval
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> fit <- locfit(NOx~E+C,data=ethanol,scale=0)
> plot.eval(fit)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.gcvplot"
> 
> ### * plot.gcvplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.gcvplot
> ### Title: Produce a cross-validation plot.
> ### Aliases: plot.gcvplot
> ### Keywords: methods
> 
> ### ** Examples
> 
> data(ethanol)
> plot(gcvplot(NOx~E,data=ethanol,alpha=seq(0.2,1.0,by=0.05)))
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.locfit"
> 
> ### * plot.locfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.locfit
> ### Title: Plot an object of class locfit.
> ### Aliases: plot.locfit
> ### Keywords: smooth
> 
> ### ** Examples
> 
> x <- rnorm(100)
> y <- dnorm(x)+rnorm(100)/5
> plot(locfit(y ~ x), vband=TRUE)
Warning: parameter "vband" could not be set in high-level plot() function
Warning: parameter "vband" could not be set in high-level plot() function
Warning: parameter "vband" could not be set in high-level plot() function
Warning: parameter "vband" could not be set in high-level plot() function
Warning: parameter "vband" could not be set in high-level plot() function
Warning: parameter "vband" could not be set in high-level plot() function
> x <- cbind(rnorm(100), rnorm(100))
> plot(locfit(~x), type="persp")
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.scb"
> 
> ### * plot.scb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.scb
> ### Title: Plot method for simultaneous confidence bands
> ### Aliases: plot.scb
> ### Keywords: methods
> 
> ### ** Examples
> 
> # corrected confidence bands for a linear logistic model
> data(insect)
> fit <- scb(deaths ~ lconc, type=4, w=nins, data=insect,
+    deg=1, family="binomial", kern="parm")
> plot(fit)
> 
> 
> 
> cleanEx(); ..nameEx <- "plotbyfactor"
> 
> ### * plotbyfactor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plotbyfactor
> ### Title: x-y scatterplot, colored by levels of a factor.
> ### Aliases: plotbyfactor
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(iris)
> plotbyfactor(petal.wid,petal.len,species,data=iris)
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.locfit"
> 
> ### * predict.locfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.locfit
> ### Title: Prediction from a Locfit object.
> ### Aliases: predict.locfit
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> fit <- locfit(NOx~E,data=ethanol)
> predict(fit,c(0.6,0.8,1.0))
[1] 0.7402965 2.9038494 2.9159805
> 
> 
> 
> cleanEx(); ..nameEx <- "right"
> 
> ### * right
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: right
> ### Title: One-sided right smooth for a Locfit model.
> ### Aliases: right
> ### Keywords: models
> 
> ### ** Examples
> 
> # compute left and right smooths
> data(penny)
> xev <- (1945:1988)+0.5
> fitl <- locfit(thickness~left(year), alpha=c(0,10), deg=1, ev=xev, data=penny)
> fitr <- locfit(thickness~right(year),alpha=c(0,10), deg=1, ev=xev, data=penny)
> # plot the squared difference, to show the change points.
> plot( xev, (predict(fitr,where="ev") - predict(fitl,where="ev"))^2 )
> 
> 
> 
> cleanEx(); ..nameEx <- "rv"
> 
> ### * rv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rv
> ### Title: Residual variance from a "locfit" object.
> ### Aliases: rv
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> fit <- locfit(NOx~E,data=ethanol)
> rv(fit)
       rv 
0.1435404 
> 
> 
> 
> cleanEx(); ..nameEx <- "scb"
> 
> ### * scb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: scb
> ### Title: Simultaneous Confidence Bands
> ### Aliases: scb
> ### Keywords: htest smooth
> 
> ### ** Examples
> 
> # corrected confidence bands for a linear logistic model
> data(insect)
> fit <- scb(deaths ~ lconc, type=4, w=nins, data=insect,
+    deg=1, family="binomial", kern="parm")
> plot(fit)
> 
> 
> 
> cleanEx(); ..nameEx <- "sjpi"
> 
> ### * sjpi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sjpi
> ### Title: Sheather-Jones Plug-in bandwidth criterion.
> ### Aliases: sjpi
> ### Keywords: smooth
> 
> ### ** Examples
> 
> # Fig 10.2 (S-J parts) from Loader (1999).
> data(geyser)
> gf <- 2.5
> a <- seq(0.05, 0.7, length=100)
> z <- sjpi(geyser, a)
> 
> # the plug-in curve. Multiplying by gf=2.5 corresponds to Locfit's standard
> # scaling for the Gaussian kernel.
> plot(gf*z[, 2], gf*z[, 1], type = "l", xlab = "Pilot Bandwidth k", ylab
+      = "Bandwidth h")
> 
> # Add the assumed curve.
> lines(gf * z[, 3], gf * z[, 1], lty = 2)
> legend(gf*0.05, gf*0.4, lty = 1:2, legend = c("Plug-in", "SJ assumed"))
> 
> 
> 
> cleanEx(); ..nameEx <- "spence.15"
> 
> ### * spence.15
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: spence.15
> ### Title: Spencer's 15 point graduation rule.
> ### Aliases: spence.15
> ### Keywords: smooth
> 
> ### ** Examples
> 
>   data(spencer)
>   yy <- spence.15(spencer$mortality)
>   plot(spencer$age, spencer$mortality)
>   lines(spencer$age, yy)
> 
> 
> 
> cleanEx(); ..nameEx <- "spence.21"
> 
> ### * spence.21
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: spence.21
> ### Title: Spencer's 21 point graduation rule.
> ### Aliases: spence.21
> ### Keywords: smooth
> 
> ### ** Examples
> 
>   data(spencer)
>   yy <- spence.21(spencer$mortality)
>   plot(spencer$age, spencer$mortality)
>   lines(spencer$age, yy)
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.gcvplot"
> 
> ### * summary.gcvplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.gcvplot
> ### Title: Summary method for a gcvplot structure.
> ### Aliases: summary.gcvplot
> ### Keywords: smooth
> 
> ### ** Examples
> 
> data(ethanol)
> summary(gcvplot(NOx ~ E, data=ethanol, alpha=seq(0.2, 1.0, by=0.05)))
             df       GCV
 [1,] 18.812657 0.1183932
 [2,] 13.967891 0.1094681
 [3,] 11.469598 0.1065792
 [4,] 10.465337 0.1082955
 [5,]  8.435026 0.1180386
 [6,]  7.859473 0.1208126
 [7,]  7.011088 0.1216522
 [8,]  6.523260 0.1235520
 [9,]  6.068841 0.1265091
[10,]  5.337439 0.1413958
[11,]  5.089902 0.1517506
[12,]  4.767998 0.1744947
[13,]  4.567191 0.1936765
[14,]  4.416665 0.2084184
[15,]  4.207973 0.2322066
[16,]  3.979570 0.2557630
[17,]  3.838952 0.2664913
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  4.06 0.35 4.44 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')