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> ### > 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("svmpath-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('svmpath') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "balanced.overlap" > > ### * balanced.overlap > > flush(stderr()); flush(stdout()) > > ### Name: balanced.overlap > ### Title: simple examples for svmpath > ### Aliases: balanced.overlap balanced.separated unbalanced.separated > ### mixture.data > ### Keywords: datasets > > ### ** Examples > > data(svmpath) > attach(balanced.overlap) > svmpath(x,y,trace=TRUE,plot=TRUE) 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 > detach(2) > > > > cleanEx(); ..nameEx <- "predict.svmpath" > > ### * predict.svmpath > > flush(stderr()); flush(stdout()) > > ### Name: predict.svmpath > ### Title: Make predictions from a svmpath object > ### Aliases: predict.svmpath > ### Keywords: regression > > ### ** Examples > > data(svmpath) > attach(balanced.overlap) > fit <- svmpath(x,y,trace=TRUE,plot=TRUE) 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 > predict(fit, lambda=1,type="alpha") $alpha0 [1] 0.06087534 $alpha [1] 1.000000e+00 1.000000e+00 9.642170e-01 1.000000e+00 1.000000e+00 [6] 0.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 [11] 1.110223e-16 9.642170e-01 $lambda [1] 1 > predict(fit, x, lambda=.9) [,1] [1,] -1.00000000 [2,] 1.22203643 [3,] -1.00000000 [4,] -0.05830504 [5,] 0.99613971 [6,] -1.78259239 [7,] 0.97847787 [8,] 0.31265914 [9,] 0.05778274 [10,] -0.19166318 [11,] 1.09292292 [12,] 1.00000000 > detach(2) > > > > cleanEx(); ..nameEx <- "print.svmpath" > > ### * print.svmpath > > flush(stderr()); flush(stdout()) > > ### Name: print.svmpath > ### Title: Print a summary of the SVM path > ### Aliases: print.svmpath > ### Keywords: regression > > ### ** Examples > > data(svmpath) > attach(balanced.overlap) > fit <- svmpath(x,y,trace=TRUE,plot=TRUE) 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 > print(fit) 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 > detach(2) > > > > cleanEx(); ..nameEx <- "radial.kernel" > > ### * radial.kernel > > flush(stderr()); flush(stdout()) > > ### Name: radial.kernel > ### Title: compute the kernel matrix for svmpath > ### Aliases: radial.kernel poly.kernel > ### Keywords: regression > > ### ** Examples > > data(svmpath) > attach(balanced.overlap) > fit<-svmpath(x,y,kernel=radial.kernel) > detach(2) > > > > cleanEx(); ..nameEx <- "summary.svmpath" > > ### * summary.svmpath > > flush(stderr()); flush(stdout()) > > ### Name: summary.svmpath > ### Title: produce a summary of an svmpath object > ### Aliases: summary.svmpath > ### Keywords: regression > > ### ** Examples > > data(svmpath) > attach(balanced.overlap) > fit <- svmpath(x,y,trace=TRUE,plot=TRUE) 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 > summary(fit) Call: svmpath(x = x, y = y, trace = TRUE, plot.it = TRUE) Number of steps: 4 Selected steps: Lambda Error Size.Elbow Support SumEps 1 3.851000 2 2 12 8.698223 2 1.489298 3 0 11 8.633890 3 1.047224 3 2 11 8.057200 4 0.907920 3 3 11 8.002615 > detach(2) > > > > cleanEx(); ..nameEx <- "svmpath" > > ### * svmpath > > flush(stderr()); flush(stdout()) > > ### Name: svmpath > ### Title: Fit the entire regularization path for a 2-class SVM > ### Aliases: svmpath > ### Keywords: regression > > ### ** Examples > > data(svmpath) > attach(balanced.overlap) > svmpath(x,y,trace=TRUE,plot=TRUE) 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 1: Obs 11 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 1: Obs 6 ->E lambda = 3.851000 Margin = 8.7 Elbow = 2 Error = 2 2: Obs 11 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 2: Obs 6 E->R lambda = 1.489298 Margin = 8.63 Elbow = 0 Error = 3 3: Obs 12 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 3: Obs 3 ->E lambda = 1.047224 Margin = 8.06 Elbow = 2 Error = 3 4: Obs 1 L->E lambda = 0.907920 Margin = 8 Elbow = 3 Error = 3 > detach(2) > ## Not run: svmpath(x,y,kernel=radial.kernel,param.kernel=.8) > > > > ### *