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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("mda-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('mda') Loading required package: class > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "bruto" > > ### * bruto > > flush(stderr()); flush(stdout()) > > ### Name: bruto > ### Title: Fit an Additive Spline Model by Adaptive Backfitting > ### Aliases: bruto > ### Keywords: smooth > > ### ** Examples > > data(trees) > fit1 <- bruto(trees[,-3], trees[3]) > fit1$type [1] smooth linear Levels: excluded linear smooth > fit1$df Girth Height 2.371150 1.000000 > ## examine the fitted functions > par(mfrow=c(1,2), pty="s") > Xp <- matrix(sapply(trees[1:2], mean), nrow(trees), 2, byrow=TRUE) > for(i in 1:2) { + xr <- sapply(trees, range) + Xp1 <- Xp; Xp1[,i] <- seq(xr[1,i], xr[2,i], len=nrow(trees)) + Xf <- predict(fit1, Xp1) + plot(Xp1[ ,i], Xf, xlab=names(trees)[i], ylab="", type="l") + } > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "confusion" > > ### * confusion > > flush(stderr()); flush(stdout()) > > ### Name: confusion > ### Title: Confusion Matrices > ### Aliases: confusion confusion.default confusion.list confusion.fda > ### Keywords: category > > ### ** Examples > > data(iris) > irisfit <- fda(Species ~ ., data = iris) > confusion(predict(irisfit, iris), iris$Species) true object setosa versicolor virginica setosa 50 0 0 versicolor 0 48 1 virginica 0 2 49 attr(,"error") [1] 0.02 > ## Setosa Versicolor Virginica > ## Setosa 50 0 0 > ## Versicolor 0 48 1 > ## Virginica 0 2 49 > ## attr(, "error"): > ## [1] 0.02 > > > > cleanEx(); ..nameEx <- "fda" > > ### * fda > > flush(stderr()); flush(stdout()) > > ### Name: fda > ### Title: Flexible Discriminant Analysis > ### Aliases: fda coef.fda plot.fda print.fda > ### Keywords: classif > > ### ** Examples > > data(iris) > irisfit <- fda(Species ~ ., data = iris) > irisfit Call: fda(formula = Species ~ ., data = iris) Dimension: 2 Percent Between-Group Variance Explained: v1 v2 99.12 100.00 Degrees of Freedom (per dimension): 5 Training Misclassification Error: 0.02 ( N = 150 ) > ## fda(formula = Species ~ ., data = iris) > ## > ## Dimension: 2 > ## > ## Percent Between-Group Variance Explained: > ## v1 v2 > ## 99.12 100.00 > ## > ## Degrees of Freedom (per dimension): 5 > ## > ## Training Misclassification Error: 0.02 ( N = 150 ) > > confusion(irisfit, iris) true object setosa versicolor virginica setosa 50 0 0 versicolor 0 48 1 virginica 0 2 49 attr(,"error") [1] 0.02 > ## Setosa Versicolor Virginica > ## Setosa 50 0 0 > ## Versicolor 0 48 1 > ## Virginica 0 2 49 > ## attr(, "error"): > ## [1] 0.02 > > plot(irisfit) > > coef(irisfit) [,1] [,2] Intercept -2.126479 -6.72910343 Sepal.Length -0.837798 0.02434685 Sepal.Width -1.550052 2.18649663 Petal.Length 2.223560 -0.94138258 Petal.Width 2.838994 2.86801283 attr(,"scaled:scale") v1 v2 0.1709389 0.4156090 > ## [,1] [,2] > ## [1,] -2.126479 -6.72910343 > ## [2,] -0.837798 0.02434685 > ## [3,] -1.550052 2.18649663 > ## [4,] 2.223560 -0.94138258 > ## [5,] 2.838994 2.86801283 > > marsfit <- fda(Species ~ ., data = iris, method = mars) > marsfit2 <- update(marsfit, degree = 2) > marsfit3 <- update(marsfit, theta = marsfit$means[, 1:2]) > ## this refits the model, using the fitted means (scaled theta's) > ## from marsfit to start the iterations > > > > cleanEx(); ..nameEx <- "mars" > > ### * mars > > flush(stderr()); flush(stdout()) > > ### Name: mars > ### Title: Multivariate Additive Regression Splines > ### Aliases: mars > ### Keywords: smooth > > ### ** Examples > > data(trees) > fit1 <- mars(trees[,-3], trees[3]) > showcuts <- function(obj) + { + tmp <- obj$cuts[obj$sel, ] + dimnames(tmp) <- list(NULL, names(trees)[-3]) + tmp + } > showcuts(fit1) Girth Height [1,] 0 0 [2,] 12 0 [3,] 12 0 [4,] 0 76 > > ## examine the fitted functions > par(mfrow=c(1,2), pty="s") > Xp <- matrix(sapply(trees[1:2], mean), nrow(trees), 2, byrow=TRUE) > for(i in 1:2) { + xr <- sapply(trees, range) + Xp1 <- Xp; Xp1[,i] <- seq(xr[1,i], xr[2,i], len=nrow(trees)) + Xf <- predict(fit1, Xp1) + plot(Xp1[ ,i], Xf, xlab=names(trees)[i], ylab="", type="l") + } > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "mda" > > ### * mda > > flush(stderr()); flush(stdout()) > > ### Name: mda > ### Title: Mixture Discriminant Analysis > ### Aliases: mda print.mda > ### Keywords: classif > > ### ** Examples > > data(iris) > irisfit <- mda(Species ~ ., data = iris) > irisfit Call: mda(formula = Species ~ ., data = iris) Dimension: 4 Percent Between-Group Variance Explained: v1 v2 v3 v4 96.02 98.55 99.90 100.00 Degrees of Freedom (per dimension): 5 Training Misclassification Error: 0.02 ( N = 150 ) Deviance: 15.102 > ## Call: > ## mda(formula = Species ~ ., data = iris) > ## > ## Dimension: 4 > ## > ## Percent Between-Group Variance Explained: > ## v1 v2 v3 v4 > ## 96.02 98.55 99.90 100.00 > ## > ## Degrees of Freedom (per dimension): 5 > ## > ## Training Misclassification Error: 0.02 ( N = 150 ) > ## > ## Deviance: 15.102 > > data(glass) > # random sample of size 100 > samp <- c(1, 3, 4, 11, 12, 13, 14, 16, 17, 18, 19, 20, 27, 28, 31, + 38, 42, 46, 47, 48, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, + 67, 68, 69, 70, 72, 73, 78, 79, 83, 84, 85, 87, 91, 92, 94, + 99, 100, 106, 107, 108, 111, 112, 113, 115, 118, 121, 123, + 124, 125, 126, 129, 131, 133, 136, 139, 142, 143, 145, 147, + 152, 153, 156, 159, 160, 161, 164, 165, 166, 168, 169, 171, + 172, 173, 174, 175, 177, 178, 181, 182, 185, 188, 189, 192, + 195, 197, 203, 205, 211, 212, 214) > glass.train <- glass[samp,] > glass.test <- glass[-samp,] > glass.mda <- mda(Type ~ ., data = glass.train) > predict(glass.mda, glass.test, type="post") # abbreviations are allowed 1 2 3 5 6 [1,] 1.264350e-01 8.272228e-01 4.634191e-02 2.106560e-30 1.066730e-13 [2,] 2.926294e-01 6.899555e-01 1.741505e-02 1.317702e-27 4.219347e-15 [3,] 4.618357e-01 5.372106e-01 9.536696e-04 1.210610e-19 7.922295e-15 [4,] 3.658653e-01 4.992758e-01 1.348589e-01 3.311979e-29 4.560209e-15 [5,] 4.391207e-01 4.143377e-01 1.465416e-01 1.057735e-28 2.207756e-15 [6,] 4.875401e-01 4.348184e-01 7.755131e-02 8.191245e-30 6.119974e-13 [7,] 6.964351e-01 2.909869e-01 1.257800e-02 5.845053e-24 1.622033e-13 [8,] 7.245356e-01 2.717643e-01 3.700084e-03 3.659156e-22 1.561232e-14 [9,] 4.970155e-01 5.011636e-01 1.820858e-03 5.063558e-17 2.788388e-13 [10,] 2.348862e-01 7.610086e-07 7.651130e-01 9.239189e-45 3.329524e-18 [11,] 5.130299e-01 4.720716e-01 1.489846e-02 1.584051e-22 2.191527e-15 [12,] 7.227943e-01 2.374829e-01 3.972281e-02 5.307566e-24 4.632529e-14 [13,] 3.744886e-01 5.274013e-01 9.811010e-02 1.683781e-26 1.008328e-13 [14,] 6.306488e-01 3.227627e-01 4.658853e-02 8.325527e-26 1.862535e-14 [15,] 5.766222e-01 4.211565e-01 2.221276e-03 2.671774e-18 5.899345e-14 [16,] 4.335481e-01 5.592639e-01 7.188071e-03 3.108822e-23 8.136691e-14 [17,] 7.148877e-01 2.595441e-01 2.556817e-02 7.300333e-24 5.393951e-14 [18,] 8.109298e-01 1.858191e-01 3.251041e-03 6.085739e-21 4.477569e-13 [19,] 8.810039e-01 1.147942e-01 4.201894e-03 1.767892e-20 8.992226e-13 [20,] 6.839273e-01 3.014682e-01 1.460453e-02 1.841971e-20 8.809230e-14 [21,] 3.971585e-01 3.678043e-01 2.350372e-01 7.319294e-27 2.836980e-14 [22,] 6.679130e-01 6.491209e-02 2.671682e-01 1.078587e-28 1.236649e-12 [23,] 2.358200e-01 1.185201e-06 7.641788e-01 8.675711e-38 1.003369e-16 [24,] 2.358200e-01 1.185201e-06 7.641788e-01 8.675711e-38 1.003369e-16 [25,] 7.307605e-01 2.456389e-01 2.360064e-02 1.668652e-23 2.435264e-14 [26,] 5.287568e-01 4.401187e-01 3.112431e-02 8.010518e-23 3.431084e-12 [27,] 8.420993e-01 5.268970e-05 1.578480e-01 3.689093e-31 4.767966e-15 [28,] 8.401899e-01 1.594042e-01 4.059077e-04 2.620124e-17 1.226008e-12 [29,] 4.083131e-01 5.481279e-01 4.355264e-02 1.984060e-22 7.141533e-12 [30,] 9.994256e-01 1.137526e-06 5.732621e-04 4.635155e-28 3.784859e-14 [31,] 9.853189e-01 1.460671e-02 4.771226e-05 1.364456e-06 2.534834e-05 [32,] 2.433814e-01 7.564429e-01 1.757358e-04 1.692504e-19 1.327515e-14 [33,] 1.987674e-01 2.793076e-01 5.219250e-01 1.375081e-33 4.937858e-16 [34,] 4.252551e-01 4.792146e-01 9.553035e-02 1.421260e-28 1.085911e-14 [35,] 4.950676e-01 3.565084e-01 1.484232e-01 3.666827e-24 8.064885e-11 [36,] 9.146338e-01 4.424536e-02 4.094099e-02 5.619646e-21 1.203003e-09 [37,] 1.947872e-02 9.756175e-01 4.903227e-03 1.939942e-30 5.643896e-13 [38,] 1.776724e-01 8.117240e-01 1.060361e-02 1.454597e-26 7.714059e-15 [39,] 2.768258e-01 7.178278e-01 5.346366e-03 3.765489e-26 6.831782e-16 [40,] 3.436413e-01 6.462438e-01 1.011491e-02 1.218735e-26 9.352642e-16 [41,] 5.210261e-02 9.410153e-01 6.882111e-03 2.544822e-26 1.297689e-16 [42,] 1.547395e-01 8.042466e-01 4.101388e-02 1.597566e-19 5.328005e-15 [43,] 8.304307e-02 4.791231e-01 4.378334e-01 1.979196e-18 2.374053e-14 [44,] 2.080330e-01 7.894548e-01 2.512157e-03 1.627394e-25 1.154497e-14 [45,] 2.337906e-01 7.330662e-01 3.314316e-02 6.147674e-25 6.256540e-14 [46,] 2.514905e-01 7.357778e-01 1.273168e-02 3.085622e-25 5.434705e-14 [47,] 1.209804e-01 8.784627e-01 5.569028e-04 1.806260e-21 1.007967e-15 [48,] 5.297049e-01 4.572846e-01 1.301052e-02 2.669743e-16 5.472400e-13 [49,] 8.043606e-01 1.910594e-01 4.580011e-03 2.154920e-15 2.874927e-08 [50,] 4.286010e-01 5.705967e-01 8.022447e-04 7.125753e-19 8.789412e-14 [51,] 1.025125e-01 8.937625e-01 3.721591e-03 1.071319e-18 7.251711e-13 [52,] 7.391035e-01 1.905031e-01 7.039345e-02 3.316874e-24 1.702470e-14 [53,] 9.522508e-01 4.768534e-02 6.389601e-05 5.509940e-13 6.387203e-12 [54,] 9.497571e-01 4.965639e-02 5.858738e-04 4.166731e-11 5.942445e-07 [55,] 6.488413e-01 3.286132e-01 5.994015e-03 1.655050e-02 7.366036e-08 [56,] 9.855822e-01 5.350676e-03 9.033156e-03 2.982695e-10 3.393063e-05 [57,] 9.999019e-01 3.275305e-08 1.092755e-05 2.898796e-18 7.927704e-10 [58,] 7.817661e-01 8.594596e-04 1.719645e-03 2.116588e-11 6.193363e-04 [59,] 8.580845e-33 1.461768e-04 3.203563e-38 9.998533e-01 2.062337e-08 [60,] 3.562839e-31 8.542917e-06 7.447997e-32 1.367886e-01 9.729905e-08 [61,] 3.240318e-01 6.567061e-01 1.926194e-02 6.767735e-29 3.035513e-15 [62,] 1.460335e-01 8.410164e-01 1.295004e-02 9.419299e-29 9.136456e-17 [63,] 1.558098e-01 8.363565e-01 7.833694e-03 4.814509e-26 8.566394e-17 [64,] 2.403044e-01 7.586631e-01 1.032506e-03 4.722299e-23 8.298721e-15 [65,] 4.459336e-02 9.534662e-01 1.940475e-03 5.780791e-27 2.422789e-16 [66,] 3.757580e-01 6.235172e-01 7.248536e-04 1.238881e-19 5.904859e-14 [67,] 3.873485e-01 6.002634e-01 1.238803e-02 8.108843e-24 9.547163e-16 [68,] 9.289114e-04 5.566026e-01 3.504414e-05 5.635836e-07 4.356787e-01 [69,] 4.767778e-16 9.947049e-01 3.243033e-16 2.681769e-03 2.613339e-03 [70,] 3.552321e-43 4.298159e-06 2.998833e-49 9.999957e-01 1.671703e-20 [71,] 8.293282e-02 8.509537e-01 6.611328e-02 2.936607e-28 1.356355e-16 [72,] 2.095439e-01 7.734920e-01 1.696406e-02 7.887885e-30 1.250520e-16 [73,] 6.388154e-01 2.921479e-01 6.903672e-02 2.993701e-28 5.684612e-16 [74,] 2.224580e-01 7.745791e-01 2.962830e-03 4.811776e-22 2.081472e-15 [75,] 3.197415e-01 6.749689e-01 5.289565e-03 1.860529e-22 1.027647e-14 [76,] 1.405097e-01 8.221425e-01 3.734763e-02 5.951547e-25 8.992455e-15 [77,] 2.182272e-01 7.143369e-01 6.743494e-02 1.027323e-19 2.200524e-13 [78,] 6.499581e-01 3.497421e-01 2.997644e-04 4.821819e-19 1.994593e-14 [79,] 2.475058e-01 7.259798e-01 2.651439e-02 4.067753e-26 6.730784e-15 [80,] 5.439183e-01 3.914413e-01 6.464037e-02 2.339284e-25 1.375348e-13 [81,] 2.688445e-02 9.731151e-01 4.703113e-07 2.553969e-10 2.739551e-19 [82,] 3.698430e-01 4.676054e-01 1.625513e-01 1.255625e-18 6.474997e-12 [83,] 1.679020e-01 8.217923e-01 1.030570e-02 2.309509e-25 8.579726e-15 [84,] 4.719533e-01 4.711331e-01 5.691362e-02 1.042019e-22 2.801612e-15 [85,] 4.349112e-01 3.080653e-01 2.570235e-01 2.007511e-25 2.558148e-12 [86,] 6.139503e-01 1.788184e-05 3.860319e-01 4.292499e-34 2.756483e-15 [87,] 4.163124e-01 2.624113e-02 5.574465e-01 2.434587e-25 5.676373e-11 [88,] 9.920381e-01 7.701168e-03 2.563451e-04 8.827113e-25 1.374612e-12 [89,] 1.998995e-22 4.759813e-09 4.538252e-24 1.000000e+00 4.516744e-16 [90,] 4.306650e-38 1.330091e-08 3.610567e-39 1.000000e+00 2.244105e-13 [91,] 2.043283e-40 3.471771e-09 1.259674e-45 1.000000e+00 1.275980e-16 [92,] 2.967493e-07 8.930271e-05 8.812923e-07 3.203283e-13 9.998978e-01 [93,] 1.482440e-07 1.423235e-03 8.043396e-08 1.284236e-09 9.985728e-01 [94,] 3.436445e-40 2.499863e-08 9.510182e-40 9.998758e-01 6.599228e-11 [95,] 1.125192e-28 1.036411e-04 1.304349e-29 1.796119e-03 2.080867e-05 [96,] 3.630682e-03 2.869572e-01 7.094121e-01 4.605047e-10 8.839679e-16 [97,] 2.864273e-02 9.674346e-01 3.836318e-03 4.986716e-06 8.291535e-17 [98,] 7.246874e-03 5.271197e-05 1.447567e-02 4.960977e-28 7.774652e-01 [99,] 5.858680e-09 2.120687e-04 5.225563e-10 6.835476e-12 9.997879e-01 [100,] 1.583489e-45 1.100572e-16 1.326924e-46 1.684444e-12 8.484202e-17 [101,] 1.921032e-47 1.561104e-23 2.610742e-48 4.154037e-27 1.511832e-17 [102,] 1.865969e-42 9.946538e-16 2.469582e-43 2.767175e-12 3.706669e-15 [103,] 7.267652e-43 3.482087e-16 3.223739e-45 4.054195e-16 5.152540e-15 [104,] 4.121171e-41 1.088874e-14 1.937950e-41 8.213448e-12 5.702646e-14 [105,] 3.592200e-42 8.927155e-17 7.412356e-43 8.183017e-18 3.278831e-14 [106,] 6.609017e-42 7.542932e-19 1.814292e-44 3.387324e-22 5.654193e-11 [107,] 2.795805e-09 3.868282e-03 2.688591e-13 2.518776e-07 1.712567e-01 [108,] 2.694789e-48 4.024979e-26 1.929656e-48 7.946769e-31 1.984508e-17 [109,] 3.730241e-47 1.480240e-24 1.243496e-46 1.668580e-29 4.055783e-17 [110,] 1.717867e-44 2.174115e-22 5.479287e-44 8.401703e-27 2.319251e-15 [111,] 4.404676e-54 4.963629e-44 3.870901e-52 1.279257e-62 5.540856e-20 [112,] 4.427792e-42 1.806291e-13 3.244956e-41 4.810310e-09 2.488221e-13 [113,] 1.751116e-41 7.433836e-14 1.028284e-40 6.054631e-10 4.007127e-14 [114,] 1.523728e-47 4.517427e-25 3.734286e-48 6.771217e-28 2.349824e-17 7 [1,] 2.902663e-07 [2,] 2.169952e-09 [3,] 9.334383e-11 [4,] 8.351445e-10 [5,] 1.712182e-10 [6,] 9.021989e-05 [7,] 5.506290e-09 [8,] 1.054189e-09 [9,] 7.027690e-09 [10,] 1.289279e-14 [11,] 6.397340e-10 [12,] 4.455874e-09 [13,] 1.491253e-09 [14,] 2.105974e-09 [15,] 6.716007e-09 [16,] 1.798668e-08 [17,] 2.877126e-10 [18,] 4.000612e-10 [19,] 2.274700e-09 [20,] 6.957198e-09 [21,] 2.546542e-11 [22,] 6.785383e-06 [23,] 1.048855e-10 [24,] 1.048855e-10 [25,] 7.874704e-10 [26,] 1.352419e-07 [27,] 2.006527e-08 [28,] 2.053363e-10 [29,] 6.370890e-06 [30,] 1.058402e-09 [31,] 7.037008e-09 [32,] 1.398514e-08 [33,] 1.203387e-09 [34,] 1.629212e-09 [35,] 8.020277e-07 [36,] 1.798820e-04 [37,] 6.012970e-07 [38,] 9.016172e-09 [39,] 4.982222e-10 [40,] 3.079832e-10 [41,] 4.420965e-09 [42,] 2.170282e-08 [43,] 4.057098e-07 [44,] 4.215597e-10 [45,] 4.179628e-09 [46,] 9.731366e-09 [47,] 3.007018e-10 [48,] 3.105575e-08 [49,] 2.755324e-09 [50,] 7.066915e-10 [51,] 3.373310e-06 [52,] 1.092458e-09 [53,] 7.871816e-12 [54,] 2.813158e-09 [55,] 9.744932e-07 [56,] 1.324819e-10 [57,] 8.717063e-05 [58,] 2.150354e-01 [59,] 4.541742e-07 [60,] 8.632027e-01 [61,] 1.347212e-07 [62,] 6.698283e-08 [63,] 2.836698e-08 [64,] 1.702392e-09 [65,] 3.764844e-09 [66,] 2.744913e-09 [67,] 8.774729e-11 [68,] 6.754128e-03 [69,] 1.087216e-11 [70,] 2.967751e-25 [71,] 2.297541e-07 [72,] 7.657795e-09 [73,] 1.392810e-10 [74,] 1.061372e-08 [75,] 1.398452e-08 [76,] 1.034277e-07 [77,] 9.792212e-07 [78,] 7.509786e-10 [79,] 3.297076e-10 [80,] 2.000312e-09 [81,] 2.780650e-13 [82,] 1.931639e-07 [83,] 1.168209e-10 [84,] 4.214272e-10 [85,] 3.383236e-09 [86,] 2.940627e-10 [87,] 4.558248e-10 [88,] 4.393859e-06 [89,] 4.879367e-24 [90,] 3.210070e-11 [91,] 6.084898e-13 [92,] 1.171752e-05 [93,] 3.742119e-06 [94,] 1.241940e-04 [95,] 9.980794e-01 [96,] 1.373451e-17 [97,] 8.134099e-05 [98,] 2.007596e-01 [99,] 1.088608e-08 [100,] 1.000000e+00 [101,] 1.000000e+00 [102,] 1.000000e+00 [103,] 1.000000e+00 [104,] 1.000000e+00 [105,] 1.000000e+00 [106,] 1.000000e+00 [107,] 8.248748e-01 [108,] 1.000000e+00 [109,] 1.000000e+00 [110,] 1.000000e+00 [111,] 1.000000e+00 [112,] 1.000000e+00 [113,] 1.000000e+00 [114,] 1.000000e+00 > confusion(glass.mda,glass.test) true object 1 2 3 5 6 7 1 22 11 5 0 0 0 2 10 28 4 0 0 1 3 4 0 1 0 0 1 5 0 2 0 3 1 0 6 0 0 0 0 2 2 7 0 1 0 0 1 15 attr(,"error") [1] 0.377193 > > > > cleanEx(); ..nameEx <- "predict.bruto" > > ### * predict.bruto > > flush(stderr()); flush(stdout()) > > ### Name: predict.bruto > ### Title: Predict method for BRUTO Objects > ### Aliases: predict.bruto > ### Keywords: smooth > > ### ** Examples > > data(trees) > fit1 <- bruto(trees[,-3], trees[3]) > fitted.terms <- predict(fit1, as.matrix(trees[,-3]), type = "terms") > par(mfrow=c(1,2), pty="s") > for(tt in fitted.terms) plot(tt, type="l") > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "predict.fda" > > ### * predict.fda > > flush(stderr()); flush(stdout()) > > ### Name: predict.fda > ### Title: Classify by Flexible Discriminant Analysis > ### Aliases: predict.fda > ### Keywords: classif > > ### ** Examples > > data(iris) > irisfit <- fda(Species ~ ., data = iris) > irisfit Call: fda(formula = Species ~ ., data = iris) Dimension: 2 Percent Between-Group Variance Explained: v1 v2 99.12 100.00 Degrees of Freedom (per dimension): 5 Training Misclassification Error: 0.02 ( N = 150 ) > ## Call: > ## fda(x = iris$x, g = iris$g) > ## > ## Dimension: 2 > ## > ## Percent Between-Group Variance Explained: > ## v1 v2 > ## 99.12 100 > confusion(predict(irisfit, iris), iris$Species) true object setosa versicolor virginica setosa 50 0 0 versicolor 0 48 1 virginica 0 2 49 attr(,"error") [1] 0.02 > ## Setosa Versicolor Virginica > ## Setosa 50 0 0 > ## Versicolor 0 48 1 > ## Virginica 0 2 49 > ## attr(, "error"): > ## [1] 0.02 > > > > cleanEx(); ..nameEx <- "predict.mda" > > ### * predict.mda > > flush(stderr()); flush(stdout()) > > ### Name: predict.mda > ### Title: Classify by Mixture Discriminant Analysis > ### Aliases: predict.mda > ### Keywords: classif > > ### ** Examples > > data(glass) > samp <- sample(1:nrow(glass), 100) > glass.train <- glass[samp,] > glass.test <- glass[-samp,] > glass.mda <- mda(Type ~ ., data = glass.train) > predict(glass.mda, glass.test, type = "post") # abbreviations are allowed 1 2 3 5 6 [1,] 9.779075e-01 3.242592e-03 1.884988e-02 1.091940e-51 5.303309e-29 [2,] 6.353497e-01 3.474771e-01 1.717319e-02 2.265199e-28 3.039304e-15 [3,] 7.752633e-01 2.150716e-01 9.665082e-03 7.616789e-31 3.743443e-19 [4,] 6.155778e-01 3.819633e-01 2.458951e-03 1.642757e-33 4.699664e-19 [5,] 7.160300e-01 2.764486e-01 7.521455e-03 7.042404e-32 2.886702e-20 [6,] 2.788412e-01 6.459626e-01 7.519615e-02 2.141027e-26 1.951391e-16 [7,] 6.431683e-01 3.501198e-01 6.711844e-03 1.585369e-34 1.843886e-19 [8,] 7.029981e-01 2.939769e-01 3.025029e-03 2.404012e-31 2.751916e-18 [9,] 5.222824e-01 4.772342e-01 4.834481e-04 4.771114e-37 2.011970e-21 [10,] 8.008907e-01 1.912371e-01 7.872154e-03 8.606820e-32 2.331346e-19 [11,] 7.935161e-01 1.947087e-01 1.177528e-02 1.227661e-34 3.035368e-22 [12,] 4.480625e-01 4.193738e-01 1.325638e-01 9.122325e-37 1.079981e-15 [13,] 3.445062e-01 6.453296e-01 1.016423e-02 5.347739e-32 6.715061e-19 [14,] 5.867787e-01 6.199326e-05 4.131593e-01 2.110208e-51 8.615923e-32 [15,] 6.062505e-01 3.272729e-01 6.647660e-02 3.555620e-30 2.232979e-20 [16,] 7.879179e-01 1.647296e-01 4.735254e-02 5.917606e-30 1.601034e-18 [17,] 7.925177e-01 1.880715e-01 1.941079e-02 2.315261e-30 4.276600e-20 [18,] 2.150604e-01 7.648016e-01 2.013803e-02 3.725788e-28 6.295475e-19 [19,] 1.906851e-01 8.043031e-01 5.011839e-03 5.599022e-29 1.513269e-18 [20,] 8.596566e-01 1.294337e-01 1.090967e-02 6.254379e-32 4.024786e-20 [21,] 3.822495e-01 6.173639e-01 3.865690e-04 4.692216e-37 2.834629e-23 [22,] 6.936148e-01 2.814467e-01 2.493853e-02 9.336300e-30 1.123905e-18 [23,] 1.950354e-01 3.598981e-01 4.450665e-01 1.730055e-27 1.354060e-16 [24,] 7.951447e-01 1.850345e-01 1.982080e-02 6.685394e-28 1.309243e-18 [25,] 6.645292e-01 3.074203e-01 2.805045e-02 3.777515e-29 4.994501e-20 [26,] 9.375224e-01 8.830021e-04 6.159457e-02 5.186896e-48 6.250363e-30 [27,] 5.827840e-01 1.825716e-01 2.346444e-01 2.966462e-25 4.168241e-17 [28,] 2.372914e-01 7.610567e-01 1.651918e-03 8.256754e-34 3.939603e-21 [29,] 2.769602e-01 6.866467e-01 3.639305e-02 1.037380e-29 5.769723e-19 [30,] 7.693984e-01 1.876745e-01 4.292710e-02 1.312114e-21 1.040983e-11 [31,] 8.491963e-01 1.503722e-01 4.315094e-04 1.851910e-27 5.302433e-15 [32,] 1.623246e-01 8.196642e-01 1.801115e-02 1.953672e-27 3.100132e-21 [33,] 2.751474e-01 7.220864e-01 2.766206e-03 1.040937e-35 6.910028e-22 [34,] 4.905540e-01 4.918190e-01 1.762701e-02 2.771347e-36 1.028779e-16 [35,] 5.590405e-01 3.437065e-01 9.725300e-02 1.677346e-33 3.077802e-14 [36,] 8.549806e-01 1.941434e-02 1.256050e-01 1.171411e-45 1.686746e-26 [37,] 4.247355e-01 5.331352e-01 4.212933e-02 3.360076e-35 4.130742e-17 [38,] 4.754508e-01 8.434458e-03 5.161148e-01 1.581037e-50 7.345617e-29 [39,] 6.410524e-01 8.838257e-02 2.705651e-01 1.100898e-45 5.549100e-27 [40,] 3.965454e-01 5.782898e-01 2.516485e-02 2.093293e-25 1.681490e-14 [41,] 6.630425e-01 3.035036e-01 3.345389e-02 1.234511e-26 3.330340e-16 [42,] 1.164853e-01 8.266812e-01 5.683348e-02 1.021527e-23 1.544885e-17 [43,] 2.520643e-03 9.962807e-01 1.198628e-03 4.843248e-20 6.309507e-11 [44,] 8.268247e-01 1.107896e-01 6.238563e-02 3.955894e-27 3.225445e-19 [45,] 4.780680e-01 4.766383e-01 4.529363e-02 2.594782e-26 2.261090e-16 [46,] 2.300108e-01 2.249613e-01 3.876296e-01 4.383089e-04 4.396750e-06 [47,] 1.753822e-01 7.920173e-01 3.260047e-02 4.892457e-28 1.824385e-14 [48,] 1.364471e-01 8.556956e-01 7.857217e-03 1.570199e-29 7.109618e-13 [49,] 5.580230e-01 3.626619e-01 7.931509e-02 5.795813e-26 8.529929e-16 [50,] 8.517568e-01 1.419852e-01 6.257997e-03 4.329693e-37 3.336016e-10 [51,] 4.270538e-02 9.345840e-01 2.271057e-02 4.878851e-24 4.488238e-18 [52,] 2.171224e-01 7.766203e-01 6.257358e-03 3.068913e-37 2.668963e-26 [53,] 9.000572e-01 9.478541e-02 5.157398e-03 1.027809e-25 4.080406e-13 [54,] 6.306825e-04 9.972213e-01 2.148066e-03 4.209149e-14 5.675756e-15 [55,] 8.300680e-01 1.697747e-01 1.573172e-04 2.161171e-35 3.383956e-19 [56,] 1.065974e-01 2.188568e-01 6.745458e-01 2.142745e-24 1.821426e-11 [57,] 1.026759e-24 1.000000e+00 1.042691e-22 1.434674e-15 1.953024e-21 [58,] 1.376026e-21 9.999964e-01 3.652740e-22 3.431457e-06 1.690941e-07 [59,] 1.665215e-21 1.000000e+00 9.643215e-23 1.059942e-18 4.103602e-16 [60,] 6.474137e-02 9.322725e-01 2.986163e-03 1.325612e-38 3.857639e-24 [61,] 5.604732e-01 4.147048e-01 2.482197e-02 1.853990e-32 1.322660e-20 [62,] 1.807011e-01 8.093948e-01 9.904137e-03 2.917869e-34 7.795923e-22 [63,] 1.140912e-01 8.458253e-01 4.008351e-02 1.651420e-21 2.232813e-13 [64,] 1.724807e-01 8.156953e-01 1.182396e-02 2.223780e-26 1.858353e-16 [65,] 1.297877e-01 8.596520e-01 1.056035e-02 1.670772e-21 5.393304e-13 [66,] 4.679851e-01 2.635539e-01 2.269618e-01 7.984387e-08 4.075019e-02 [67,] 1.429135e-11 9.934922e-01 4.297393e-12 6.365167e-03 1.426139e-04 [68,] 5.552222e-24 9.999736e-01 9.657733e-22 2.639513e-05 3.265474e-08 [69,] 8.759584e-02 8.892153e-01 2.318888e-02 7.183614e-43 6.840859e-27 [70,] 1.039412e-01 8.924684e-01 3.590471e-03 2.964321e-27 1.815938e-17 [71,] 2.088811e-01 7.868854e-01 4.233529e-03 6.392314e-26 2.437970e-15 [72,] 6.409903e-02 9.310595e-01 4.841511e-03 5.368684e-22 2.208026e-13 [73,] 6.943443e-01 3.034752e-01 2.180500e-03 1.395501e-32 2.395437e-19 [74,] 5.287941e-02 9.438500e-01 3.270599e-03 2.883140e-42 1.729086e-28 [75,] 6.750609e-01 2.827290e-01 4.221012e-02 1.179169e-37 7.071389e-16 [76,] 4.334397e-01 4.292227e-01 1.373376e-01 4.929710e-27 4.232530e-17 [77,] 1.666241e-06 9.987147e-01 1.283659e-03 3.568388e-29 2.461643e-30 [78,] 4.890586e-01 2.640282e-01 2.469132e-01 2.744420e-26 3.281015e-13 [79,] 6.704909e-01 4.051713e-02 2.889920e-01 6.045287e-46 9.504458e-23 [80,] 5.304855e-01 3.035066e-01 1.660079e-01 1.334843e-28 9.845356e-20 [81,] 6.121450e-01 3.219556e-01 6.589939e-02 3.176632e-28 8.719793e-17 [82,] 6.214480e-01 8.539350e-02 2.931585e-01 4.568038e-27 2.790604e-15 [83,] 1.845340e-01 1.703224e-01 6.451436e-01 9.860418e-23 4.285253e-13 [84,] 2.683783e-01 9.356460e-02 6.380571e-01 1.232616e-22 3.226057e-13 [85,] 8.555020e-04 2.664486e-01 1.929998e-04 1.261338e-01 6.063692e-01 [86,] 4.282551e-03 9.952125e-01 5.045227e-04 3.983209e-09 3.757802e-07 [87,] 9.608246e-03 9.895959e-01 7.890127e-04 3.884272e-09 6.866038e-06 [88,] 5.297085e-24 9.990771e-01 5.237133e-25 7.681691e-04 1.537240e-04 [89,] 3.073437e-32 7.096062e-04 1.040826e-32 9.992904e-01 1.207384e-16 [90,] 6.064798e-27 1.974883e-01 1.302125e-27 8.025107e-01 9.678712e-07 [91,] 4.774360e-112 1.518566e-53 4.969023e-112 1.000000e+00 7.887416e-136 [92,] 3.987711e-110 1.556075e-53 4.686555e-111 1.000000e+00 4.175946e-133 [93,] 3.908048e-26 9.998106e-01 6.585936e-25 1.894266e-04 5.063619e-09 [94,] 4.410662e-06 9.997903e-01 3.733018e-07 2.448844e-05 1.804685e-04 [95,] 1.228088e-18 9.999580e-01 1.171121e-19 1.837695e-10 4.203813e-05 [96,] 1.959985e-04 1.432299e-02 4.410501e-04 4.804128e-17 9.850400e-01 [97,] 3.166256e-01 5.769406e-01 9.913547e-02 5.156899e-20 7.298336e-03 [98,] 3.011084e-08 4.963604e-06 5.128972e-08 2.040370e-17 9.999950e-01 [99,] 1.959730e-13 1.470198e-09 6.963252e-15 9.280404e-19 9.998781e-01 [100,] 8.363537e-38 2.917121e-13 2.398084e-37 2.246127e-10 4.688480e-06 [101,] 1.607742e-18 9.999976e-01 5.268664e-19 3.616514e-09 2.425131e-06 [102,] 7.932825e-38 5.715320e-26 4.673005e-39 3.077542e-26 7.011502e-19 [103,] 9.096553e-11 3.823124e-11 1.832824e-08 9.999983e-01 2.303837e-07 [104,] 8.733289e-14 6.686400e-11 3.041825e-14 6.739354e-19 1.000000e+00 [105,] 2.690559e-60 5.544491e-36 5.192720e-60 9.011074e-29 1.181790e-18 [106,] 2.859790e-48 5.559433e-27 7.412670e-50 1.314220e-26 9.132129e-18 [107,] 6.479816e-59 3.351955e-33 9.854226e-59 7.876187e-26 2.612128e-18 [108,] 7.298235e-53 3.272378e-30 4.791364e-54 1.694163e-23 9.299349e-16 [109,] 4.262040e-45 9.086840e-17 7.452318e-49 3.963781e-08 1.661014e-56 [110,] 4.049304e-57 1.206816e-34 2.095912e-58 2.697895e-27 8.514974e-18 [111,] 4.454225e-41 7.110247e-15 5.472579e-44 1.155688e-06 1.723629e-31 [112,] 1.456595e-59 6.840055e-34 1.905352e-58 7.607479e-26 1.391740e-17 [113,] 3.928614e-50 4.998532e-29 7.658581e-52 9.586074e-27 2.056520e-18 [114,] 4.297818e-51 1.365270e-29 4.565411e-53 4.335978e-29 9.191058e-20 7 [1,] 6.161128e-27 [2,] 2.028012e-10 [3,] 3.334871e-12 [4,] 1.159745e-18 [5,] 4.586444e-11 [6,] 3.503778e-08 [7,] 2.610774e-15 [8,] 6.105645e-17 [9,] 3.371950e-16 [10,] 2.411065e-15 [11,] 7.882821e-15 [12,] 1.972559e-20 [13,] 5.505233e-18 [14,] 1.423388e-18 [15,] 1.601836e-16 [16,] 3.551863e-13 [17,] 6.583578e-12 [18,] 8.018274e-16 [19,] 4.462883e-17 [20,] 9.581643e-14 [21,] 4.114617e-15 [22,] 5.397520e-16 [23,] 4.526640e-11 [24,] 1.321713e-13 [25,] 3.124958e-14 [26,] 1.395352e-20 [27,] 1.064461e-12 [28,] 1.071516e-12 [29,] 4.091035e-12 [30,] 8.998525e-11 [31,] 2.488255e-13 [32,] 5.732355e-14 [33,] 3.057154e-13 [34,] 4.770652e-18 [35,] 1.028457e-16 [36,] 1.526712e-20 [37,] 1.295417e-18 [38,] 1.118023e-23 [39,] 5.436258e-21 [40,] 1.358342e-13 [41,] 1.542504e-13 [42,] 2.485264e-14 [43,] 5.873805e-20 [44,] 1.152332e-10 [45,] 2.978767e-14 [46,] 1.569556e-01 [47,] 7.144200e-18 [48,] 1.047388e-18 [49,] 3.970811e-12 [50,] 3.772977e-27 [51,] 3.033431e-15 [52,] 3.148329e-15 [53,] 1.935829e-14 [54,] 4.280703e-18 [55,] 3.545771e-15 [56,] 1.398073e-14 [57,] 2.136146e-27 [58,] 1.016223e-14 [59,] 5.226377e-32 [60,] 2.779445e-13 [61,] 1.459130e-14 [62,] 3.497939e-16 [63,] 1.087462e-13 [64,] 5.528348e-15 [65,] 3.886658e-13 [66,] 7.489047e-04 [67,] 5.601745e-13 [68,] 1.593866e-22 [69,] 1.641368e-19 [70,] 3.080449e-16 [71,] 7.139489e-15 [72,] 2.089707e-15 [73,] 1.268861e-15 [74,] 2.977076e-18 [75,] 4.418073e-20 [76,] 2.295933e-15 [77,] 8.959420e-29 [78,] 6.298321e-17 [79,] 6.176675e-21 [80,] 8.035922e-17 [81,] 1.047143e-15 [82,] 2.521562e-14 [83,] 2.287525e-14 [84,] 4.583366e-14 [85,] 1.818963e-12 [86,] 1.960012e-18 [87,] 8.403457e-17 [88,] 1.017932e-06 [89,] 1.203278e-16 [90,] 2.398002e-09 [91,] 3.458300e-12 [92,] 3.176653e-09 [93,] 2.296495e-19 [94,] 5.116145e-09 [95,] 2.966925e-12 [96,] 3.996342e-19 [97,] 8.498586e-15 [98,] 6.967084e-20 [99,] 1.219468e-04 [100,] 9.999953e-01 [101,] 1.392851e-10 [102,] 1.000000e+00 [103,] 1.415608e-06 [104,] 4.861029e-09 [105,] 1.000000e+00 [106,] 1.000000e+00 [107,] 1.000000e+00 [108,] 1.000000e+00 [109,] 1.000000e+00 [110,] 1.000000e+00 [111,] 9.999988e-01 [112,] 1.000000e+00 [113,] 1.000000e+00 [114,] 1.000000e+00 > confusion(glass.mda, glass.test) true object 1 2 3 5 6 7 1 26 10 7 0 0 0 2 11 23 1 6 2 0 3 2 2 2 0 0 0 5 0 0 0 4 0 1 6 0 0 0 1 3 1 7 0 0 0 0 2 10 attr(,"error") [1] 0.4035088 > > > > cleanEx(); ..nameEx <- "softmax" > > ### * softmax > > flush(stderr()); flush(stdout()) > > ### Name: softmax > ### Title: Find the Maximum in Each Row of a Matrix > ### Aliases: softmax > ### Keywords: utilities > > ### ** Examples > > data(iris) > irisfit <- fda(Species ~ ., data = iris) > posteriors <- predict(irisfit, type = "post") > confusion(softmax(posteriors), iris[, "Species"]) true object setosa versicolor virginica setosa 50 0 0 versicolor 0 48 1 virginica 0 2 49 attr(,"error") [1] 0.02 > > > > ### *