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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("klaR-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('klaR') Loading required package: MASS > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "B3" > > ### * B3 > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: B3 > ### Title: West German Business Cycles 1955-1994 > ### Aliases: B3 > ### Keywords: datasets > > ### ** Examples > > data(B3) > summary(B3) PHASEN BSP91JW CP91JW DEFRATE EWAJW 1:59 Min. :-3.780 Min. :-2.38 Min. :-11.020 Min. :-4.1400 2:24 1st Qu.: 1.580 1st Qu.: 1.89 1st Qu.: -2.800 1st Qu.: 0.2800 3:47 Median : 3.590 Median : 3.64 Median : -0.760 Median : 1.1500 4:27 Mean : 3.499 Mean : 3.86 Mean : -1.168 Mean : 0.9616 3rd Qu.: 5.170 3rd Qu.: 5.56 3rd Qu.: 0.190 3rd Qu.: 1.9400 Max. :11.120 Max. :12.66 Max. : 3.220 Max. : 5.7000 EXIMRATE GM1JW IAU91JW IB91JW Min. :-1.570 Min. :-1.600 Min. :-19.950 Min. :-21.590 1st Qu.: 1.790 1st Qu.: 6.160 1st Qu.: -1.250 1st Qu.: -1.160 Median : 3.080 Median : 8.670 Median : 5.300 Median : 2.600 Mean : 3.372 Mean : 8.621 Mean : 3.993 Mean : 2.565 3rd Qu.: 4.600 3rd Qu.:11.170 3rd Qu.: 9.100 3rd Qu.: 5.550 Max. : 7.870 Max. :27.840 Max. : 27.250 Max. : 40.250 LSTKJW PBSPJW PCPJW ZINSK Min. :-2.530 Min. :-0.250 Min. :-1.130 Min. : 2.790 1st Qu.: 1.710 1st Qu.: 2.590 1st Qu.: 1.970 1st Qu.: 4.100 Median : 3.870 Median : 3.580 Median : 3.100 Median : 5.290 Mean : 4.049 Mean : 3.718 Mean : 3.213 Mean : 6.092 3rd Qu.: 5.760 3rd Qu.: 4.570 3rd Qu.: 4.190 3rd Qu.: 7.740 Max. :14.210 Max. : 8.300 Max. : 7.410 Max. :14.170 ZINSLR Min. :-0.52 1st Qu.: 3.00 Median : 3.70 Mean : 3.65 3rd Qu.: 4.62 Max. : 7.34 > > > > cleanEx(); ..nameEx <- "EDAM" > > ### * EDAM > > flush(stderr()); flush(stdout()) > > ### Name: EDAM > ### Title: Computation of an Eight Direction Arranged Map > ### Aliases: EDAM > ### Keywords: multivariate > > ### ** Examples > > # Compute an Eight Directions Arranged Map for a random sample > # of the iris data. > data(iris) > set.seed(1234) > iris.sample <- sample(150, 42) > irisEDAM <- EDAM(iris[iris.sample, 1:4], classes = iris[iris.sample, 5], + standardize = TRUE, iter.max = 3) 1 / 18 2 / 18 0.5410866 3 / 18 0.5206556 4 / 18 0.5691 5 / 18 0.5780345 6 / 18 0.609391 7 / 18 0.6170824 8 / 18 0.6399899 9 / 18 0.646767 10 / 18 0.6470014 11 / 16 0.6470014 > plot(irisEDAM, vertices = FALSE) > legend(3, 5, col = rainbow(3), legend = levels(iris[,5]), pch = 16) > print(irisEDAM) $preimages Sepal.Length Sepal.Width Petal.Length Petal.Width 114 5.7 2.5 5.0 2.0 73 6.3 2.5 4.9 1.5 88 6.3 2.3 4.4 1.3 90 5.5 2.5 4.0 1.3 61 5.0 2.0 3.5 1.0 34 5.5 4.2 1.4 0.2 112 6.4 2.7 5.3 1.9 127 6.2 2.8 4.8 1.8 102 5.8 2.7 5.1 1.9 91 5.5 2.6 4.4 1.2 60 5.2 2.7 3.9 1.4 42 4.5 2.3 1.3 0.3 133 6.4 2.8 5.6 2.2 55 6.5 2.8 4.6 1.5 98 6.2 2.9 4.3 1.3 93 5.8 2.6 4.0 1.2 2 4.9 3.0 1.4 0.2 39 4.4 3.0 1.3 0.2 113 6.8 3.0 5.5 2.1 76 6.6 3.0 4.4 1.4 92 6.1 3.0 4.6 1.4 95 5.6 2.7 4.2 1.3 31 4.8 3.1 1.6 0.2 30 4.7 3.2 1.6 0.2 125 6.7 3.3 5.7 2.1 138 6.4 3.1 5.5 1.8 71 5.9 3.2 4.8 1.8 32 5.4 3.4 1.5 0.4 40 5.1 3.4 1.5 0.2 36 5.0 3.2 1.2 0.2 126 7.2 3.2 6.0 1.8 149 6.2 3.4 5.4 2.3 66 6.7 3.1 4.4 1.4 21 5.4 3.4 1.7 0.2 18 5.1 3.5 1.4 0.3 25 4.8 3.4 1.9 0.2 136 7.7 3.0 6.1 2.3 137 6.3 3.4 5.6 2.4 6 5.4 3.9 1.7 0.4 22 5.1 3.7 1.5 0.4 28 5.2 3.5 1.5 0.2 23 4.6 3.6 1.0 0.2 $Z [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 2 3 4 5 6 [2,] 7 8 9 10 11 12 [3,] 13 14 15 16 17 18 [4,] 19 20 21 22 23 24 [5,] 25 26 27 28 29 30 [6,] 31 32 33 34 35 36 [7,] 37 38 39 40 41 42 $Z.old.terms [,1] [,2] [,3] [,4] [,5] [,6] [1,] 16 10 36 40 41 8 [2,] 39 14 26 3 34 21 [3,] 33 31 11 2 7 17 [4,] 28 12 4 9 20 38 [5,] 29 15 42 32 13 18 [6,] 5 6 27 23 1 19 [7,] 22 30 24 35 25 37 $cl.ord [1] virginica versicolor versicolor versicolor versicolor setosa [7] virginica virginica virginica versicolor versicolor setosa [13] virginica versicolor versicolor versicolor setosa setosa [19] virginica versicolor versicolor versicolor setosa setosa [25] virginica virginica versicolor setosa setosa setosa [31] virginica virginica versicolor setosa setosa setosa [37] virginica virginica setosa setosa setosa setosa Levels: setosa versicolor virginica $S [,1] [1,] 0.6470031 attr(,"class") [1] "EDAM" > > # Construct clusters within the phases of the german business data > # and visualize the centroids by EDAM. > data(B3) > phasemat <- lapply(1:4, function(x) B3[B3[,1] == x, 2:14]) > subclasses <- lapply(phasemat, + function(x) cutree(hclust(dist(x)), k = round(nrow(x) / 4.47))) > centroids <- lapply(1:4, + function(y) apply(phasemat[[y]], 2, + function(x) by(x, subclasses[[y]], mean))) > centmat <- matrix(unlist(sapply(centroids, t)), ncol = 13, + byrow = TRUE, dimnames = list(NULL, colnames(centroids[[1]]))) > centclasses <- unlist(lapply(1:4, + function(x) rep(x, unlist(lapply(centroids, nrow))[x]))) > B3EDAM <- EDAM(centmat, classes = centclasses, standardize = TRUE, + iter.max = 6, rand = FALSE) 1 / 36 2 / 36 0.5503169 3 / 36 0.5782196 4 / 36 0.5766633 5 / 36 0.5894002 6 / 36 0.5661183 7 / 36 0.5744937 8 / 36 0.6509185 9 / 36 0.6455015 10 / 36 0.6693491 11 / 36 0.6878151 12 / 36 0.6966163 13 / 36 0.6975969 14 / 36 0.6994951 15 / 36 0.6996743 16 / 33 0.6996743 17 / 28 0.6996743 > plot(B3EDAM, standardize = TRUE) > opar <- par(xpd = NA) > legend(4, 5.1, col = rainbow(4), pch = 16, xjust = 0.5, yjust = 0, + ncol = 2, legend = c("upswing", "upper turning point", + "downswing", "lower turning point")) > print(B3EDAM) $preimages BSP91JW CP91JW DEFRATE EWAJW EXIMRATE GM1JW [1,] 10.0400000 6.1700000 1.9600000 1.1200000 2.400000 8.1000000 [2,] 6.1625000 6.7200000 0.0175000 1.0750000 3.795000 12.6525000 [3,] 8.3900000 5.6200000 0.0400000 3.2000000 2.900000 6.6200000 [4,] 7.3900000 8.7200000 1.0200000 2.9900000 2.360000 6.9900000 [5,] 9.0250000 8.7050000 0.6500000 4.2000000 2.900000 10.2550000 [6,] 5.1100000 5.1500000 -2.8000000 2.7900000 6.990000 2.8300000 [7,] 4.9650000 4.0100000 -2.6800000 1.3200000 6.600000 9.7600000 [8,] 8.5325000 5.3275000 0.1900000 2.5850000 3.382500 12.4375000 [9,] 7.4450000 7.9183333 1.2283333 2.6250000 2.948333 7.8200000 [10,] 7.4250000 8.8150000 -1.5950000 3.8700000 1.260000 6.2400000 [11,] 2.4192857 2.8328571 -1.4964286 0.9785714 5.100714 7.6600000 [12,] 3.9462500 3.3037500 0.0425000 1.2100000 4.281250 6.4462500 [13,] 5.1966667 4.3177778 -1.7744444 1.0677778 2.688889 9.9822222 [14,] 4.1200000 5.8822222 0.8833333 1.3222222 1.157778 10.1344444 [15,] 7.8950000 6.7500000 0.4700000 3.6350000 2.595000 9.4400000 [16,] 2.1900000 0.5966667 -2.0566667 -1.0666667 5.706667 10.7933333 [17,] 2.5200000 5.2433333 -2.3266667 0.1700000 6.156667 10.2500000 [18,] 3.6150000 3.3700000 -1.3680000 0.9510000 2.430000 8.3680000 [19,] 4.7740000 4.7000000 -0.8950000 1.9170000 2.208000 9.0190000 [20,] 5.8400000 7.3800000 1.5000000 2.3500000 1.660000 6.9800000 [21,] 1.4800000 1.7800000 -0.5100000 0.2200000 2.360000 3.1200000 [22,] 1.2328571 1.4757143 -2.7028571 -1.8357143 3.722857 10.5057143 [23,] 3.7750000 4.4125000 -3.4125000 0.6775000 3.380000 12.3600000 [24,] 3.5000000 4.2430000 -0.2990000 1.7180000 4.225000 7.5170000 [25,] 5.3466667 7.7633333 -0.1766667 2.3866667 2.200000 6.2433333 [26,] 0.0200000 -0.0700000 -4.0200000 0.7000000 3.330000 3.7300000 [27,] -0.8883333 -0.1566667 -0.9966667 -2.2083333 2.928333 2.8350000 [28,] 1.0650000 0.5200000 -1.9833333 1.4300000 0.975000 2.4200000 [29,] 2.5700000 4.4250000 1.3950000 0.8700000 2.325000 12.9400000 [30,] 2.7425000 5.5025000 -0.3025000 1.2475000 1.842500 13.0475000 [31,] 6.5450000 6.1775000 -4.2200000 3.3200000 6.602500 23.5700000 [32,] -2.3025000 0.1850000 -2.8775000 -1.7225000 7.220000 7.4100000 [33,] 0.3550000 -0.2900000 -3.1166667 -0.3900000 2.461667 0.5283333 [34,] -1.6000000 1.9500000 -4.1160000 -2.1840000 3.458000 11.6040000 [35,] -1.4000000 2.7100000 1.9400000 0.8900000 0.900000 8.0400000 IAU91JW IB91JW LSTKJW PBSPJW PCPJW ZINSK ZINSLR [1,] 5.9300000 40.2500000 0.9600000 2.360000 2.1100000 3.400000 3.7000000 [2,] 8.2225000 11.6250000 0.7250000 1.105000 0.5425000 3.172500 4.8600000 [3,] -3.7600000 14.5800000 1.0300000 1.950000 1.6500000 4.960000 4.9500000 [4,] 27.2500000 1.1300000 2.3800000 3.340000 1.5700000 4.000000 3.2500000 [5,] 23.0000000 11.3800000 3.6600000 3.460000 2.0000000 6.285000 3.1900000 [6,] 16.8300000 7.3800000 1.4900000 3.090000 2.4400000 8.200000 5.4800000 [7,] 5.8600000 14.8900000 0.4850000 1.700000 1.7950000 4.725000 4.5700000 [8,] 17.8850000 9.5750000 0.7350000 2.542500 1.7075000 4.122500 3.6525000 [9,] 15.5716667 4.4733333 5.8316667 3.846667 1.8933333 5.218333 2.5900000 [10,] 9.5100000 22.3900000 3.2400000 3.720000 3.8500000 6.815000 3.6950000 [11,] 4.8378571 -0.1050000 1.7557143 2.195000 0.8692857 4.587143 4.0871429 [12,] 12.2587500 1.3700000 1.8400000 2.782500 2.5437500 6.202500 4.5262500 [13,] 7.6688889 4.5355556 2.6711111 3.258889 2.6300000 3.952222 3.4066667 [14,] 6.8233333 3.9111111 6.4377778 4.095556 3.4333333 4.301111 2.4444444 [15,] 11.8150000 18.6200000 6.7750000 5.505000 4.0900000 6.780000 1.5800000 [16,] -0.6500000 3.2266667 -0.8133333 1.506667 2.2633333 4.563333 5.5800000 [17,] -9.1700000 2.5300000 2.6966667 3.290000 2.4566667 6.686667 3.4666667 [18,] 0.9430000 5.2770000 4.1060000 3.577000 3.2060000 4.621000 3.4980000 [19,] 6.3370000 5.8630000 4.7270000 4.338000 4.0720000 7.099000 3.4370000 [20,] 20.0600000 -2.9100000 12.3400000 6.170000 3.1900000 9.400000 1.1000000 [21,] -10.4200000 0.9000000 0.5900000 2.270000 2.9300000 5.940000 5.7300000 [22,] 1.9085714 0.5042857 0.5500000 3.055714 3.3142857 5.298571 4.6714286 [23,] 8.4225000 -3.4675000 2.9875000 3.920000 3.4550000 3.837500 2.7700000 [24,] -0.6340000 2.5890000 5.3150000 4.071000 3.6840000 7.768000 3.6960000 [25,] 14.0266667 7.9733333 13.1100000 7.566667 3.5800000 9.233333 0.3233333 [26,] 7.8800000 -16.2000000 1.3100000 1.640000 2.3400000 6.050000 5.8000000 [27,] -11.8233333 -7.1150000 3.6950000 3.458333 3.8683333 6.716667 4.9033333 [28,] -0.2566667 -4.1616667 8.7966667 5.455000 6.1250000 11.236667 3.8033333 [29,] 6.0150000 -6.6750000 6.9050000 5.090000 3.7450000 3.415000 1.4200000 [30,] 0.6950000 5.7900000 8.9175000 7.197500 5.2875000 6.427500 0.9475000 [31,] 12.7075000 2.7300000 1.1600000 3.362500 3.1825000 8.892500 5.5075000 [32,] -17.6050000 -0.8950000 3.3225000 3.195000 3.2450000 7.295000 3.1750000 [33,] -8.3000000 -3.4950000 5.0616667 4.393333 5.8050000 10.930000 5.6516667 [34,] -5.5220000 -9.8380000 8.7380000 6.814000 6.6660000 6.792000 2.7200000 [35,] -2.8400000 -21.5900000 6.4600000 4.150000 3.2700000 3.400000 2.0100000 $Z [,1] [,2] [,3] [,4] [,5] [1,] 1 2 3 4 5 [2,] 6 7 8 9 10 [3,] 11 12 13 14 15 [4,] 16 17 18 19 20 [5,] 21 22 23 24 25 [6,] 26 27 28 29 30 [7,] 31 32 33 34 35 $Z.old.terms [,1] [,2] [,3] [,4] [,5] [1,] 3 30 21 7 14 [2,] 13 12 1 16 15 [3,] 10 6 4 24 19 [4,] 5 22 2 17 26 [5,] 9 33 8 20 27 [6,] 11 32 28 23 34 [7,] 18 29 25 35 31 $cl.ord [1] 1 4 3 1 2 1 1 1 2 2 1 1 1 3 3 1 3 1 2 3 1 4 1 3 3 1 4 3 3 4 2 3 3 4 4 $S [,1] [1,] 0.6996743 attr(,"class") [1] "EDAM" > par(opar) > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "NaiveBayes" > > ### * NaiveBayes > > flush(stderr()); flush(stdout()) > > ### Name: NaiveBayes > ### Title: Naive Bayes Classifier > ### Aliases: NaiveBayes NaiveBayes.default NaiveBayes.formula > ### Keywords: classif category > > ### ** Examples > > data(iris) > m <- NaiveBayes(Species ~ ., data = iris) > > > > cleanEx(); ..nameEx <- "TopoS" > > ### * TopoS > > flush(stderr()); flush(stdout()) > > ### Name: TopoS > ### Title: Computation of criterion S of a visualization > ### Aliases: TopoS > ### Keywords: internal > > ### ** Examples > > # Compute S for the MDS visualization of the german business data > data(B3) > plot(cmdscale(dist(B3[, 2:14])), col = rainbow(4)[B3[, 1]], pch = 16) > TopoS(dist(B3[, 2:14]), dist(cmdscale(dist(B3[, 2:14])))) [,1] [1,] 0.7979349 > > > > cleanEx(); ..nameEx <- "b.scal" > > ### * b.scal > > flush(stderr()); flush(stdout()) > > ### Name: b.scal > ### Title: Calculation of beta scaling parameters > ### Aliases: b.scal > ### Keywords: classif > > ### ** Examples > > library(MASS) > data(B3) > pB3 <- predict(lda(PHASEN ~ ., data = B3))$posterior > pbB3 <- b.scal(pB3, B3$PHASEN, dis = TRUE) > ucpm(pB3, B3$PHASEN) $CR [1] 0.8280255 $AC [1] 0.5893278 $AS [1] 0.7144436 $CF [1] 0.8051573 $CFvec 1 2 3 4 0.8523405 0.7460055 0.8039736 0.7566931 > ucpm(pbB3$member, B3$PHASEN) $CR [1] 0.8280255 $AC [1] 0.6181139 $AS [1] 0.7498357 $CF [1] 0.8285739 $CFvec 1 2 3 4 0.8255262 0.8484951 0.8216531 0.8295732 > > > > cleanEx(); ..nameEx <- "benchB3" > > ### * benchB3 > > flush(stderr()); flush(stdout()) > > ### Name: benchB3 > ### Title: Benchmarking on B3 data > ### Aliases: benchB3 > ### Keywords: classif > > ### ** Examples > > perLDA <- benchB3("lda") Error Rate in 1 th cycle: 0.667 Error Rate in 2 th cycle: 0.438 Error Rate in 3 th cycle: 0.294 Error Rate in 4 th cycle: 0.667 Error Rate in 5 th cycle: 0.344 Error Rate in 6 th cycle: 0.562 ------------------------------------------ Mean Error Rate of method lda : 0.495 > ## Not run: > ##D ## due to parameter optimization rda takes a while > ##D perRDA <- benchB3("rda") > ##D library(rpart) > ##D ## rpart will not work with prior argument: > ##D perRpart <- benchB3("rpart", prior = NULL) > ## End(Not run) > > > > cleanEx(); ..nameEx <- "betascale" > > ### * betascale > > flush(stderr()); flush(stdout()) > > ### Name: betascale > ### Title: Scale membership values according to a beta scaling > ### Aliases: betascale > ### Keywords: classif > > ### ** Examples > > library(MASS) > data(B3) > pB3 <- predict(lda(PHASEN ~ ., data = B3))$posterior > pbB3 <- b.scal(pB3, B3$PHASEN) > betascale(pbB3) 1 2 3 4 1955,4 5.014154e-07 9.999991e-01 3.855194e-07 1.642369e-11 1956,1 7.183358e-08 9.999993e-01 5.953466e-07 2.974244e-10 1956,2 1.595718e-03 9.930993e-01 5.304861e-03 1.203083e-07 1956,3 1.703715e-03 1.708619e-01 8.273429e-01 9.154481e-05 1956,4 2.155743e-04 2.893688e-02 9.706023e-01 2.452769e-04 1957,1 6.436600e-01 2.346153e-01 1.208067e-01 9.180734e-04 1957,2 7.319222e-03 3.097156e-01 6.728901e-01 1.007506e-02 1957,3 2.670387e-01 1.967233e-01 5.310581e-01 5.179947e-03 1957,4 8.462827e-01 1.010357e-01 4.651432e-02 6.167267e-03 1958,1 8.741493e-02 6.282107e-03 6.404908e-02 8.422539e-01 1958,2 5.529815e-01 3.365032e-03 4.064048e-02 4.030130e-01 1958,3 5.412534e-01 8.919177e-02 5.448755e-02 3.150673e-01 1958,4 4.384762e-01 1.188473e-01 2.189497e-01 2.237267e-01 1959,1 4.799161e-01 1.833069e-01 2.185946e-01 1.181823e-01 1959,2 8.812499e-01 8.972709e-02 3.749292e-03 2.527373e-02 1959,3 9.356997e-01 4.428727e-02 8.463310e-03 1.154975e-02 1959,4 7.641170e-01 2.206895e-01 1.413479e-02 1.058756e-03 1960,1 9.494164e-01 5.035943e-02 2.190106e-04 5.140498e-06 1960,2 1.158985e-02 9.761586e-01 1.208450e-02 1.670961e-04 1960,3 1.074515e-02 9.664170e-01 2.253780e-02 3.000409e-04 1960,4 3.822020e-02 8.110603e-01 1.503069e-01 4.125865e-04 1961,1 6.527468e-01 2.333283e-01 1.125234e-01 1.401551e-03 1961,2 8.931427e-01 2.271101e-02 7.770177e-02 6.444469e-03 1961,3 1.021002e-01 6.598029e-02 6.855524e-01 1.463671e-01 1961,4 1.791025e-01 7.168244e-02 4.548679e-01 2.943471e-01 1962,1 5.120376e-01 6.013989e-02 2.298203e-01 1.980021e-01 1962,2 3.159091e-02 1.540149e-01 7.040990e-01 1.102951e-01 1962,3 7.220796e-01 7.162993e-02 1.991405e-01 7.149980e-03 1962,4 9.412282e-02 6.271653e-02 7.677509e-01 7.540974e-02 1963,1 1.689309e-01 1.303432e-02 6.623716e-01 1.556631e-01 1963,2 6.818989e-01 1.952865e-02 2.730807e-01 2.549181e-02 1963,3 4.241694e-01 3.915606e-02 5.006827e-01 3.599183e-02 1963,4 4.574297e-01 4.688179e-02 4.934522e-01 2.236264e-03 1964,1 8.233472e-01 4.593194e-02 4.710863e-02 8.361220e-02 1964,2 9.223021e-01 4.774914e-02 1.479734e-02 1.515146e-02 1964,3 5.544424e-01 1.131490e-01 2.504403e-01 8.196832e-02 1964,4 1.037427e-01 1.603363e-01 6.896254e-01 4.629561e-02 1965,1 8.153026e-01 8.914462e-02 6.508314e-02 3.046965e-02 1965,2 1.899484e-02 3.453948e-01 6.034575e-01 3.215283e-02 1965,3 1.781036e-03 5.724659e-02 9.304759e-01 1.049648e-02 1965,4 1.031281e-02 2.369078e-01 7.463960e-01 6.383448e-03 1966,1 3.139769e-02 9.992249e-02 7.671707e-01 1.015091e-01 1966,2 8.880805e-02 5.898894e-02 7.991957e-01 5.300728e-02 1966,3 4.710444e-02 1.127649e-02 2.362872e-01 7.053319e-01 1966,4 6.472856e-02 3.119572e-03 8.247959e-01 1.073559e-01 1967,1 3.174681e-04 5.073659e-06 8.055692e-03 9.916218e-01 1967,2 1.378417e-03 3.215227e-07 1.273732e-04 9.984939e-01 1967,3 1.751924e-03 1.205967e-06 1.349550e-04 9.981119e-01 1967,4 2.324159e-01 2.060192e-04 1.676751e-04 7.672104e-01 1968,1 8.394311e-01 1.022882e-03 9.409359e-03 1.501367e-01 1968,2 8.352849e-01 1.552874e-02 1.331593e-02 1.358705e-01 1968,3 9.796411e-01 1.096433e-02 1.908329e-03 7.486259e-03 1968,4 6.642172e-01 2.241124e-01 1.113293e-01 3.412052e-04 1969,1 8.147865e-01 1.820078e-01 1.165379e-03 2.040274e-03 1969,2 8.133637e-01 1.780096e-01 5.518916e-03 3.107800e-03 1969,3 2.370172e-02 9.706877e-01 4.550733e-03 1.059896e-03 1969,4 2.247762e-02 8.320212e-01 1.419450e-01 3.556098e-03 1970,1 1.153334e-04 3.596820e-02 9.638570e-01 5.945130e-05 1970,2 4.419997e-03 1.875895e-01 8.075828e-01 4.076391e-04 1970,3 3.794012e-04 2.501646e-02 9.715523e-01 3.051855e-03 1970,4 6.265577e-04 3.972189e-02 9.579745e-01 1.677011e-03 1971,1 9.709846e-02 3.184043e-01 5.071900e-01 7.730722e-02 1971,2 2.744685e-02 8.347617e-02 1.559499e-01 7.331271e-01 1971,3 2.027992e-03 3.055750e-02 6.834724e-01 2.839421e-01 1971,4 1.739072e-02 2.313441e-02 2.786240e-01 6.808509e-01 1972,1 1.062066e-02 4.305948e-02 1.387590e-01 8.075608e-01 1972,2 3.115528e-01 5.733524e-02 2.444037e-01 3.867083e-01 1972,3 2.189233e-02 1.495615e-02 4.805527e-02 9.150962e-01 1972,4 9.876097e-03 9.838895e-02 8.649819e-01 2.675305e-02 1973,1 1.214770e-02 1.919178e-01 7.708444e-01 2.509010e-02 1973,2 1.169434e-05 4.542004e-02 9.544142e-01 1.540895e-04 1973,3 7.858449e-06 6.627907e-03 9.933450e-01 1.922155e-05 1973,4 2.754404e-05 3.058563e-03 9.969089e-01 4.994291e-06 1974,1 4.192934e-03 3.869076e-03 9.879509e-01 3.987116e-03 1974,2 1.269226e-02 3.923895e-04 1.104168e-01 8.764986e-01 1974,3 2.421562e-05 1.218537e-05 1.306470e-03 9.986571e-01 1974,4 1.321823e-04 6.613344e-06 1.847574e-03 9.980136e-01 1975,1 1.946128e-05 9.024189e-07 1.292183e-04 9.998504e-01 1975,2 2.635512e-06 7.525989e-08 4.218418e-07 9.999969e-01 1975,3 6.968653e-06 4.027782e-07 5.684657e-06 9.999869e-01 1975,4 5.616937e-04 2.102627e-05 4.205825e-06 9.994131e-01 1976,1 4.794971e-01 5.382958e-02 3.084343e-03 4.635890e-01 1976,2 9.177093e-01 6.132467e-03 9.588680e-04 7.519938e-02 1976,3 8.099615e-01 6.410776e-03 5.201946e-03 1.784258e-01 1976,4 9.776122e-01 1.266836e-02 6.862839e-03 2.856624e-03 1977,1 9.019857e-01 2.812752e-02 3.941813e-02 3.046862e-02 1977,2 5.197071e-01 5.613536e-02 3.448978e-01 7.925978e-02 1977,3 3.457600e-01 1.773582e-01 3.905767e-01 8.630510e-02 1977,4 7.841774e-01 8.548268e-02 1.107870e-01 1.955289e-02 1978,1 7.808533e-01 8.754624e-02 1.126841e-01 1.891628e-02 1978,2 9.074437e-01 4.738616e-02 2.878394e-02 1.638621e-02 1978,3 9.259672e-01 2.270463e-02 9.743048e-03 4.158511e-02 1978,4 9.349595e-01 2.470276e-02 1.975713e-02 2.058058e-02 1979,1 9.781730e-01 1.526221e-02 3.887327e-03 2.677499e-03 1979,2 1.196620e-01 8.504640e-01 2.665135e-02 3.222726e-03 1979,3 3.642960e-01 3.392897e-01 2.925240e-01 3.890284e-03 1979,4 2.776067e-02 3.744978e-01 5.976564e-01 8.506791e-05 1980,1 9.035010e-03 9.545767e-01 3.606713e-02 3.211576e-04 1980,2 7.695798e-02 2.160242e-02 9.011105e-01 3.291410e-04 1980,3 5.248378e-04 9.984865e-03 9.893746e-01 1.156847e-04 1980,4 3.392699e-03 9.395447e-03 9.849166e-01 2.295223e-03 1981,1 3.123086e-03 2.545341e-02 9.691214e-01 2.302106e-03 1981,2 6.159826e-04 3.246166e-02 9.576522e-01 9.270186e-03 1981,3 4.816257e-03 5.046552e-02 8.989870e-01 4.573120e-02 1981,4 2.642717e-01 3.868462e-02 6.371894e-01 5.985427e-02 1982,1 1.096190e-01 1.684420e-02 7.114668e-01 1.620700e-01 1982,2 3.424503e-02 7.892553e-03 2.170790e-01 7.407834e-01 1982,3 8.299636e-02 3.076253e-03 2.245552e-01 6.893722e-01 1982,4 3.206511e-01 1.003029e-03 3.857212e-02 6.397737e-01 1983,1 2.845797e-01 2.387063e-03 8.801506e-03 7.042317e-01 1983,2 1.702476e-03 3.099481e-05 4.714490e-05 9.982194e-01 1983,3 3.183042e-03 1.092855e-04 8.404153e-05 9.966236e-01 1983,4 8.079807e-01 6.106658e-03 7.941847e-04 1.851184e-01 1984,1 9.465186e-01 9.629882e-03 1.727964e-02 2.657189e-02 1984,2 5.248478e-01 1.918779e-02 1.138095e-01 3.421549e-01 1984,3 9.201967e-01 1.340319e-02 7.255739e-03 5.914433e-02 1984,4 9.817471e-01 4.045143e-03 1.172561e-02 2.482112e-03 1985,1 9.889213e-01 5.455609e-03 4.339461e-03 1.283653e-03 1985,2 9.906305e-01 2.981754e-03 2.680556e-03 3.707197e-03 1985,3 9.418429e-01 1.717809e-02 2.645569e-02 1.452329e-02 1985,4 9.861062e-01 4.313414e-03 2.885806e-03 6.694596e-03 1986,1 9.926962e-01 1.891084e-03 2.252639e-03 3.160047e-03 1986,2 9.331962e-01 1.672420e-02 6.703344e-03 4.337630e-02 1986,3 9.580504e-01 8.291267e-03 1.556729e-02 1.809108e-02 1986,4 9.887724e-01 2.427660e-03 2.334937e-03 6.464981e-03 1987,1 9.938723e-01 2.663928e-03 1.894847e-03 1.568960e-03 1987,2 8.827516e-01 9.441599e-03 8.321619e-02 2.459064e-02 1987,3 8.955618e-01 1.826503e-02 4.631455e-02 3.985858e-02 1987,4 8.933447e-01 1.814906e-02 4.353152e-02 4.497476e-02 1988,1 9.742552e-01 1.441170e-02 6.821835e-03 4.511296e-03 1988,2 9.909459e-01 3.496726e-03 2.710512e-03 2.846835e-03 1988,3 9.102811e-01 4.943232e-02 3.090764e-02 9.378991e-03 1988,4 9.804202e-01 1.039811e-02 7.835085e-03 1.346593e-03 1989,1 8.851688e-01 8.001400e-02 3.263700e-02 2.180234e-03 1989,2 8.528027e-01 1.153487e-01 2.823430e-02 3.614273e-03 1989,3 8.575277e-01 6.920010e-02 7.104284e-02 2.229379e-03 1989,4 6.942333e-01 2.166753e-01 8.314314e-02 5.948266e-03 1990,1 9.236377e-01 7.512688e-02 7.847904e-04 4.506135e-04 1990,2 5.904263e-01 3.214851e-01 1.668977e-02 7.139880e-02 1990,3 1.005677e-04 9.998900e-01 5.711347e-06 3.698731e-06 1990,4 7.362303e-07 9.999988e-01 4.798862e-07 2.750188e-08 1991,1 1.010347e-11 1.000000e+00 2.082209e-11 9.484747e-14 1991,2 1.018569e-07 9.999987e-01 1.026224e-06 1.416913e-07 1991,3 5.116126e-03 9.868184e-01 7.804046e-03 2.613781e-04 1991,4 6.543743e-02 7.405568e-01 1.843880e-01 9.617764e-03 1992,1 4.073831e-01 1.667372e-01 4.241553e-01 1.724358e-03 1992,2 7.776848e-02 7.472302e-02 8.216590e-01 2.584946e-02 1992,3 3.300064e-02 5.386865e-02 8.907004e-01 2.243035e-02 1992,4 1.788556e-03 1.838094e-02 9.729422e-01 6.888260e-03 1993,1 3.210058e-04 2.429690e-04 9.964040e-01 3.031993e-03 1993,2 4.331192e-04 3.493447e-04 9.490681e-01 5.014942e-02 1993,3 1.995211e-03 2.612831e-03 9.753079e-01 2.008411e-02 1993,4 6.200290e-02 9.833092e-04 9.295469e-01 7.466886e-03 1994,1 2.794576e-02 1.438001e-02 8.875093e-01 7.016495e-02 1994,2 1.394589e-01 1.702181e-03 1.501181e-02 8.438271e-01 1994,3 8.297551e-01 2.914739e-03 4.310269e-03 1.630199e-01 1994,4 7.222472e-01 1.759210e-03 1.579107e-03 2.744145e-01 > > > > cleanEx(); ..nameEx <- "calc.trans" > > ### * calc.trans > > flush(stderr()); flush(stdout()) > > ### Name: calc.trans > ### Title: Calculation of transition probabilities > ### Aliases: calc.trans > ### Keywords: ts > > ### ** Examples > > data(B3) > calc.trans(B3$PHASEN) 1 2 3 4 1 0.8965517 0.1034483 0.0000000 0.0000000 2 0.0000000 0.7083333 0.2916667 0.0000000 3 0.0000000 0.0000000 0.8510638 0.1489362 4 0.2592593 0.0000000 0.0000000 0.7407407 > > > > cleanEx(); ..nameEx <- "centerlines" > > ### * centerlines > > flush(stderr()); flush(stdout()) > > ### Name: centerlines > ### Title: Lines from classborders to the center > ### Aliases: centerlines > ### Keywords: classif dplot > > ### ** Examples > > centerlines(3) [,1] [,2] [,3] [1,] 0.3333333 0.3333333 0.3333333 [2,] 0.5000000 0.5000000 0.0000000 [3,] NA NA NA [4,] 0.3333333 0.3333333 0.3333333 [5,] 0.5000000 0.0000000 0.5000000 [6,] NA NA NA [7,] 0.3333333 0.3333333 0.3333333 [8,] 0.0000000 0.5000000 0.5000000 [9,] NA NA NA > centerlines(4) [,1] [,2] [,3] [,4] [1,] 0.25 0.25 0.25 0.25 [2,] 0.50 0.50 0.00 0.00 [3,] NA NA NA NA [4,] 0.25 0.25 0.25 0.25 [5,] 0.50 0.00 0.50 0.00 [6,] NA NA NA NA [7,] 0.25 0.25 0.25 0.25 [8,] 0.00 0.50 0.50 0.00 [9,] NA NA NA NA [10,] 0.25 0.25 0.25 0.25 [11,] 0.50 0.00 0.00 0.50 [12,] NA NA NA NA [13,] 0.25 0.25 0.25 0.25 [14,] 0.00 0.50 0.00 0.50 [15,] NA NA NA NA [16,] 0.25 0.25 0.25 0.25 [17,] 0.00 0.00 0.50 0.50 [18,] NA NA NA NA > > > > cleanEx(); ..nameEx <- "classscatter" > > ### * classscatter > > flush(stderr()); flush(stdout()) > > ### Name: classscatter > ### Title: Classification scatterplot matrix > ### Aliases: classscatter > ### Keywords: dplot classif > > ### ** Examples > > data(B3) > library(MASS) > classscatter(PHASEN ~ BSP91JW + EWAJW + LSTKJW, + data = B3, method = "lda") [1] 0.3184713 > > > > cleanEx(); ..nameEx <- "countries" > > ### * countries > > flush(stderr()); flush(stdout()) > > ### Name: countries > ### Title: Socioeconomic data for the most populous countries. > ### Aliases: countries > ### Keywords: datasets > > ### ** Examples > > data(countries) > summary(countries) Country Popul PopDens GDPpp Afghanistan: 1 Min. :2.309e+07 Min. : 3.226 Min. : 568.4 Algeria : 1 1st Qu.:3.824e+07 1st Qu.: 35.439 1st Qu.: 2323.3 Argentina : 1 Median :5.905e+07 Median : 70.498 Median : 5318.8 Bangladesh : 1 Mean :1.272e+08 Mean :128.103 Mean : 9218.4 Brazil : 1 3rd Qu.:9.984e+07 3rd Qu.:142.223 3rd Qu.: 9937.5 Canada : 1 Max. :1.287e+09 Max. :961.446 Max. :35992.0 (Other) :36 LifeEx InfMor Illit Min. :41.24 Min. : 0.330 Min. : 0.20 1st Qu.:62.55 1st Qu.: 1.687 1st Qu.: 2.30 Median :70.47 Median : 3.128 Median :10.10 Mean :67.03 Mean : 3.972 Mean :17.04 3rd Qu.:73.89 3rd Qu.: 6.273 3rd Qu.:27.95 Max. :80.93 Max. :14.248 Max. :64.00 > > > > cleanEx(); ..nameEx <- "dkernel" > > ### * dkernel > > flush(stderr()); flush(stdout()) > > ### Name: dkernel > ### Title: Estimate density of a given kernel > ### Aliases: dkernel > ### Keywords: distribution nonparametric > > ### ** Examples > > kern <- density(rnorm(50)) > x <- seq(-3, 3, len = 100) > y <- dkernel(x, kern) > plot(x, y, type = "l") > > > > cleanEx(); ..nameEx <- "e.scal" > > ### * e.scal > > flush(stderr()); flush(stdout()) > > ### Name: e.scal > ### Title: Function to calculate e- or softmax scaled membership values > ### Aliases: e.scal > ### Keywords: classif > > ### ** Examples > > library(MASS) > data(iris) > ldaobj <- lda(Species ~ ., data = iris) > ldapred <- predict(ldaobj)$posterior > e.scal(ldapred) $sv setosa versicolor virginica 1 0.5761169 0.2119416 0.2119416 2 0.5761169 0.2119416 0.2119416 3 0.5761169 0.2119416 0.2119416 4 0.5761169 0.2119416 0.2119416 5 0.5761169 0.2119416 0.2119416 6 0.5761169 0.2119416 0.2119416 7 0.5761169 0.2119416 0.2119416 8 0.5761169 0.2119416 0.2119416 9 0.5761169 0.2119416 0.2119416 10 0.5761169 0.2119416 0.2119416 11 0.5761169 0.2119416 0.2119416 12 0.5761169 0.2119416 0.2119416 13 0.5761169 0.2119416 0.2119416 14 0.5761169 0.2119416 0.2119416 15 0.5761169 0.2119416 0.2119416 16 0.5761169 0.2119416 0.2119416 17 0.5761169 0.2119416 0.2119416 18 0.5761169 0.2119416 0.2119416 19 0.5761169 0.2119416 0.2119416 20 0.5761169 0.2119416 0.2119416 21 0.5761169 0.2119416 0.2119416 22 0.5761169 0.2119416 0.2119416 23 0.5761169 0.2119416 0.2119416 24 0.5761169 0.2119416 0.2119416 25 0.5761169 0.2119416 0.2119416 26 0.5761169 0.2119416 0.2119416 27 0.5761169 0.2119416 0.2119416 28 0.5761169 0.2119416 0.2119416 29 0.5761169 0.2119416 0.2119416 30 0.5761169 0.2119416 0.2119416 31 0.5761169 0.2119416 0.2119416 32 0.5761169 0.2119416 0.2119416 33 0.5761169 0.2119416 0.2119416 34 0.5761169 0.2119416 0.2119416 35 0.5761169 0.2119416 0.2119416 36 0.5761169 0.2119416 0.2119416 37 0.5761169 0.2119416 0.2119416 38 0.5761169 0.2119416 0.2119416 39 0.5761169 0.2119416 0.2119416 40 0.5761169 0.2119416 0.2119416 41 0.5761169 0.2119416 0.2119416 42 0.5761169 0.2119416 0.2119416 43 0.5761169 0.2119416 0.2119416 44 0.5761169 0.2119416 0.2119416 45 0.5761169 0.2119416 0.2119416 46 0.5761169 0.2119416 0.2119416 47 0.5761169 0.2119416 0.2119416 48 0.5761169 0.2119416 0.2119416 49 0.5761169 0.2119416 0.2119416 50 0.5761169 0.2119416 0.2119416 51 0.2119501 0.5760764 0.2119735 52 0.2119988 0.5758448 0.2121563 53 0.2122642 0.5745797 0.2131561 54 0.2119692 0.5759859 0.2120450 55 0.2122808 0.5745002 0.2132190 56 0.2120571 0.5755680 0.2123749 57 0.2130237 0.5709136 0.2160627 58 0.2119416 0.5761168 0.2119416 59 0.2119510 0.5760722 0.2119768 60 0.2119799 0.5759347 0.2120854 61 0.2119417 0.5761164 0.2119420 62 0.2120010 0.5758346 0.2121644 63 0.2119417 0.5761164 0.2119419 64 0.2123777 0.5740364 0.2135860 65 0.2119417 0.5761163 0.2119420 66 0.2119449 0.5761012 0.2119539 67 0.2134143 0.5690011 0.2175847 68 0.2119416 0.5761165 0.2119418 69 0.2149688 0.5611939 0.2238373 70 0.2119418 0.5761157 0.2119425 71 0.2273578 0.2928771 0.4797651 72 0.2119423 0.5761135 0.2119443 73 0.2240667 0.5064758 0.2694575 74 0.2119746 0.5759602 0.2120653 75 0.2119434 0.5761080 0.2119486 76 0.2119480 0.5760865 0.2119655 77 0.2120761 0.5754771 0.2124467 78 0.2295344 0.4572663 0.3131993 79 0.2125160 0.5733717 0.2141123 80 0.2119416 0.5761169 0.2119416 81 0.2119418 0.5761158 0.2119424 82 0.2119416 0.5761168 0.2119417 83 0.2119418 0.5761155 0.2119427 84 0.2217619 0.2559536 0.5222845 85 0.2146789 0.5626744 0.2226467 86 0.2123996 0.5739311 0.2136693 87 0.2120786 0.5754655 0.2124559 88 0.2119836 0.5759175 0.2120990 89 0.2119455 0.5760981 0.2119564 90 0.2119556 0.5760503 0.2119941 91 0.2119890 0.5758918 0.2121193 92 0.2120885 0.5754182 0.2124933 93 0.2119424 0.5761128 0.2119448 94 0.2119416 0.5761168 0.2119416 95 0.2119649 0.5760062 0.2120289 96 0.2119430 0.5761102 0.2119468 97 0.2119501 0.5760763 0.2119736 98 0.2119451 0.5760999 0.2119550 99 0.2119416 0.5761169 0.2119416 100 0.2119472 0.5760901 0.2119627 101 0.2119416 0.2119416 0.5761169 102 0.2120247 0.2122535 0.5757218 103 0.2119436 0.2119491 0.5761074 104 0.2120239 0.2122505 0.5757255 105 0.2119417 0.2119421 0.5761162 106 0.2119416 0.2119418 0.5761166 107 0.2155590 0.2262985 0.5581425 108 0.2119523 0.2119819 0.5760658 109 0.2119588 0.2120062 0.5760350 110 0.2119416 0.2119416 0.5761168 111 0.2129396 0.2157374 0.5713231 112 0.2120706 0.2124259 0.5755035 113 0.2119570 0.2119996 0.5760434 114 0.2119566 0.2119979 0.5760455 115 0.2119416 0.2119418 0.5761165 116 0.2119436 0.2119491 0.5761073 117 0.2124091 0.2137052 0.5738858 118 0.2119417 0.2119420 0.5761163 119 0.2119416 0.2119416 0.5761169 120 0.2258967 0.2817096 0.4923938 121 0.2119421 0.2119434 0.5761145 122 0.2120054 0.2121808 0.5758138 123 0.2119416 0.2119418 0.5761165 124 0.2188811 0.2412052 0.5399137 125 0.2119484 0.2119671 0.5760845 126 0.2121480 0.2127171 0.5751349 127 0.2242727 0.2707593 0.5049680 128 0.2212157 0.2529979 0.5257864 129 0.2119426 0.2119453 0.5761121 130 0.2193073 0.2432662 0.5374264 131 0.2119527 0.2119833 0.5760640 132 0.2119817 0.2120919 0.5759265 133 0.2119418 0.2119424 0.5761158 134 0.2280721 0.4729786 0.2989493 135 0.2167852 0.2315810 0.5516338 136 0.2119417 0.2119422 0.5761161 137 0.2119416 0.2119418 0.5761166 138 0.2124153 0.2137291 0.5738556 139 0.2244898 0.2721509 0.5033593 140 0.2120055 0.2121814 0.5758131 141 0.2119416 0.2119419 0.5761165 142 0.2119746 0.2120652 0.5759602 143 0.2120247 0.2122535 0.5757218 144 0.2119416 0.2119419 0.5761165 145 0.2119416 0.2119416 0.5761168 146 0.2119473 0.2119632 0.5760895 147 0.2123949 0.2136515 0.5739536 148 0.2121838 0.2128524 0.5749638 149 0.2119425 0.2119452 0.5761123 150 0.2132783 0.2170527 0.5696690 $k [1] 1 > e.scal(ldapred, tc = iris$Species) $sv setosa versicolor virginica 1 1.000000e+00 8.470496e-241 8.470496e-241 2 1.000000e+00 8.470496e-241 8.470496e-241 3 1.000000e+00 8.470496e-241 8.470496e-241 4 1.000000e+00 8.470496e-241 8.470496e-241 5 1.000000e+00 8.470496e-241 8.470496e-241 6 1.000000e+00 8.470496e-241 8.470496e-241 7 1.000000e+00 8.470496e-241 8.470496e-241 8 1.000000e+00 8.470496e-241 8.470496e-241 9 1.000000e+00 8.470496e-241 8.470496e-241 10 1.000000e+00 8.470496e-241 8.470496e-241 11 1.000000e+00 8.470496e-241 8.470496e-241 12 1.000000e+00 8.470496e-241 8.470496e-241 13 1.000000e+00 8.470496e-241 8.470496e-241 14 1.000000e+00 8.470496e-241 8.470496e-241 15 1.000000e+00 8.470496e-241 8.470496e-241 16 1.000000e+00 8.470496e-241 8.470496e-241 17 1.000000e+00 8.470496e-241 8.470496e-241 18 1.000000e+00 8.470496e-241 8.470496e-241 19 1.000000e+00 8.470496e-241 8.470496e-241 20 1.000000e+00 8.470496e-241 8.470496e-241 21 1.000000e+00 8.470496e-241 8.470496e-241 22 1.000000e+00 8.470496e-241 8.470496e-241 23 1.000000e+00 8.470496e-241 8.470496e-241 24 1.000000e+00 8.470496e-241 8.470496e-241 25 1.000000e+00 8.470496e-241 8.470496e-241 26 1.000000e+00 8.470496e-241 8.470496e-241 27 1.000000e+00 8.470496e-241 8.470496e-241 28 1.000000e+00 8.470496e-241 8.470496e-241 29 1.000000e+00 8.470496e-241 8.470496e-241 30 1.000000e+00 8.470496e-241 8.470496e-241 31 1.000000e+00 8.470496e-241 8.470496e-241 32 1.000000e+00 8.470496e-241 8.470496e-241 33 1.000000e+00 8.470496e-241 8.470496e-241 34 1.000000e+00 8.470496e-241 8.470496e-241 35 1.000000e+00 8.470496e-241 8.470496e-241 36 1.000000e+00 8.470496e-241 8.470496e-241 37 1.000000e+00 8.470496e-241 8.470496e-241 38 1.000000e+00 8.470496e-241 8.470496e-241 39 1.000000e+00 8.470496e-241 8.470496e-241 40 1.000000e+00 8.470496e-241 8.470496e-241 41 1.000000e+00 8.470496e-241 8.470496e-241 42 1.000000e+00 8.470496e-241 8.470496e-241 43 1.000000e+00 8.470496e-241 8.470496e-241 44 1.000000e+00 8.470496e-241 8.470496e-241 45 1.000000e+00 8.470496e-241 8.470496e-241 46 1.000000e+00 8.470496e-241 8.470496e-241 47 1.000000e+00 8.470496e-241 8.470496e-241 48 1.000000e+00 8.470496e-241 8.470496e-241 49 1.000000e+00 8.470496e-241 8.470496e-241 50 1.000000e+00 8.470496e-241 8.470496e-241 51 9.004464e-241 1.000000e+00 9.572093e-241 52 1.276937e-240 1.000000e+00 1.924997e-240 53 8.600898e-240 1.000000e+00 8.733308e-239 54 1.032216e-240 1.000000e+00 1.257861e-240 55 9.694913e-240 1.000000e+00 1.109632e-238 56 1.938797e-240 1.000000e+00 4.437680e-240 57 2.131233e-237 1.000000e+00 5.362324e-234 58 8.471020e-241 1.000000e+00 8.471545e-241 59 9.060772e-241 1.000000e+00 9.692182e-241 60 1.115061e-240 1.000000e+00 1.467873e-240 61 8.477151e-241 1.000000e+00 8.483812e-241 62 1.296885e-240 1.000000e+00 1.985612e-240 63 8.476211e-241 1.000000e+00 8.481930e-241 64 1.949424e-239 1.000000e+00 4.486461e-238 65 8.478222e-241 1.000000e+00 8.485954e-241 66 8.672726e-241 1.000000e+00 8.879784e-241 67 3.749713e-236 1.000000e+00 1.659920e-231 68 8.474782e-241 1.000000e+00 8.479071e-241 69 4.297242e-231 1.000000e+00 2.180072e-221 70 8.485944e-241 1.000000e+00 8.501419e-241 71 5.259425e-180 3.265636e-119 1.000000e+00 72 8.514367e-241 1.000000e+00 8.558466e-241 73 1.634288e-196 1.000000e+00 3.153179e-152 74 1.072998e-240 1.000000e+00 1.359218e-240 75 8.584640e-241 1.000000e+00 8.700322e-241 76 8.867722e-241 1.000000e+00 9.283577e-241 77 2.223614e-240 1.000000e+00 5.837271e-240 78 3.460710e-166 1.000000e+00 1.413909e-91 79 5.301531e-239 1.000000e+00 3.318133e-237 80 8.470585e-241 1.000000e+00 8.470675e-241 81 8.484591e-241 1.000000e+00 8.498710e-241 82 8.472026e-241 1.000000e+00 8.473556e-241 83 8.488206e-241 1.000000e+00 8.505954e-241 84 2.250630e-206 5.979974e-172 1.000000e+00 85 4.750250e-232 1.000000e+00 2.663938e-223 86 2.284136e-239 1.000000e+00 6.159354e-238 87 2.262968e-240 1.000000e+00 6.045718e-240 88 1.144412e-240 1.000000e+00 1.546165e-240 89 8.714482e-241 1.000000e+00 8.965496e-241 90 9.365410e-241 1.000000e+00 1.035487e-240 91 1.189636e-240 1.000000e+00 1.670779e-240 92 2.430123e-240 1.000000e+00 6.971843e-240 93 8.523505e-241 1.000000e+00 8.576846e-241 94 8.471027e-241 1.000000e+00 8.471559e-241 95 1.000958e-240 1.000000e+00 1.182832e-240 96 8.556445e-241 1.000000e+00 8.643265e-241 97 9.005677e-241 1.000000e+00 9.574673e-241 98 8.690592e-241 1.000000e+00 8.916407e-241 99 8.470584e-241 1.000000e+00 8.470671e-241 100 8.819586e-241 1.000000e+00 9.183062e-241 101 8.470529e-241 8.470563e-241 1.000000e+00 102 1.537329e-240 2.790132e-240 1.000000e+00 103 8.592776e-241 8.716821e-241 1.000000e+00 104 1.528760e-240 2.759113e-240 1.000000e+00 105 8.478989e-241 8.487491e-241 1.000000e+00 106 8.473613e-241 8.476732e-241 1.000000e+00 107 3.983592e-229 1.873444e-217 1.000000e+00 108 9.149767e-241 9.883510e-241 1.000000e+00 109 9.584566e-241 1.084516e-240 1.000000e+00 110 8.471305e-241 8.472114e-241 1.000000e+00 111 1.152648e-237 1.568499e-234 1.000000e+00 112 2.136778e-240 5.390263e-240 1.000000e+00 113 9.464022e-241 1.057408e-240 1.000000e+00 114 9.433895e-241 1.050687e-240 1.000000e+00 115 8.475182e-241 8.479870e-241 1.000000e+00 116 8.593378e-241 8.718042e-241 1.000000e+00 117 2.445682e-239 7.061408e-238 1.000000e+00 118 8.477540e-241 8.484590e-241 1.000000e+00 119 8.470502e-241 8.470508e-241 1.000000e+00 120 8.621498e-188 8.775192e-135 1.000000e+00 121 8.500760e-241 8.531132e-241 1.000000e+00 122 1.338174e-240 2.114056e-240 1.000000e+00 123 8.474950e-241 8.479406e-241 1.000000e+00 124 1.752149e-217 3.624376e-194 1.000000e+00 125 8.894543e-241 9.339818e-241 1.000000e+00 126 3.725086e-240 1.638188e-239 1.000000e+00 127 1.411558e-195 2.352279e-150 1.000000e+00 128 1.431952e-208 2.420740e-176 1.000000e+00 129 8.531760e-241 8.593466e-241 1.000000e+00 130 6.593739e-216 5.132803e-191 1.000000e+00 131 9.173506e-241 9.934862e-241 1.000000e+00 132 1.129014e-240 1.504839e-240 1.000000e+00 133 8.484621e-241 8.498769e-241 1.000000e+00 134 7.839111e-176 1.000000e+00 7.254790e-111 135 5.998953e-225 4.248563e-209 1.000000e+00 136 8.480582e-241 8.490681e-241 1.000000e+00 137 8.474656e-241 8.478818e-241 1.000000e+00 138 2.559228e-239 7.732305e-238 1.000000e+00 139 1.406278e-194 2.334713e-148 1.000000e+00 140 1.339523e-240 2.118319e-240 1.000000e+00 141 8.476027e-241 8.481561e-241 1.000000e+00 142 1.072935e-240 1.359057e-240 1.000000e+00 143 1.537329e-240 2.790132e-240 1.000000e+00 144 8.475313e-241 8.480133e-241 1.000000e+00 145 8.471678e-241 8.472861e-241 1.000000e+00 146 8.827755e-241 9.200083e-241 1.000000e+00 147 2.208220e-239 5.756728e-238 1.000000e+00 148 4.821090e-240 2.743985e-239 1.000000e+00 149 8.529580e-241 8.589077e-241 1.000000e+00 150 1.378235e-236 2.242527e-232 1.000000e+00 $k [1] 552.7864 > > > > cleanEx(); ..nameEx <- "errormatrix" > > ### * errormatrix > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: errormatrix > ### Title: Tabulation of prediction errors by classes > ### Aliases: errormatrix > ### Keywords: multivariate > > ### ** Examples > > data(iris) > library(MASS) > x <- lda(Species ~ Sepal.Length + Sepal.Width, data=iris) > y <- predict(x, iris) > > # absolute numbers: > errormatrix(iris$Species, y$class) predicted true setosa versicolor virginica -SUM- setosa 49 1 0 1 versicolor 0 36 14 14 virginica 0 15 35 15 -SUM- 0 16 14 30 > > # relative frequencies: > errormatrix(iris$Species, y$class, relative = TRUE) predicted true setosa versicolor virginica -SUM- setosa 0.98 0.0200000 0.0000000 0.02 versicolor 0.00 0.7200000 0.2800000 0.28 virginica 0.00 0.3000000 0.7000000 0.30 -SUM- 0.00 0.5333333 0.4666667 0.20 > > # percentages: > round(100 * errormatrix(iris$Species, y$class, relative = TRUE), 0) predicted true setosa versicolor virginica -SUM- setosa 98 2 0 2 versicolor 0 72 28 28 virginica 0 30 70 30 -SUM- 0 53 47 20 > > # expected error rate in case of class prior: > indiv.rates <- errormatrix(iris$Species, y$class, relative = TRUE)[1:3, 4] > prior <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0.5) > total.rate <- t(indiv.rates) %*% prior > total.rate [,1] [1,] 0.238 > > > > cleanEx(); ..nameEx <- "friedmandata" > > ### * friedmandata > > flush(stderr()); flush(stdout()) > > ### Name: friedman.data > ### Title: Friedman's classification benchmark data > ### Aliases: friedman.data > ### Keywords: multivariate > > ### ** Examples > > # Reproduce the 1st setting with 6 variables. > # Error rate should be somewhat near 9 percent. > training <- friedman.data(1, 6, 40) > x <- rda(class ~ ., data = training, gamma = 0.74, lambda = 0.77) > test <- friedman.data(1, 6, 100) > y <- predict(x, test[,-1]) > errormatrix(test[,1], y$class) predicted true 1 2 3 -SUM- 1 24 0 1 1 2 6 32 1 7 3 5 2 29 7 -SUM- 11 2 2 15 > > > > cleanEx(); ..nameEx <- "greedy.wilks" > > ### * greedy.wilks > > flush(stderr()); flush(stdout()) > > ### Name: greedy.wilks > ### Title: Stepwise forward variable selection for classification > ### Aliases: greedy.wilks greedy.wilks.default greedy.wilks.formula > ### print.greedy.wilks > ### Keywords: multivariate > > ### ** Examples > > data(B3) > gw_obj <- greedy.wilks(PHASEN ~ ., data = B3, niveau = 0.1) > gw_obj Formula containing included variables: PHASEN ~ EWAJW + LSTKJW + ZINSK + CP91JW + IAU91JW + PBSPJW + ZINSLR + PCPJW Values calculated in each step of the selection procedure: vars Wilks.lambda F.statistics.overall p.value.overall F.statistics.diff 1 EWAJW 0.6058201 33.18341 1.405358e-16 33.183411 2 LSTKJW 0.4271561 26.85606 1.218146e-25 21.192038 3 ZINSK 0.3614525 21.20584 7.607587e-29 9.149422 4 CP91JW 0.3002868 19.05337 1.153881e-32 10.184539 5 IAU91JW 0.2624925 17.11094 6.597858e-35 7.151127 6 PBSPJW 0.2451025 14.99388 3.695840e-35 3.500196 7 ZINSLR 0.2205325 13.94619 1.442943e-36 5.459204 8 PCPJW 0.1999847 13.10739 9.454573e-38 5.000333 p.value.diff 1 1.405358e-16 2 1.554268e-11 3 1.326989e-05 4 3.783582e-06 5 1.604993e-04 6 1.708972e-02 7 1.379166e-03 8 2.486333e-03 > ## now you can say stuff like > ## lda(gw_obj$formula, data = B3) > > > > cleanEx(); ..nameEx <- "hmm.sop" > > ### * hmm.sop > > flush(stderr()); flush(stdout()) > > ### Name: hmm.sop > ### Title: Calculation of HMM Sum of Path > ### Aliases: hmm.sop > ### Keywords: ts classif > > ### ** Examples > > library(MASS) > data(B3) > trans.matrix <- calc.trans(B3$PHASEN) > > # Calculate posterior prob. for the classes via lda > prob.matrix <- predict(lda(PHASEN ~ ., data = B3))$post > errormatrix(B3$PHASEN, apply(prob.matrix, 1, which.max)) predicted true 1 2 3 4 -SUM- 1 55 0 3 1 4 2 4 15 5 0 9 3 5 1 39 2 8 4 4 0 2 21 6 -SUM- 13 1 10 3 27 > prior.prob <- hmm.sop("2", trans.matrix, prob.matrix) > errormatrix(B3$PHASEN, apply(prior.prob, 1, which.max)) predicted true 1 2 3 4 -SUM- 1 53 0 3 3 6 2 7 15 2 0 9 3 0 1 44 2 3 4 4 0 7 16 11 -SUM- 11 1 12 5 29 > > > > cleanEx(); ..nameEx <- "meclight" > > ### * meclight > > flush(stderr()); flush(stdout()) > > ### Name: meclight.default > ### Title: Minimal Error Classification > ### Aliases: meclight meclight.default meclight.formula meclight.matrix > ### meclight.data.frame print.meclight > ### Keywords: classif > > ### ** Examples > > data(iris) > meclight.obj <- meclight(Species ~ ., data = iris) > meclight.obj Dimension of projection: 1 est. bootstrap error rate: 0.01333333 est. improvement to LDA: 0 Projection matrix: Proj.dim 1 Sepal.Length 0.8293776 Sepal.Width 1.5344731 Petal.Length -2.2012117 Petal.Width -2.8104603 > > > > cleanEx(); ..nameEx <- "nm" > > ### * nm > > flush(stderr()); flush(stdout()) > > ### Name: nm > ### Title: Nearest Mean Classification > ### Aliases: nm nm.default nm.formula nm.matrix nm.data.frame > ### Keywords: classif > > ### ** Examples > > data(B3) > x <- nm(PHASEN ~ ., data = B3) > x$learn BSP91JW CP91JW DEFRATE EWAJW EXIMRATE GM1JW IAU91JW 1 4.036102 3.545254 -1.3394915 1.064576 3.880847 8.613390 6.893559322 2 6.312083 6.427500 -0.8479167 2.680833 3.104167 11.015833 11.360416667 3 2.769149 3.656596 -0.8380851 1.077872 3.013617 6.841489 -0.003404255 4 1.093333 2.620000 -1.6548148 -0.994074 3.124815 9.607778 -1.939259259 IB91JW LSTKJW PBSPJW PCPJW ZINSK ZINSLR 1 3.397119 2.111017 2.789153 2.214746 4.715085 3.976271 2 6.830417 4.195833 3.927917 3.187917 6.836250 3.571250 3 1.673191 6.291064 4.496170 4.062128 7.682553 3.230000 4 -1.491852 4.249630 4.206296 3.938148 5.672222 3.739259 > x <- nm(PHASEN ~ ., data = B3, gamma = 0.1) > predict(x)$post 1 2 3 4 1955,4 3.472988e-12 1.000000e+00 6.256530e-28 9.209813e-38 1956,1 1.967463e-11 1.000000e+00 8.959782e-23 1.924185e-33 1956,2 1.882068e-06 9.999981e-01 2.223063e-14 5.553180e-22 1956,3 1.174916e-01 5.236247e-03 8.771519e-01 1.202531e-04 1956,4 9.067165e-05 2.234848e-08 9.968615e-01 3.047773e-03 1957,1 3.870388e-01 1.020512e-03 6.119223e-01 1.837694e-05 1957,2 5.496775e-09 3.318330e-17 6.801114e-02 9.319889e-01 1957,3 1.166941e-03 3.486237e-08 5.046809e-01 4.941521e-01 1957,4 9.151862e-01 4.514699e-04 4.943612e-02 3.492618e-02 1958,1 8.511008e-06 2.470814e-11 2.222837e-03 9.977687e-01 1958,2 9.016348e-01 8.529318e-03 7.993207e-02 9.903783e-03 1958,3 8.568335e-01 1.431629e-01 3.607673e-06 3.512772e-08 1958,4 3.067390e-01 6.932609e-01 4.717733e-08 1.233783e-10 1959,1 1.382153e-02 9.861785e-01 4.985562e-10 1.393021e-14 1959,2 9.385329e-05 9.999061e-01 1.018568e-17 1.280491e-21 1959,3 2.745819e-03 9.972542e-01 2.241443e-14 5.375662e-17 1959,4 6.405728e-05 9.999359e-01 5.953432e-17 6.669036e-21 1960,1 6.988521e-09 1.000000e+00 5.725794e-26 5.818246e-33 1960,2 3.440691e-05 9.999656e-01 1.207759e-15 7.516451e-21 1960,3 6.612344e-05 9.999339e-01 1.263519e-14 3.231589e-20 1960,4 3.087470e-03 9.969125e-01 1.340914e-10 1.287466e-15 1961,1 1.875582e-06 9.999981e-01 1.809291e-14 1.141122e-22 1961,2 1.229742e-02 9.877024e-01 1.340580e-07 8.658827e-13 1961,3 2.297179e-01 7.696405e-01 6.410647e-04 5.668398e-07 1961,4 4.960982e-01 4.997128e-01 4.135101e-03 5.394124e-05 1962,1 9.852647e-01 2.189668e-03 4.685051e-03 7.860576e-03 1962,2 2.187218e-01 7.808754e-01 3.996255e-04 3.160063e-06 1962,3 7.325656e-01 2.672382e-01 1.957836e-04 3.591392e-07 1962,4 9.032811e-01 9.265692e-02 4.054543e-03 7.389732e-06 1963,1 7.339309e-16 3.430401e-30 2.172746e-07 9.999998e-01 1963,2 8.269534e-03 2.127405e-08 9.615644e-01 3.016605e-02 1963,3 8.335183e-01 3.978251e-04 1.657725e-01 3.113607e-04 1963,4 8.898305e-01 6.742892e-04 1.094195e-01 7.574328e-05 1964,1 5.552572e-10 1.000000e+00 6.276229e-20 1.265898e-32 1964,2 2.377382e-01 7.622618e-01 2.008911e-08 2.120929e-12 1964,3 9.557727e-01 4.414078e-02 8.640369e-05 1.395924e-07 1964,4 4.228472e-02 9.577040e-01 1.125014e-05 1.613403e-10 1965,1 2.358090e-01 7.641897e-01 1.293290e-06 4.573253e-10 1965,2 5.046386e-01 4.947181e-01 6.421651e-04 1.112905e-06 1965,3 2.721444e-01 6.712938e-01 5.655436e-02 7.492892e-06 1965,4 9.384091e-01 1.556845e-02 4.579564e-02 2.267695e-04 1966,1 7.087844e-01 1.269574e-01 1.642543e-01 3.823452e-06 1966,2 2.887408e-02 1.025617e-06 9.654188e-01 5.706075e-03 1966,3 1.715603e-06 1.012954e-14 7.673364e-01 2.326619e-01 1966,4 7.816257e-11 1.022979e-22 2.608917e-01 7.391083e-01 1967,1 1.067741e-19 6.628875e-38 3.480040e-05 9.999652e-01 1967,2 6.058609e-18 3.811354e-37 1.005614e-06 9.999990e-01 1967,3 1.691011e-13 1.559688e-30 4.752637e-06 9.999952e-01 1967,4 9.759991e-01 5.045654e-09 1.085870e-04 2.389234e-02 1968,1 4.232896e-04 7.851046e-14 1.512715e-02 9.844496e-01 1968,2 9.997123e-01 6.389233e-05 1.841760e-04 3.965700e-05 1968,3 6.543684e-02 9.345632e-01 1.951972e-11 3.454319e-15 1968,4 8.532763e-01 1.467106e-01 1.303746e-05 5.223202e-09 1969,1 3.943878e-07 9.999996e-01 8.795726e-23 4.390598e-29 1969,2 5.915987e-07 9.999994e-01 3.635206e-19 3.274949e-26 1969,3 1.558122e-08 1.000000e+00 2.943650e-21 8.742149e-29 1969,4 2.844816e-04 9.997155e-01 2.451193e-09 5.647720e-15 1970,1 9.234552e-06 9.999908e-01 1.927538e-10 6.734492e-17 1970,2 2.888751e-08 1.000000e+00 9.785636e-13 6.232884e-22 1970,3 9.718046e-06 9.999872e-01 3.063395e-06 1.936368e-14 1970,4 3.862314e-06 9.999959e-01 2.061494e-07 6.264200e-16 1971,1 3.441319e-10 1.000000e+00 5.282803e-15 4.909304e-26 1971,2 1.839722e-02 9.534786e-01 2.811598e-02 8.243135e-06 1971,3 1.445700e-06 1.404073e-08 9.562189e-01 4.377967e-02 1971,4 7.345582e-06 2.780965e-08 9.138428e-01 8.614982e-02 1972,1 3.050049e-03 7.821211e-04 9.934640e-01 2.703812e-03 1972,2 2.563834e-06 2.715661e-11 3.783050e-01 6.216924e-01 1972,3 1.335780e-04 1.737048e-08 2.428053e-01 7.570611e-01 1972,4 6.583025e-04 1.002554e-05 9.443097e-01 5.502195e-02 1973,1 8.323619e-03 3.686080e-03 9.865152e-01 1.475052e-03 1973,2 1.907606e-06 1.020643e-08 9.987786e-01 1.219488e-03 1973,3 5.650933e-10 2.184012e-15 9.999027e-01 9.726599e-05 1973,4 1.784516e-11 2.694241e-18 9.983770e-01 1.622967e-03 1974,1 3.495811e-18 1.961723e-32 1.054575e-01 8.945425e-01 1974,2 3.309108e-20 1.751800e-34 2.290788e-03 9.977092e-01 1974,3 4.320003e-17 5.203365e-28 7.138464e-04 9.992862e-01 1974,4 8.035438e-19 3.234839e-30 1.216134e-04 9.998784e-01 1975,1 4.756468e-19 7.025849e-32 4.146113e-07 9.999996e-01 1975,2 1.123433e-11 4.518901e-21 3.278204e-06 9.999967e-01 1975,3 1.785758e-10 3.404891e-19 1.114261e-05 9.999889e-01 1975,4 9.637751e-01 1.222955e-03 6.129196e-05 3.494065e-02 1976,1 9.971666e-01 2.746922e-03 6.381602e-07 8.581077e-05 1976,2 7.890956e-01 2.109031e-01 1.180233e-06 4.340608e-08 1976,3 3.344110e-01 6.902980e-05 5.985957e-01 6.692425e-02 1976,4 9.902541e-01 2.793671e-04 9.428759e-03 3.777358e-05 1977,1 9.495612e-01 4.799824e-02 2.440279e-03 2.665894e-07 1977,2 2.153304e-01 2.721643e-06 7.096038e-01 7.506303e-02 1977,3 8.433622e-01 1.566371e-01 6.924847e-07 6.671118e-09 1977,4 9.939679e-01 4.724456e-04 2.867925e-03 2.691708e-03 1978,1 1.875724e-01 2.445933e-06 5.781048e-03 8.066441e-01 1978,2 9.162849e-01 8.369739e-02 1.620503e-05 1.469576e-06 1978,3 5.745327e-01 4.254631e-01 4.062001e-06 1.166544e-07 1978,4 9.562770e-01 4.361003e-02 9.857144e-05 1.440811e-05 1979,1 9.918409e-01 8.147995e-03 8.420808e-06 2.682733e-06 1979,2 2.829828e-01 7.170153e-01 1.886117e-06 4.410491e-10 1979,3 8.217633e-01 1.759412e-01 2.295405e-03 9.574092e-08 1979,4 5.989776e-01 3.245700e-01 7.645220e-02 2.031714e-07 1980,1 1.924036e-03 9.980587e-01 1.728536e-05 8.075518e-15 1980,2 9.198976e-07 1.372799e-12 9.992905e-01 7.085780e-04 1980,3 1.653621e-06 7.104327e-12 9.989598e-01 1.038576e-03 1980,4 6.086415e-09 1.879658e-17 3.493405e-01 6.506595e-01 1981,1 1.513218e-12 2.489632e-24 1.969965e-03 9.980300e-01 1981,2 2.339505e-09 1.259689e-18 8.866675e-01 1.133325e-01 1981,3 2.577487e-10 1.283297e-20 8.105798e-01 1.894202e-01 1981,4 1.167114e-12 3.949235e-26 1.467601e-01 8.532399e-01 1982,1 2.021965e-15 6.276169e-30 5.379590e-03 9.946204e-01 1982,2 6.572380e-13 3.671270e-26 2.928630e-03 9.970714e-01 1982,3 1.560676e-14 5.208210e-28 1.489215e-03 9.985108e-01 1982,4 3.225832e-08 1.638642e-18 4.319003e-02 9.568099e-01 1983,1 1.771391e-04 1.630827e-12 4.921423e-03 9.949014e-01 1983,2 9.613690e-01 3.413997e-06 2.246396e-03 3.638115e-02 1983,3 9.989620e-01 1.373144e-04 3.202853e-04 5.803885e-04 1983,4 9.966626e-01 3.337206e-03 1.972565e-07 2.034946e-08 1984,1 3.963533e-01 6.083644e-07 5.930797e-01 1.056641e-02 1984,2 6.537712e-10 2.104297e-22 4.196614e-02 9.580339e-01 1984,3 8.987604e-01 1.462443e-08 8.702338e-02 1.421616e-02 1984,4 6.585481e-02 2.894535e-10 6.979735e-01 2.361717e-01 1985,1 1.303221e-03 7.709256e-15 2.759518e-04 9.984208e-01 1985,2 9.996519e-01 3.479989e-04 8.893914e-08 5.285830e-10 1985,3 8.867201e-01 2.139783e-07 9.784082e-02 1.543886e-02 1985,4 9.971931e-01 3.603590e-07 1.209688e-03 1.596895e-03 1986,1 9.598700e-01 1.609785e-06 3.122576e-02 8.902619e-03 1986,2 9.968522e-01 3.122231e-03 2.504093e-05 5.603311e-07 1986,3 8.649695e-01 5.460061e-06 9.956050e-02 3.546451e-02 1986,4 9.474326e-01 6.196164e-06 4.903993e-02 3.521318e-03 1987,1 9.192757e-01 1.959045e-07 6.295037e-03 7.442907e-02 1987,2 5.973861e-01 4.688865e-07 1.430719e-01 2.595416e-01 1987,3 9.995478e-01 2.231940e-05 2.188156e-04 2.110916e-04 1987,4 9.949703e-01 3.335791e-06 2.172346e-03 2.854048e-03 1988,1 2.047822e-01 7.952178e-01 1.331397e-08 3.012846e-13 1988,2 9.998637e-01 6.817994e-06 4.259450e-05 8.689389e-05 1988,3 9.996827e-01 1.158645e-05 6.264816e-05 2.430339e-04 1988,4 9.999308e-01 1.192765e-05 1.573440e-05 4.152823e-05 1989,1 7.549508e-01 2.450472e-01 2.024052e-06 4.811993e-10 1989,2 9.920881e-01 7.910957e-03 8.953224e-07 5.292392e-10 1989,3 9.989622e-01 6.122715e-05 9.729765e-04 3.591625e-06 1989,4 9.952112e-01 4.756394e-03 3.235102e-05 8.214640e-09 1990,1 8.025813e-03 9.919742e-01 4.148329e-12 7.506986e-19 1990,2 6.388090e-02 9.361191e-01 5.332443e-08 3.087810e-12 1990,3 9.375471e-05 9.999062e-01 4.536061e-14 3.320149e-15 1990,4 7.947931e-05 9.999205e-01 1.064924e-13 5.326177e-14 1991,1 5.785052e-04 9.994215e-01 6.689156e-14 5.606857e-11 1991,2 4.791956e-06 9.999952e-01 5.018906e-15 3.632442e-17 1991,3 1.411074e-01 8.588737e-01 1.896947e-05 2.038736e-09 1991,4 6.020221e-02 4.260563e-05 9.375429e-01 2.212304e-03 1992,1 1.449101e-03 2.216462e-07 9.984883e-01 6.241682e-05 1992,2 3.628242e-06 1.039444e-13 8.635592e-01 1.364372e-01 1992,3 2.234100e-07 7.217030e-15 7.891512e-01 2.108485e-01 1992,4 5.187204e-08 2.343732e-15 2.563485e-01 7.436514e-01 1993,1 1.310536e-19 1.524020e-34 1.552034e-05 9.999845e-01 1993,2 5.719466e-20 2.103846e-35 3.496522e-05 9.999650e-01 1993,3 1.932700e-17 2.245841e-32 1.292095e-05 9.999871e-01 1993,4 3.212406e-17 3.508225e-35 1.582053e-04 9.998418e-01 1994,1 3.417393e-09 1.486352e-19 1.254912e-03 9.987451e-01 1994,2 1.348941e-04 8.197988e-14 3.024959e-03 9.968401e-01 1994,3 3.291346e-02 9.995157e-11 7.579785e-03 9.595068e-01 1994,4 9.973659e-01 1.738824e-05 8.005291e-05 2.536681e-03 > > > > cleanEx(); ..nameEx <- "partimat" > > ### * partimat > > flush(stderr()); flush(stdout()) > > ### Name: partimat > ### Title: Plotting the 2-d partitions of classification methods > ### Aliases: partimat partimat.default partimat.formula partimat.data.frame > ### partimat.matrix > ### Keywords: classif dplot > > ### ** Examples > > library(MASS) > data(iris) > partimat(Species ~ ., data = iris, method = "lda") > ## Not run: > ##D partimat(Species ~ ., data = iris, method = "lda", > ##D plot.matrix = TRUE, imageplot = FALSE) # takes some time ... > ## End(Not run) > > > > cleanEx(); ..nameEx <- "plineplot" > > ### * plineplot > > flush(stderr()); flush(stdout()) > > ### Name: plineplot > ### Title: Plotting marginal posterior class probabilities > ### Aliases: plineplot > ### Keywords: classif dplot > > ### ** Examples > > library(MASS) > > # The name of the variable can be used for x > data(B3) > plineplot(PHASEN ~ ., data = B3, method = "lda", + x = "EWAJW", xlab = "EWAJW") Warning: parameter "xlab" could not be set in high-level plot() function Warning: parameter "xlab" could not be set in high-level plot() function Warning: parameter "xlab" could not be set in high-level plot() function Warning: parameter "xlab" could not be set in high-level plot() function [1] 0.1719745 > > # The plotted variable need not be in the data > data(iris) > iris2 <- iris[ , c(1,3,5)] > plineplot(Species ~ ., data = iris2, method = "lda", + x = iris[ , 4], xlab = "Petal.Width") Warning: parameter "xlab" could not be set in high-level plot() function Warning: parameter "xlab" could not be set in high-level plot() function Warning: parameter "xlab" could not be set in high-level plot() function [1] 0.03333333 > > > > cleanEx(); ..nameEx <- "plot.NaiveBayes" > > ### * plot.NaiveBayes > > flush(stderr()); flush(stdout()) > > ### Name: plot.NaiveBayes > ### Title: Naive Bayes Plot > ### Aliases: plot.NaiveBayes > ### Keywords: classif dplot > > ### ** Examples > > data(iris) > mN <- NaiveBayes(Species ~ ., data = iris) > plot(mN) > > mK <- NaiveBayes(Species ~ ., data = iris, usekernel = TRUE) > plot(mK) > > > > cleanEx(); ..nameEx <- "predict.NaiveBayes" > > ### * predict.NaiveBayes > > flush(stderr()); flush(stdout()) > > ### Name: predict.NaiveBayes > ### Title: Naive Bayes Classifier > ### Aliases: predict.NaiveBayes > ### Keywords: classif category > > ### ** Examples > > > data(iris) > m <- NaiveBayes(Species ~ ., data = iris) > predict(m) $class [1] setosa setosa setosa setosa setosa setosa [7] setosa setosa setosa setosa setosa setosa [13] setosa setosa setosa setosa setosa setosa [19] setosa setosa setosa setosa setosa setosa [25] setosa setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa setosa [37] setosa setosa setosa setosa setosa setosa [43] setosa setosa setosa setosa setosa setosa [49] setosa setosa versicolor versicolor virginica versicolor [55] versicolor versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor versicolor [67] versicolor versicolor versicolor versicolor virginica versicolor [73] versicolor versicolor versicolor versicolor versicolor virginica [79] versicolor versicolor versicolor versicolor versicolor versicolor [85] versicolor versicolor versicolor versicolor versicolor versicolor [91] versicolor versicolor versicolor versicolor versicolor versicolor [97] versicolor versicolor versicolor versicolor virginica virginica [103] virginica virginica virginica virginica versicolor virginica [109] virginica virginica virginica virginica virginica virginica [115] virginica virginica virginica virginica virginica versicolor [121] virginica virginica virginica virginica virginica virginica [127] virginica virginica virginica virginica virginica virginica [133] virginica versicolor virginica virginica virginica virginica [139] virginica virginica virginica virginica virginica virginica [145] virginica virginica virginica virginica virginica virginica Levels: setosa versicolor virginica $posterior setosa versicolor virginica 1 1.000000e+00 2.981309e-18 2.152373e-25 2 1.000000e+00 3.169312e-17 6.938030e-25 3 1.000000e+00 2.367113e-18 7.240956e-26 4 1.000000e+00 3.069606e-17 8.690636e-25 5 1.000000e+00 1.017337e-18 8.885794e-26 6 1.000000e+00 2.717732e-14 4.344285e-21 7 1.000000e+00 2.321639e-17 7.988271e-25 8 1.000000e+00 1.390751e-17 8.166995e-25 9 1.000000e+00 1.990156e-17 3.606469e-25 10 1.000000e+00 7.378931e-18 3.615492e-25 11 1.000000e+00 9.396089e-18 1.474623e-24 12 1.000000e+00 3.461964e-17 2.093627e-24 13 1.000000e+00 2.804520e-18 1.010192e-25 14 1.000000e+00 1.799033e-19 6.060578e-27 15 1.000000e+00 5.533879e-19 2.485033e-25 16 1.000000e+00 6.273863e-17 4.509864e-23 17 1.000000e+00 1.106658e-16 1.282419e-23 18 1.000000e+00 4.841773e-17 2.350011e-24 19 1.000000e+00 1.126175e-14 2.567180e-21 20 1.000000e+00 1.808513e-17 1.963924e-24 21 1.000000e+00 2.178382e-15 2.013989e-22 22 1.000000e+00 1.210057e-15 7.788592e-23 23 1.000000e+00 4.535220e-20 3.130074e-27 24 1.000000e+00 3.147327e-11 8.175305e-19 25 1.000000e+00 1.838507e-14 1.553757e-21 26 1.000000e+00 6.873990e-16 1.830374e-23 27 1.000000e+00 3.192598e-14 1.045146e-21 28 1.000000e+00 1.542562e-17 1.274394e-24 29 1.000000e+00 8.833285e-18 5.368077e-25 30 1.000000e+00 9.557935e-17 3.652571e-24 31 1.000000e+00 2.166837e-16 6.730536e-24 32 1.000000e+00 3.940500e-14 1.546678e-21 33 1.000000e+00 1.609092e-20 1.013278e-26 34 1.000000e+00 7.222217e-20 4.261853e-26 35 1.000000e+00 6.289348e-17 1.831694e-24 36 1.000000e+00 2.850926e-18 8.874002e-26 37 1.000000e+00 7.746279e-18 7.235628e-25 38 1.000000e+00 8.623934e-20 1.223633e-26 39 1.000000e+00 4.612936e-18 9.655450e-26 40 1.000000e+00 2.009325e-17 1.237755e-24 41 1.000000e+00 1.300634e-17 5.657689e-25 42 1.000000e+00 1.577617e-15 5.717219e-24 43 1.000000e+00 1.494911e-18 4.800333e-26 44 1.000000e+00 1.076475e-10 3.721344e-18 45 1.000000e+00 1.357569e-12 1.708326e-19 46 1.000000e+00 3.882113e-16 5.587814e-24 47 1.000000e+00 5.086735e-18 8.960156e-25 48 1.000000e+00 5.012793e-18 1.636566e-25 49 1.000000e+00 5.717245e-18 8.231337e-25 50 1.000000e+00 7.713456e-18 3.349997e-25 51 4.893048e-107 8.018653e-01 1.981347e-01 52 7.920550e-100 9.429283e-01 5.707168e-02 53 5.494369e-121 4.606254e-01 5.393746e-01 54 1.129435e-69 9.999621e-01 3.789964e-05 55 1.473329e-105 9.503408e-01 4.965916e-02 56 1.931184e-89 9.990013e-01 9.986538e-04 57 4.539099e-113 6.592515e-01 3.407485e-01 58 2.549753e-34 9.999997e-01 3.119517e-07 59 6.562814e-97 9.895385e-01 1.046153e-02 60 5.000210e-69 9.998928e-01 1.071638e-04 61 7.354548e-41 9.999997e-01 3.143915e-07 62 4.799134e-86 9.958564e-01 4.143617e-03 63 4.631287e-60 9.999925e-01 7.541274e-06 64 1.052252e-103 9.850868e-01 1.491324e-02 65 4.789799e-55 9.999700e-01 2.999393e-05 66 1.514706e-92 9.787587e-01 2.124125e-02 67 1.338348e-97 9.899311e-01 1.006893e-02 68 2.026115e-62 9.999799e-01 2.007314e-05 69 6.547473e-101 9.941996e-01 5.800427e-03 70 3.016276e-58 9.999913e-01 8.739959e-06 71 1.053341e-127 1.609361e-01 8.390639e-01 72 1.248202e-70 9.997743e-01 2.256698e-04 73 3.294753e-119 9.245812e-01 7.541876e-02 74 1.314175e-95 9.979398e-01 2.060233e-03 75 3.003117e-83 9.982736e-01 1.726437e-03 76 2.536747e-92 9.865372e-01 1.346281e-02 77 1.558909e-111 9.102260e-01 8.977398e-02 78 7.014282e-136 7.989607e-02 9.201039e-01 79 5.034528e-99 9.854957e-01 1.450433e-02 80 1.439052e-41 9.999984e-01 1.601574e-06 81 1.251567e-54 9.999955e-01 4.500139e-06 82 8.769539e-48 9.999983e-01 1.742560e-06 83 3.447181e-62 9.999664e-01 3.361987e-05 84 1.087302e-132 6.134355e-01 3.865645e-01 85 4.119852e-97 9.918297e-01 8.170260e-03 86 1.140835e-102 8.734107e-01 1.265893e-01 87 2.247339e-110 7.971795e-01 2.028205e-01 88 4.870630e-88 9.992978e-01 7.022084e-04 89 2.028672e-72 9.997620e-01 2.379898e-04 90 2.227900e-69 9.999461e-01 5.390514e-05 91 5.110709e-81 9.998510e-01 1.489819e-04 92 5.774841e-99 9.885399e-01 1.146006e-02 93 5.146736e-66 9.999591e-01 4.089540e-05 94 1.332816e-34 9.999997e-01 2.716264e-07 95 6.094144e-77 9.998034e-01 1.966331e-04 96 1.424276e-72 9.998236e-01 1.764463e-04 97 8.302641e-77 9.996692e-01 3.307548e-04 98 1.835520e-82 9.988601e-01 1.139915e-03 99 5.710350e-30 9.999997e-01 3.094739e-07 100 3.996459e-73 9.998204e-01 1.795726e-04 101 3.993755e-249 1.031032e-10 1.000000e+00 102 1.228659e-149 2.724406e-02 9.727559e-01 103 2.460661e-216 2.327488e-07 9.999998e-01 104 2.864831e-173 2.290954e-03 9.977090e-01 105 8.299884e-214 3.175384e-07 9.999997e-01 106 1.371182e-267 3.807455e-10 1.000000e+00 107 3.444090e-107 9.719885e-01 2.801154e-02 108 3.741929e-224 1.782047e-06 9.999982e-01 109 5.564644e-188 5.823191e-04 9.994177e-01 110 2.052443e-260 2.461662e-12 1.000000e+00 111 8.669405e-159 4.895235e-04 9.995105e-01 112 4.220200e-163 3.168643e-03 9.968314e-01 113 4.360059e-190 6.230821e-06 9.999938e-01 114 6.142256e-151 1.423414e-02 9.857659e-01 115 2.201426e-186 1.393247e-06 9.999986e-01 116 2.949945e-191 6.128385e-07 9.999994e-01 117 2.909076e-168 2.152843e-03 9.978472e-01 118 1.347608e-281 2.872996e-12 1.000000e+00 119 2.786402e-306 1.151469e-12 1.000000e+00 120 2.082510e-123 9.561626e-01 4.383739e-02 121 2.194169e-217 1.712166e-08 1.000000e+00 122 3.325791e-145 1.518718e-02 9.848128e-01 123 6.251357e-269 1.170872e-09 1.000000e+00 124 4.415135e-135 1.360432e-01 8.639568e-01 125 6.315716e-201 1.300512e-06 9.999987e-01 126 5.257347e-203 9.507989e-06 9.999905e-01 127 1.476391e-129 2.067703e-01 7.932297e-01 128 8.772841e-134 1.130589e-01 8.869411e-01 129 5.230800e-194 1.395719e-05 9.999860e-01 130 7.014892e-179 8.232518e-04 9.991767e-01 131 6.306820e-218 1.214497e-06 9.999988e-01 132 2.539020e-247 4.668891e-10 1.000000e+00 133 2.210812e-201 2.000316e-06 9.999980e-01 134 1.128613e-128 7.118948e-01 2.881052e-01 135 8.114869e-151 4.900992e-01 5.099008e-01 136 7.419068e-249 1.448050e-10 1.000000e+00 137 1.004503e-215 9.743357e-09 1.000000e+00 138 1.346716e-167 2.186989e-03 9.978130e-01 139 1.994716e-128 1.999894e-01 8.000106e-01 140 8.440466e-185 6.769126e-06 9.999932e-01 141 2.334365e-218 7.456220e-09 1.000000e+00 142 2.179139e-183 6.352663e-07 9.999994e-01 143 1.228659e-149 2.724406e-02 9.727559e-01 144 3.426814e-229 6.597015e-09 1.000000e+00 145 2.011574e-232 2.620636e-10 1.000000e+00 146 1.078519e-187 7.915543e-07 9.999992e-01 147 1.061392e-146 2.770575e-02 9.722942e-01 148 1.846900e-164 4.398402e-04 9.995602e-01 149 1.439996e-195 3.384156e-07 9.999997e-01 150 2.771480e-143 5.987903e-02 9.401210e-01 > > > > cleanEx(); ..nameEx <- "predict.meclight" > > ### * predict.meclight > > flush(stderr()); flush(stdout()) > > ### Name: predict.meclight > ### Title: Prediction of Minimal Error Classification > ### Aliases: predict.meclight > ### Keywords: classif > > ### ** Examples > > data(iris) > meclight.obj <- meclight(Species ~ ., data = iris) > predict(meclight.obj, iris) $class [1] setosa setosa setosa setosa setosa setosa [7] setosa setosa setosa setosa setosa setosa [13] setosa setosa setosa setosa setosa setosa [19] setosa setosa setosa setosa setosa setosa [25] setosa setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa setosa [37] setosa setosa setosa setosa setosa setosa [43] setosa setosa setosa setosa setosa setosa [49] setosa setosa versicolor versicolor versicolor versicolor [55] versicolor versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor versicolor [67] versicolor versicolor versicolor versicolor versicolor versicolor [73] virginica versicolor versicolor versicolor versicolor versicolor [79] versicolor versicolor versicolor versicolor versicolor virginica [85] versicolor versicolor versicolor versicolor versicolor versicolor [91] versicolor versicolor versicolor versicolor versicolor versicolor [97] versicolor versicolor versicolor versicolor virginica virginica [103] virginica virginica virginica virginica virginica virginica [109] virginica virginica virginica virginica virginica virginica [115] virginica virginica virginica virginica virginica virginica [121] virginica virginica virginica virginica virginica virginica [127] virginica virginica virginica virginica virginica virginica [133] virginica virginica virginica virginica virginica virginica [139] virginica virginica virginica virginica virginica virginica [145] virginica virginica virginica virginica virginica virginica Levels: setosa versicolor virginica $posterior setosa versicolor virginica 1 1.000000e+00 6.587188e-22 2.661011e-42 2 1.000000e+00 4.377620e-18 7.101397e-37 3 1.000000e+00 1.451476e-19 5.639184e-39 4 1.000000e+00 8.583139e-17 4.852731e-35 5 1.000000e+00 3.387305e-22 1.035177e-42 6 1.000000e+00 1.962706e-20 3.293735e-40 7 1.000000e+00 1.983422e-18 2.308176e-37 8 1.000000e+00 4.884269e-20 1.201581e-39 9 1.000000e+00 9.303250e-16 1.429578e-33 10 1.000000e+00 5.794984e-19 4.024501e-38 11 1.000000e+00 2.779412e-23 2.975224e-44 12 1.000000e+00 1.862316e-18 2.110704e-37 13 1.000000e+00 6.755820e-19 5.003697e-38 14 1.000000e+00 6.656717e-20 1.864777e-39 15 1.000000e+00 3.118142e-29 1.064377e-52 16 1.000000e+00 2.123660e-26 1.119332e-48 17 1.000000e+00 4.851855e-24 2.497089e-45 18 1.000000e+00 9.332940e-21 1.146581e-40 19 1.000000e+00 5.634630e-22 2.131826e-42 20 1.000000e+00 9.681502e-22 4.596811e-42 21 1.000000e+00 1.359062e-19 5.136400e-39 22 1.000000e+00 5.832643e-20 1.545795e-39 23 1.000000e+00 1.913996e-24 6.667785e-46 24 1.000000e+00 1.718202e-14 8.974010e-32 25 1.000000e+00 9.446347e-16 1.460893e-33 26 1.000000e+00 1.273366e-16 8.495046e-35 27 1.000000e+00 7.819397e-17 4.251409e-35 28 1.000000e+00 2.402583e-21 1.670340e-41 29 1.000000e+00 1.280990e-21 6.840354e-42 30 1.000000e+00 7.362415e-17 3.903078e-35 31 1.000000e+00 1.431746e-16 1.003319e-34 32 1.000000e+00 4.289458e-19 2.625716e-38 33 1.000000e+00 2.869028e-26 1.715628e-48 34 1.000000e+00 1.146665e-27 1.776087e-50 35 1.000000e+00 8.210520e-18 1.734084e-36 36 1.000000e+00 1.740995e-21 1.057393e-41 37 1.000000e+00 3.613510e-24 1.643469e-45 38 1.000000e+00 5.227504e-23 7.293937e-44 39 1.000000e+00 2.743429e-17 9.611849e-36 40 1.000000e+00 2.233783e-20 3.957743e-40 41 1.000000e+00 2.558822e-21 1.826613e-41 42 1.000000e+00 4.467690e-12 2.405584e-28 43 1.000000e+00 1.517348e-18 1.578092e-37 44 1.000000e+00 3.691526e-15 1.011374e-32 45 1.000000e+00 5.548920e-17 2.612579e-35 46 1.000000e+00 1.356173e-16 9.289819e-35 47 1.000000e+00 5.449556e-22 2.033116e-42 48 1.000000e+00 2.531076e-18 3.262761e-37 49 1.000000e+00 6.077311e-23 9.032856e-44 50 1.000000e+00 2.604156e-20 4.920697e-40 51 1.505641e-18 9.999066e-01 9.340634e-05 52 6.183175e-20 9.996436e-01 3.563931e-04 53 1.789846e-22 9.958828e-01 4.117166e-03 54 7.699278e-22 9.977617e-01 2.238293e-03 55 5.151785e-23 9.930851e-01 6.914852e-03 56 1.582671e-22 9.956661e-01 4.333899e-03 57 1.328589e-22 9.953381e-01 4.661888e-03 58 1.752222e-13 9.999993e-01 6.998921e-07 59 9.679269e-20 9.997047e-01 2.953309e-04 60 1.357346e-20 9.993270e-01 6.730058e-04 61 1.842652e-17 9.999673e-01 3.266302e-05 62 3.470394e-20 9.995459e-01 4.540533e-04 63 2.579612e-17 9.999716e-01 2.836335e-05 64 1.695655e-23 9.890415e-01 1.095853e-02 65 4.034164e-14 9.999987e-01 1.296095e-06 66 1.718218e-17 9.999664e-01 3.363531e-05 67 6.440845e-24 9.836778e-01 1.632217e-02 68 9.404402e-16 9.999937e-01 6.273679e-06 69 5.157813e-27 7.702969e-01 2.297031e-01 70 4.883760e-17 9.999783e-01 2.170009e-05 71 5.027849e-28 5.861033e-01 4.138967e-01 72 1.172165e-16 9.999850e-01 1.502924e-05 73 1.304744e-28 4.689150e-01 5.310850e-01 74 8.076103e-22 9.978060e-01 2.194006e-03 75 1.027179e-17 9.999583e-01 4.173805e-05 76 1.847962e-18 9.999143e-01 8.571420e-05 77 1.202197e-22 9.951398e-01 4.860197e-03 78 4.173014e-27 7.556791e-01 2.443209e-01 79 3.491209e-23 9.918731e-01 8.126937e-03 80 2.602509e-11 9.999999e-01 8.587049e-08 81 4.189169e-17 9.999769e-01 2.314270e-05 82 4.733603e-15 9.999968e-01 3.184609e-06 83 2.979657e-16 9.999898e-01 1.016123e-05 84 3.211440e-32 6.013507e-02 9.398649e-01 85 1.327081e-24 9.690013e-01 3.099868e-02 86 3.449078e-21 9.988051e-01 1.194884e-03 87 2.387582e-21 9.986061e-01 1.393877e-03 88 9.914037e-23 9.947334e-01 5.266586e-03 89 5.316800e-18 9.999450e-01 5.501924e-05 90 1.394256e-20 9.993345e-01 6.654797e-04 91 2.069763e-22 9.961250e-01 3.875006e-03 92 5.799203e-22 9.974801e-01 2.519894e-03 93 8.786387e-18 9.999554e-01 4.456473e-05 94 9.010386e-14 9.999991e-01 9.251607e-07 95 8.665066e-21 9.991877e-01 8.122910e-04 96 2.065384e-17 9.999689e-01 3.113618e-05 97 3.427806e-19 9.998262e-01 1.737705e-04 98 2.148386e-18 9.999195e-01 8.046539e-05 99 1.275272e-10 1.000000e+00 4.408225e-08 100 6.429339e-19 9.998665e-01 1.334750e-04 101 1.272853e-51 1.158252e-07 9.999999e-01 102 4.628467e-38 1.178522e-03 9.988215e-01 103 1.269645e-42 5.289824e-05 9.999471e-01 104 1.246932e-38 8.000333e-04 9.992000e-01 105 7.170942e-46 5.800530e-06 9.999942e-01 106 3.587453e-49 6.136354e-07 9.999994e-01 107 2.993506e-33 3.048299e-02 9.695170e-01 108 9.094476e-43 4.793091e-05 9.999521e-01 109 7.868928e-43 4.592373e-05 9.999541e-01 110 6.957803e-46 5.749032e-06 9.999943e-01 111 6.852153e-32 7.442440e-02 9.255756e-01 112 9.977267e-38 1.478545e-03 9.985215e-01 113 5.980867e-39 6.439485e-04 9.993561e-01 114 1.108125e-40 1.981699e-04 9.998018e-01 115 2.434964e-45 8.324935e-06 9.999917e-01 116 8.389799e-40 3.604354e-04 9.996396e-01 117 1.699307e-35 6.724793e-03 9.932752e-01 118 1.825416e-44 1.509903e-05 9.999849e-01 119 2.284734e-59 5.949293e-10 1.000000e+00 120 9.479412e-34 2.183610e-02 9.781639e-01 121 1.641177e-42 5.706715e-05 9.999429e-01 122 3.300123e-37 2.104689e-03 9.978953e-01 123 4.043074e-50 3.218875e-07 9.999997e-01 124 1.691628e-31 9.563584e-02 9.043642e-01 125 2.578724e-39 5.022402e-04 9.994978e-01 126 9.827270e-37 2.904065e-03 9.970959e-01 127 6.868950e-30 2.503938e-01 7.496062e-01 128 7.203370e-30 2.532538e-01 7.467462e-01 129 6.068410e-44 2.153481e-05 9.999785e-01 130 1.043457e-32 4.366623e-02 9.563338e-01 131 2.981990e-42 6.808237e-05 9.999319e-01 132 2.154238e-36 3.660300e-03 9.963397e-01 133 1.408390e-45 7.081214e-06 9.999929e-01 134 1.643873e-28 4.887628e-01 5.112372e-01 135 9.761405e-36 5.712593e-03 9.942874e-01 136 1.474656e-45 7.178096e-06 9.999928e-01 137 5.645717e-44 2.108016e-05 9.999789e-01 138 4.358777e-35 8.870051e-03 9.911299e-01 139 3.823473e-29 3.685529e-01 6.314471e-01 140 2.691841e-36 3.908740e-03 9.960913e-01 141 1.009096e-44 1.267260e-05 9.999873e-01 142 1.001654e-35 5.756145e-03 9.942439e-01 143 4.628467e-38 1.178522e-03 9.988215e-01 144 1.488485e-45 7.197925e-06 9.999928e-01 145 7.484936e-46 5.874466e-06 9.999941e-01 146 7.343720e-39 6.842077e-04 9.993158e-01 147 3.723860e-36 4.301033e-03 9.956990e-01 148 6.314812e-35 9.889947e-03 9.901101e-01 149 2.909336e-40 2.635816e-04 9.997364e-01 150 2.795424e-33 2.988536e-02 9.701146e-01 $x LD1 1 8.0617998 2 7.1286877 3 7.4898280 4 6.8132006 5 8.1323093 6 7.7019467 7 7.2126176 8 7.6052935 9 6.5605516 10 7.3430599 11 8.3973865 12 7.2192969 13 7.3267960 14 7.5724707 15 9.8498430 16 9.1582389 17 8.5824314 18 7.7807538 19 8.0783588 20 8.0209745 21 7.4968023 22 7.5864812 23 8.6810429 24 6.2514036 25 6.5589334 26 6.7713832 27 6.8230803 28 7.9246164 29 7.9912902 30 6.8294645 31 6.7589549 32 7.3749525 33 9.1263463 34 9.4676820 35 7.0620139 36 7.9587624 37 8.6136720 38 8.3304176 39 6.9341201 40 7.6882313 41 7.9179372 42 5.6618807 43 7.2410147 44 6.4144356 45 6.8594438 46 6.7647039 47 8.0818994 48 7.1867690 49 8.3144488 50 7.6719674 51 -1.4592755 52 -1.7977057 53 -2.4169489 54 -2.2624735 55 -2.5486784 56 -2.4299673 57 -2.4484846 58 -0.2226665 59 -1.7502012 60 -1.9584224 61 -1.1937603 62 -1.8589257 63 -1.1580939 64 -2.6660572 65 -0.3783672 66 -1.2011726 67 -2.7681025 68 -0.7768540 69 -3.4980543 70 -1.0904279 71 -3.7158961 72 -0.9976104 73 -3.8352593 74 -2.2574125 75 -1.2557133 76 -1.4375576 77 -2.4590614 78 -3.5184849 79 -2.5897987 80 0.3074879 81 -1.1066918 82 -0.6055246 83 -0.8987038 84 -4.4984664 85 -2.9339780 86 -2.1036082 87 -2.1425821 88 -2.4794560 89 -1.3255257 90 -1.9555789 91 -2.4015702 92 -2.2924888 93 -1.2722722 94 -0.2931761 95 -2.0059888 96 -1.1816631 97 -1.6161564 98 -1.4215888 99 0.4759738 100 -1.5494826 101 -7.8394740 102 -5.5074800 103 -6.2920085 104 -5.6054563 105 -6.8505600 106 -7.4181678 107 -4.6779954 108 -6.3169269 109 -6.3277368 110 -6.8528134 111 -4.4407251 112 -5.4500957 113 -5.6603371 114 -5.9582372 115 -6.7592628 116 -5.8070433 117 -5.0660123 118 -6.6088188 119 -9.1714749 120 -4.7645357 121 -6.2728391 122 -5.3607119 123 -7.5811998 124 -4.3715028 125 -5.7231753 126 -5.2791592 127 -4.0808721 128 -4.0770364 129 -6.5191040 130 -4.5837194 131 -6.2282401 132 -5.2204877 133 -6.8001500 134 -3.8151597 135 -5.1074897 136 -6.7967163 137 -6.5244960 138 -4.9955028 139 -3.9398530 140 -5.2038309 141 -6.6530868 142 -5.1055595 143 -5.5074800 144 -6.7960192 145 -6.8473594 146 -5.6450035 147 -5.1795646 148 -4.9677409 149 -5.8861454 150 -4.6831543 > > > > cleanEx(); ..nameEx <- "predict.rda" > > ### * predict.rda > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: predict.rda > ### Title: Regularized Discriminant Analysis (RDA) > ### Aliases: predict.rda > ### Keywords: multivariate > > ### ** Examples > > data(iris) > x <- rda(Species ~ ., data = iris, gamma = 0.05, lambda = 0.2) > predict(x, iris[, 1:4]) $class [1] setosa setosa setosa setosa setosa setosa [7] setosa setosa setosa setosa setosa setosa [13] setosa setosa setosa setosa setosa setosa [19] setosa setosa setosa setosa setosa setosa [25] setosa setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa setosa [37] setosa setosa setosa setosa setosa setosa [43] setosa setosa setosa setosa setosa setosa [49] setosa setosa versicolor versicolor versicolor versicolor [55] versicolor versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor versicolor [67] versicolor versicolor versicolor versicolor virginica versicolor [73] versicolor versicolor versicolor versicolor versicolor versicolor [79] versicolor versicolor versicolor versicolor versicolor virginica [85] versicolor versicolor versicolor versicolor versicolor versicolor [91] versicolor versicolor versicolor versicolor versicolor versicolor [97] versicolor versicolor versicolor versicolor virginica virginica [103] virginica virginica virginica virginica virginica virginica [109] virginica virginica virginica virginica virginica virginica [115] virginica virginica virginica virginica virginica virginica [121] virginica virginica virginica virginica virginica virginica [127] virginica virginica virginica virginica virginica virginica [133] virginica versicolor virginica virginica virginica virginica [139] virginica virginica virginica virginica virginica virginica [145] virginica virginica virginica virginica virginica virginica Levels: setosa versicolor virginica $posterior setosa versicolor virginica [1,] 1.000000e+00 3.374052e-22 2.238500e-36 [2,] 1.000000e+00 1.004371e-16 4.882247e-31 [3,] 1.000000e+00 6.190112e-19 1.536447e-32 [4,] 1.000000e+00 3.894442e-16 1.105635e-28 [5,] 1.000000e+00 5.871159e-23 1.573698e-36 [6,] 1.000000e+00 1.690683e-22 1.756676e-35 [7,] 1.000000e+00 1.130283e-18 6.200653e-31 [8,] 1.000000e+00 4.662823e-20 1.320080e-33 [9,] 1.000000e+00 1.136032e-14 3.399897e-27 [10,] 1.000000e+00 1.004444e-17 1.738683e-31 [11,] 1.000000e+00 2.874473e-24 9.290293e-39 [12,] 1.000000e+00 1.339773e-18 6.198687e-31 [13,] 1.000000e+00 2.357871e-17 2.207062e-31 [14,] 1.000000e+00 2.935151e-18 1.282383e-31 [15,] 1.000000e+00 2.303342e-31 4.377680e-48 [16,] 1.000000e+00 2.894819e-31 5.478051e-45 [17,] 1.000000e+00 3.090004e-26 1.114835e-40 [18,] 1.000000e+00 3.083450e-21 3.027448e-35 [19,] 1.000000e+00 3.083932e-23 1.074773e-37 [20,] 1.000000e+00 1.842475e-23 1.842665e-36 [21,] 1.000000e+00 3.931588e-19 2.711184e-33 [22,] 1.000000e+00 2.366874e-21 2.389675e-34 [23,] 1.000000e+00 1.104632e-24 3.244097e-38 [24,] 1.000000e+00 1.579444e-14 3.636831e-27 [25,] 1.000000e+00 1.008788e-15 1.110720e-26 [26,] 1.000000e+00 2.847415e-15 4.595319e-29 [27,] 1.000000e+00 3.454515e-17 5.558366e-30 [28,] 1.000000e+00 1.358255e-21 1.008652e-35 [29,] 1.000000e+00 2.253937e-21 3.774306e-36 [30,] 1.000000e+00 1.870069e-16 8.963785e-29 [31,] 1.000000e+00 8.296001e-16 1.121689e-28 [32,] 1.000000e+00 4.886127e-19 1.023649e-33 [33,] 1.000000e+00 3.528712e-29 7.213749e-42 [34,] 1.000000e+00 2.544073e-31 1.982600e-45 [35,] 1.000000e+00 7.037118e-17 1.392365e-30 [36,] 1.000000e+00 2.060715e-20 1.238251e-35 [37,] 1.000000e+00 7.434255e-24 4.497988e-40 [38,] 1.000000e+00 1.369176e-23 8.458276e-37 [39,] 1.000000e+00 2.836429e-16 4.776307e-29 [40,] 1.000000e+00 2.623165e-20 3.029926e-34 [41,] 1.000000e+00 8.779641e-22 8.758384e-36 [42,] 1.000000e+00 6.350675e-10 1.066769e-23 [43,] 1.000000e+00 5.498786e-18 2.083363e-30 [44,] 1.000000e+00 6.940621e-16 8.957600e-28 [45,] 1.000000e+00 2.325599e-18 1.157260e-29 [46,] 1.000000e+00 1.197851e-15 2.388076e-29 [47,] 1.000000e+00 1.431360e-23 3.212755e-36 [48,] 1.000000e+00 7.523665e-18 1.200723e-30 [49,] 1.000000e+00 5.134773e-24 4.102791e-38 [50,] 1.000000e+00 6.888647e-20 4.559252e-34 [51,] 7.619881e-43 9.997318e-01 2.682240e-04 [52,] 1.450095e-40 9.987631e-01 1.236947e-03 [53,] 2.362227e-49 9.939245e-01 6.075452e-03 [54,] 7.082365e-34 9.977098e-01 2.290230e-03 [55,] 4.359412e-45 9.943165e-01 5.683455e-03 [56,] 1.778753e-40 9.881763e-01 1.182373e-02 [57,] 6.166469e-46 9.852686e-01 1.473138e-02 [58,] 6.769142e-19 9.999904e-01 9.574436e-06 [59,] 2.275620e-41 9.993911e-01 6.089421e-04 [60,] 2.235754e-32 9.952018e-01 4.798210e-03 [61,] 1.443308e-23 9.999368e-01 6.322143e-05 [62,] 1.345399e-36 9.978208e-01 2.179167e-03 [63,] 1.148363e-30 9.999720e-01 2.799999e-05 [64,] 4.583235e-45 9.818697e-01 1.813034e-02 [65,] 6.790245e-24 9.999782e-01 2.175359e-05 [66,] 1.568749e-37 9.999175e-01 8.250134e-05 [67,] 4.613236e-43 9.572172e-01 4.278279e-02 [68,] 3.943309e-29 9.999216e-01 7.842504e-05 [69,] 1.924596e-46 9.190936e-01 8.090643e-02 [70,] 2.571732e-28 9.999364e-01 6.359993e-05 [71,] 1.440634e-53 3.889136e-01 6.110864e-01 [72,] 8.517216e-31 9.999635e-01 3.649101e-05 [73,] 5.534533e-53 7.016668e-01 2.983332e-01 [74,] 1.546270e-42 9.883513e-01 1.164871e-02 [75,] 1.570656e-35 9.999116e-01 8.844410e-05 [76,] 2.940986e-38 9.998402e-01 1.598288e-04 [77,] 9.437226e-48 9.954649e-01 4.535141e-03 [78,] 1.265592e-55 8.053851e-01 1.946149e-01 [79,] 8.639237e-43 9.868730e-01 1.312703e-02 [80,] 6.825222e-20 9.999996e-01 4.317328e-07 [81,] 2.903450e-27 9.999463e-01 5.372001e-05 [82,] 2.365955e-24 9.999889e-01 1.114212e-05 [83,] 2.104365e-28 9.999668e-01 3.318261e-05 [84,] 1.091105e-58 1.588245e-01 8.411755e-01 [85,] 1.270751e-43 9.143387e-01 8.566131e-02 [86,] 7.415916e-42 9.897451e-01 1.025491e-02 [87,] 3.875702e-45 9.976641e-01 2.335883e-03 [88,] 3.001787e-41 9.980155e-01 1.984518e-03 [89,] 4.860773e-32 9.994961e-01 5.038800e-04 [90,] 6.146972e-33 9.987520e-01 1.248037e-03 [91,] 2.941524e-38 9.879763e-01 1.202369e-02 [92,] 1.798928e-42 9.939163e-01 6.083657e-03 [93,] 1.005014e-30 9.999051e-01 9.492713e-05 [94,] 3.131376e-19 9.999936e-01 6.397911e-06 [95,] 2.030346e-35 9.979250e-01 2.075045e-03 [96,] 3.911021e-32 9.996288e-01 3.711689e-04 [97,] 3.101508e-34 9.991648e-01 8.351581e-04 [98,] 1.414739e-35 9.997877e-01 2.122658e-04 [99,] 4.089675e-15 9.999990e-01 1.022588e-06 [100,] 6.141397e-33 9.995101e-01 4.898967e-04 [101,] 2.767363e-102 2.631718e-07 9.999997e-01 [102,] 3.941395e-66 3.121039e-03 9.968790e-01 [103,] 6.628994e-88 1.897266e-04 9.998103e-01 [104,] 4.052230e-74 5.017620e-03 9.949824e-01 [105,] 1.071933e-88 2.670125e-05 9.999733e-01 [106,] 1.224547e-108 2.475180e-06 9.999975e-01 [107,] 1.118573e-51 2.454419e-02 9.754558e-01 [108,] 1.668595e-93 7.781144e-05 9.999222e-01 [109,] 9.528132e-82 2.810513e-04 9.997189e-01 [110,] 9.200983e-101 3.136694e-06 9.999969e-01 [111,] 6.946121e-64 3.794630e-02 9.620537e-01 [112,] 1.006358e-69 4.899731e-03 9.951003e-01 [113,] 1.828255e-77 8.950302e-04 9.991050e-01 [114,] 9.482035e-68 2.041397e-04 9.997959e-01 [115,] 9.921376e-81 9.027461e-08 9.999999e-01 [116,] 5.510354e-78 3.807128e-05 9.999619e-01 [117,] 2.078624e-70 2.502491e-02 9.749751e-01 [118,] 8.851902e-108 6.271618e-05 9.999373e-01 [119,] 2.612860e-126 1.770041e-09 1.000000e+00 [120,] 3.203035e-57 8.694135e-02 9.130586e-01 [121,] 6.147472e-87 3.578267e-05 9.999642e-01 [122,] 2.429092e-64 1.106121e-03 9.988939e-01 [123,] 2.549210e-111 5.736816e-07 9.999994e-01 [124,] 9.950080e-58 1.384121e-01 8.615879e-01 [125,] 6.997680e-81 1.460068e-03 9.985399e-01 [126,] 4.013168e-82 5.170859e-03 9.948291e-01 [127,] 5.464735e-55 2.421162e-01 7.578838e-01 [128,] 2.452232e-56 2.505623e-01 7.494377e-01 [129,] 5.319408e-82 8.390581e-05 9.999161e-01 [130,] 5.037102e-74 2.845196e-02 9.715480e-01 [131,] 1.927968e-90 1.967398e-04 9.998033e-01 [132,] 3.298328e-93 2.730162e-03 9.972698e-01 [133,] 8.027852e-85 1.392729e-05 9.999861e-01 [134,] 9.199376e-56 5.793808e-01 4.206192e-01 [135,] 1.142992e-68 3.106159e-03 9.968938e-01 [136,] 5.007892e-99 5.722710e-06 9.999943e-01 [137,] 6.409086e-88 6.838325e-06 9.999932e-01 [138,] 5.647382e-70 3.099884e-02 9.690012e-01 [139,] 2.573895e-54 2.870968e-01 7.129032e-01 [140,] 4.968089e-74 2.953225e-03 9.970468e-01 [141,] 1.512354e-88 1.987583e-06 9.999980e-01 [142,] 1.020049e-72 8.544253e-05 9.999146e-01 [143,] 3.941395e-66 3.121039e-03 9.968790e-01 [144,] 1.752433e-92 1.608792e-05 9.999839e-01 [145,] 3.621681e-93 5.354998e-07 9.999995e-01 [146,] 3.774501e-76 2.589253e-05 9.999741e-01 [147,] 7.441418e-64 6.323582e-03 9.936764e-01 [148,] 1.123578e-67 9.926968e-03 9.900730e-01 [149,] 5.416835e-80 6.750667e-05 9.999325e-01 [150,] 9.969191e-62 6.324651e-02 9.367535e-01 > > > > cleanEx(); ..nameEx <- "predict.sknn" > > ### * predict.sknn > > flush(stderr()); flush(stdout()) > > ### Name: predict.sknn > ### Title: Simple k Nearest Neighbours Classification > ### Aliases: predict.sknn > ### Keywords: classif > > ### ** Examples > > data(iris) > x <- sknn(Species ~ ., data = iris) > predict(x, iris) $posterior setosa versicolor virginica 1 1 0.0000000 0.0000000 2 1 0.0000000 0.0000000 3 1 0.0000000 0.0000000 4 1 0.0000000 0.0000000 5 1 0.0000000 0.0000000 6 1 0.0000000 0.0000000 7 1 0.0000000 0.0000000 8 1 0.0000000 0.0000000 9 1 0.0000000 0.0000000 10 1 0.0000000 0.0000000 11 1 0.0000000 0.0000000 12 1 0.0000000 0.0000000 13 1 0.0000000 0.0000000 14 1 0.0000000 0.0000000 15 1 0.0000000 0.0000000 16 1 0.0000000 0.0000000 17 1 0.0000000 0.0000000 18 1 0.0000000 0.0000000 19 1 0.0000000 0.0000000 20 1 0.0000000 0.0000000 21 1 0.0000000 0.0000000 22 1 0.0000000 0.0000000 23 1 0.0000000 0.0000000 24 1 0.0000000 0.0000000 25 1 0.0000000 0.0000000 26 1 0.0000000 0.0000000 27 1 0.0000000 0.0000000 28 1 0.0000000 0.0000000 29 1 0.0000000 0.0000000 30 1 0.0000000 0.0000000 31 1 0.0000000 0.0000000 32 1 0.0000000 0.0000000 33 1 0.0000000 0.0000000 34 1 0.0000000 0.0000000 35 1 0.0000000 0.0000000 36 1 0.0000000 0.0000000 37 1 0.0000000 0.0000000 38 1 0.0000000 0.0000000 39 1 0.0000000 0.0000000 40 1 0.0000000 0.0000000 41 1 0.0000000 0.0000000 42 1 0.0000000 0.0000000 43 1 0.0000000 0.0000000 44 1 0.0000000 0.0000000 45 1 0.0000000 0.0000000 46 1 0.0000000 0.0000000 47 1 0.0000000 0.0000000 48 1 0.0000000 0.0000000 49 1 0.0000000 0.0000000 50 1 0.0000000 0.0000000 51 0 1.0000000 0.0000000 52 0 1.0000000 0.0000000 53 0 1.0000000 0.0000000 54 0 1.0000000 0.0000000 55 0 1.0000000 0.0000000 56 0 1.0000000 0.0000000 57 0 1.0000000 0.0000000 58 0 1.0000000 0.0000000 59 0 1.0000000 0.0000000 60 0 1.0000000 0.0000000 61 0 1.0000000 0.0000000 62 0 1.0000000 0.0000000 63 0 1.0000000 0.0000000 64 0 1.0000000 0.0000000 65 0 1.0000000 0.0000000 66 0 1.0000000 0.0000000 67 0 1.0000000 0.0000000 68 0 1.0000000 0.0000000 69 0 1.0000000 0.0000000 70 0 1.0000000 0.0000000 71 0 0.3333333 0.6666667 72 0 1.0000000 0.0000000 73 0 0.3333333 0.6666667 74 0 1.0000000 0.0000000 75 0 1.0000000 0.0000000 76 0 1.0000000 0.0000000 77 0 1.0000000 0.0000000 78 0 1.0000000 0.0000000 79 0 1.0000000 0.0000000 80 0 1.0000000 0.0000000 81 0 1.0000000 0.0000000 82 0 1.0000000 0.0000000 83 0 1.0000000 0.0000000 84 0 0.3333333 0.6666667 85 0 1.0000000 0.0000000 86 0 1.0000000 0.0000000 87 0 1.0000000 0.0000000 88 0 1.0000000 0.0000000 89 0 1.0000000 0.0000000 90 0 1.0000000 0.0000000 91 0 1.0000000 0.0000000 92 0 1.0000000 0.0000000 93 0 1.0000000 0.0000000 94 0 1.0000000 0.0000000 95 0 1.0000000 0.0000000 96 0 1.0000000 0.0000000 97 0 1.0000000 0.0000000 98 0 1.0000000 0.0000000 99 0 1.0000000 0.0000000 100 0 1.0000000 0.0000000 101 0 0.0000000 1.0000000 102 0 0.0000000 1.0000000 103 0 0.0000000 1.0000000 104 0 0.0000000 1.0000000 105 0 0.0000000 1.0000000 106 0 0.0000000 1.0000000 107 0 0.6666667 0.3333333 108 0 0.0000000 1.0000000 109 0 0.0000000 1.0000000 110 0 0.0000000 1.0000000 111 0 0.0000000 1.0000000 112 0 0.0000000 1.0000000 113 0 0.0000000 1.0000000 114 0 0.0000000 1.0000000 115 0 0.0000000 1.0000000 116 0 0.0000000 1.0000000 117 0 0.0000000 1.0000000 118 0 0.0000000 1.0000000 119 0 0.0000000 1.0000000 120 0 0.6666667 0.3333333 121 0 0.0000000 1.0000000 122 0 0.0000000 1.0000000 123 0 0.0000000 1.0000000 124 0 0.0000000 1.0000000 125 0 0.0000000 1.0000000 126 0 0.0000000 1.0000000 127 0 0.0000000 1.0000000 128 0 0.0000000 1.0000000 129 0 0.0000000 1.0000000 130 0 0.0000000 1.0000000 131 0 0.0000000 1.0000000 132 0 0.0000000 1.0000000 133 0 0.0000000 1.0000000 134 0 0.6666667 0.3333333 135 0 0.3333333 0.6666667 136 0 0.0000000 1.0000000 137 0 0.0000000 1.0000000 138 0 0.0000000 1.0000000 139 0 0.3333333 0.6666667 140 0 0.0000000 1.0000000 141 0 0.0000000 1.0000000 142 0 0.0000000 1.0000000 143 0 0.0000000 1.0000000 144 0 0.0000000 1.0000000 145 0 0.0000000 1.0000000 146 0 0.0000000 1.0000000 147 0 0.0000000 1.0000000 148 0 0.0000000 1.0000000 149 0 0.0000000 1.0000000 150 0 0.0000000 1.0000000 $class [1] setosa setosa setosa setosa setosa setosa [7] setosa setosa setosa setosa setosa setosa [13] setosa setosa setosa setosa setosa setosa [19] setosa setosa setosa setosa setosa setosa [25] setosa setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa setosa [37] setosa setosa setosa setosa setosa setosa [43] setosa setosa setosa setosa setosa setosa [49] setosa setosa versicolor versicolor versicolor versicolor [55] versicolor versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor versicolor [67] versicolor versicolor versicolor versicolor virginica versicolor [73] virginica versicolor versicolor versicolor versicolor versicolor [79] versicolor versicolor versicolor versicolor versicolor virginica [85] versicolor versicolor versicolor versicolor versicolor versicolor [91] versicolor versicolor versicolor versicolor versicolor versicolor [97] versicolor versicolor versicolor versicolor virginica virginica [103] virginica virginica virginica virginica versicolor virginica [109] virginica virginica virginica virginica virginica virginica [115] virginica virginica virginica virginica virginica versicolor [121] virginica virginica virginica virginica virginica virginica [127] virginica virginica virginica virginica virginica virginica [133] virginica versicolor virginica virginica virginica virginica [139] virginica virginica virginica virginica virginica virginica [145] virginica virginica virginica virginica virginica virginica Levels: setosa versicolor virginica > x <- sknn(Species ~ ., gamma = 10, kn = 10, data = iris) > predict(x, iris) $posterior setosa versicolor virginica 1 1 0.000000000 0.00000000 2 1 0.000000000 0.00000000 3 1 0.000000000 0.00000000 4 1 0.000000000 0.00000000 5 1 0.000000000 0.00000000 6 1 0.000000000 0.00000000 7 1 0.000000000 0.00000000 8 1 0.000000000 0.00000000 9 1 0.000000000 0.00000000 10 1 0.000000000 0.00000000 11 1 0.000000000 0.00000000 12 1 0.000000000 0.00000000 13 1 0.000000000 0.00000000 14 1 0.000000000 0.00000000 15 1 0.000000000 0.00000000 16 1 0.000000000 0.00000000 17 1 0.000000000 0.00000000 18 1 0.000000000 0.00000000 19 1 0.000000000 0.00000000 20 1 0.000000000 0.00000000 21 1 0.000000000 0.00000000 22 1 0.000000000 0.00000000 23 1 0.000000000 0.00000000 24 1 0.000000000 0.00000000 25 1 0.000000000 0.00000000 26 1 0.000000000 0.00000000 27 1 0.000000000 0.00000000 28 1 0.000000000 0.00000000 29 1 0.000000000 0.00000000 30 1 0.000000000 0.00000000 31 1 0.000000000 0.00000000 32 1 0.000000000 0.00000000 33 1 0.000000000 0.00000000 34 1 0.000000000 0.00000000 35 1 0.000000000 0.00000000 36 1 0.000000000 0.00000000 37 1 0.000000000 0.00000000 38 1 0.000000000 0.00000000 39 1 0.000000000 0.00000000 40 1 0.000000000 0.00000000 41 1 0.000000000 0.00000000 42 1 0.000000000 0.00000000 43 1 0.000000000 0.00000000 44 1 0.000000000 0.00000000 45 1 0.000000000 0.00000000 46 1 0.000000000 0.00000000 47 1 0.000000000 0.00000000 48 1 0.000000000 0.00000000 49 1 0.000000000 0.00000000 50 1 0.000000000 0.00000000 51 0 1.000000000 0.00000000 52 0 1.000000000 0.00000000 53 0 1.000000000 0.00000000 54 0 1.000000000 0.00000000 55 0 1.000000000 0.00000000 56 0 1.000000000 0.00000000 57 0 0.911090325 0.08890968 58 0 1.000000000 0.00000000 59 0 1.000000000 0.00000000 60 0 1.000000000 0.00000000 61 0 1.000000000 0.00000000 62 0 1.000000000 0.00000000 63 0 1.000000000 0.00000000 64 0 0.889673791 0.11032621 65 0 1.000000000 0.00000000 66 0 1.000000000 0.00000000 67 0 1.000000000 0.00000000 68 0 1.000000000 0.00000000 69 0 0.956900725 0.04309928 70 0 1.000000000 0.00000000 71 0 0.514171530 0.48582847 72 0 1.000000000 0.00000000 73 0 0.585846716 0.41415328 74 0 1.000000000 0.00000000 75 0 1.000000000 0.00000000 76 0 1.000000000 0.00000000 77 0 0.988814993 0.01118501 78 0 0.770878873 0.22912113 79 0 0.960284271 0.03971573 80 0 1.000000000 0.00000000 81 0 1.000000000 0.00000000 82 0 1.000000000 0.00000000 83 0 1.000000000 0.00000000 84 0 0.399908335 0.60009167 85 0 1.000000000 0.00000000 86 0 0.971220874 0.02877913 87 0 1.000000000 0.00000000 88 0 1.000000000 0.00000000 89 0 1.000000000 0.00000000 90 0 1.000000000 0.00000000 91 0 1.000000000 0.00000000 92 0 0.969119486 0.03088051 93 0 1.000000000 0.00000000 94 0 1.000000000 0.00000000 95 0 1.000000000 0.00000000 96 0 1.000000000 0.00000000 97 0 1.000000000 0.00000000 98 0 1.000000000 0.00000000 99 0 1.000000000 0.00000000 100 0 1.000000000 0.00000000 101 0 0.000000000 1.00000000 102 0 0.071495668 0.92850433 103 0 0.000000000 1.00000000 104 0 0.000000000 1.00000000 105 0 0.000000000 1.00000000 106 0 0.000000000 1.00000000 107 0 0.010968272 0.98903173 108 0 0.000000000 1.00000000 109 0 0.000000000 1.00000000 110 0 0.000000000 1.00000000 111 0 0.063882748 0.93611725 112 0 0.000000000 1.00000000 113 0 0.000000000 1.00000000 114 0 0.019669958 0.98033004 115 0 0.000000000 1.00000000 116 0 0.000000000 1.00000000 117 0 0.000000000 1.00000000 118 0 0.000000000 1.00000000 119 0 0.000000000 1.00000000 120 0 0.199678914 0.80032109 121 0 0.000000000 1.00000000 122 0 0.030101848 0.96989815 123 0 0.000000000 1.00000000 124 0 0.150980979 0.84901902 125 0 0.000000000 1.00000000 126 0 0.000000000 1.00000000 127 0 0.122582095 0.87741791 128 0 0.202743657 0.79725634 129 0 0.000000000 1.00000000 130 0 0.000000000 1.00000000 131 0 0.000000000 1.00000000 132 0 0.000000000 1.00000000 133 0 0.000000000 1.00000000 134 0 0.319062468 0.68093753 135 0 0.041952968 0.95804703 136 0 0.000000000 1.00000000 137 0 0.000000000 1.00000000 138 0 0.000000000 1.00000000 139 0 0.290214364 0.70978564 140 0 0.000000000 1.00000000 141 0 0.000000000 1.00000000 142 0 0.006776379 0.99322362 143 0 0.071495668 0.92850433 144 0 0.000000000 1.00000000 145 0 0.000000000 1.00000000 146 0 0.000000000 1.00000000 147 0 0.112261876 0.88773812 148 0 0.054661231 0.94533877 149 0 0.000000000 1.00000000 150 0 0.157772529 0.84222747 $class [1] setosa setosa setosa setosa setosa setosa [7] setosa setosa setosa setosa setosa setosa [13] setosa setosa setosa setosa setosa setosa [19] setosa setosa setosa setosa setosa setosa [25] setosa setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa setosa [37] setosa setosa setosa setosa setosa setosa [43] setosa setosa setosa setosa setosa setosa [49] setosa setosa versicolor versicolor versicolor versicolor [55] versicolor versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor versicolor [67] versicolor versicolor versicolor versicolor versicolor versicolor [73] versicolor versicolor versicolor versicolor versicolor versicolor [79] versicolor versicolor versicolor versicolor versicolor virginica [85] versicolor versicolor versicolor versicolor versicolor versicolor [91] versicolor versicolor versicolor versicolor versicolor versicolor [97] versicolor versicolor versicolor versicolor virginica virginica [103] virginica virginica virginica virginica virginica virginica [109] virginica virginica virginica virginica virginica virginica [115] virginica virginica virginica virginica virginica virginica [121] virginica virginica virginica virginica virginica virginica [127] virginica virginica virginica virginica virginica virginica [133] virginica virginica virginica virginica virginica virginica [139] virginica virginica virginica virginica virginica virginica [145] virginica virginica virginica virginica virginica virginica Levels: setosa versicolor virginica > > > > cleanEx(); ..nameEx <- "predict.svmlight" > > ### * predict.svmlight > > flush(stderr()); flush(stdout()) > > ### Name: predict.svmlight > ### Title: Interface to SVMlight > ### Aliases: predict.svmlight > ### Keywords: classif > > ### ** Examples > > ## Not run: > ##D data(iris) > ##D x <- svmlight(Species ~ ., data = iris) > ##D predict(x, iris) > ## End(Not run) > > > > cleanEx(); ..nameEx <- "quadplot" > > ### * quadplot > > flush(stderr()); flush(stdout()) > > ### Name: quadplot > ### Title: Plotting of 4 dimensional membership representation simplex > ### Aliases: quadplot > ### Keywords: classif dplot > > ### ** Examples > > library("MASS") > data(B3) > opar <- par(mfrow = c(1, 2), pty = "s") > posterior <- predict(lda(PHASEN ~ ., data = B3))$post > s3d <- quadplot(posterior, col = rainbow(4)[B3$PHASEN], + labelpch = 22:25, labelcex = 0.8, + pch = (22:25)[apply(posterior, 1, which.max)], + main = "LDA posterior assignments") Loading required package: scatterplot3d > quadlines(centerlines(4), sp = s3d, lty = "dashed") > > posterior <- predict(qda(PHASEN ~ ., data = B3))$post > s3d <- quadplot(posterior, col = rainbow(4)[B3$PHASEN], + labelpch = 22:25, labelcex = 0.8, + pch = (22:25)[apply(posterior, 1, which.max)], + main = "QDA posterior assignments") > quadlines(centerlines(4), sp = s3d, lty = "dashed") > par(opar) > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "quadtrafo" > > ### * quadtrafo > > flush(stderr()); flush(stdout()) > > ### Name: quadtrafo > ### Title: Transforming of 4 dimensional values in a barycentric coordinate > ### system. > ### Aliases: quadtrafo quadpoints quadlines > ### Keywords: internal > > ### ** Examples > > library(MASS) > data(B3) > posterior <- predict(lda(PHASEN ~ ., data = B3))$post > quadtrafo(posterior) Loading required package: scatterplot3d x y z 1955,4 0.505902876 8.505599e-01 7.411580e-03 1956,1 0.501049744 8.541043e-01 1.426632e-02 1956,2 0.520474996 7.519525e-01 1.111627e-01 1956,3 0.500893414 3.974351e-01 6.602498e-01 1956,4 0.499980571 3.103573e-01 7.850949e-01 1957,1 0.848255029 2.022286e-01 8.379295e-02 1957,2 0.498547131 4.719213e-01 5.348851e-01 1957,3 0.634727657 3.246858e-01 4.224994e-01 1957,4 0.937487609 7.892180e-02 2.969824e-02 1958,1 0.246715263 4.816309e-02 1.052548e-01 1958,2 0.630262073 1.274122e-02 2.886703e-02 1958,3 0.662463627 8.110808e-02 3.881198e-02 1958,4 0.649445441 1.483289e-01 1.596159e-01 1959,1 0.717634332 1.962917e-01 1.579188e-01 1959,2 0.945102847 6.006299e-02 2.333722e-03 1959,3 0.972824439 2.923353e-02 4.951618e-03 1959,4 0.903402438 1.591626e-01 9.410183e-03 1960,1 0.982320226 3.052766e-02 1.249892e-04 1960,2 0.560105611 6.854489e-01 1.038382e-01 1960,3 0.543214254 6.792059e-01 1.522685e-01 1960,4 0.549338519 5.522103e-01 3.203097e-01 1961,1 0.852298211 1.987272e-01 7.784258e-02 1961,2 0.957263827 3.175848e-02 4.786019e-02 1961,3 0.476595850 2.531116e-01 5.450109e-01 1961,4 0.441241867 1.915096e-01 3.626243e-01 1962,1 0.698729327 1.040238e-01 1.648269e-01 1962,2 0.458202286 3.396174e-01 5.598797e-01 1962,3 0.881893998 9.903560e-02 1.347298e-01 1962,4 0.510123750 2.749008e-01 6.113168e-01 1963,1 0.506978891 1.980167e-01 5.264880e-01 1963,2 0.855724065 8.040643e-02 1.872514e-01 1963,3 0.698946423 1.756860e-01 3.986021e-01 1963,4 0.733090108 1.804988e-01 3.929193e-01 1964,1 0.896846746 4.231107e-02 3.048962e-02 1964,2 0.966083809 3.333093e-02 8.826650e-03 1964,3 0.770623572 1.480858e-01 1.778253e-01 1964,4 0.530401549 3.408106e-01 5.482723e-01 1965,1 0.914349305 7.642023e-02 4.230658e-02 1965,2 0.493155731 4.807691e-01 4.796660e-01 1965,3 0.494761109 3.241489e-01 7.482518e-01 1965,4 0.502111267 4.304795e-01 5.939806e-01 1966,1 0.462077282 3.095789e-01 6.108446e-01 1966,2 0.519594619 2.811426e-01 6.370225e-01 1966,3 0.268076118 1.223240e-01 3.026533e-01 1966,4 0.476406042 2.356777e-01 6.581383e-01 1967,1 0.032600187 1.746938e-02 4.931768e-02 1967,2 0.024768524 6.362371e-04 1.786025e-03 1967,3 0.027988686 6.151736e-04 1.694546e-03 1967,4 0.400189792 3.902432e-04 2.355451e-04 1968,1 0.877998934 2.828779e-03 6.033356e-03 1968,2 0.881677873 1.361381e-02 8.559603e-03 1968,3 0.991171814 6.370250e-03 9.880050e-04 1968,4 0.858090239 1.910219e-01 7.675495e-02 1969,1 0.925440353 1.257911e-01 7.577480e-04 1969,2 0.924392314 1.241272e-01 3.591169e-03 1969,3 0.603021425 6.469719e-01 3.381285e-02 1969,4 0.526605759 5.721543e-01 3.259298e-01 1970,1 0.500035801 3.152183e-01 7.786846e-01 1970,2 0.502203705 4.061127e-01 6.439200e-01 1970,3 0.498246071 3.063332e-01 7.860083e-01 1970,4 0.499337041 3.167831e-01 7.731848e-01 1971,1 0.510151406 4.256097e-01 4.037181e-01 1971,2 0.239930797 1.912655e-01 2.076041e-01 1971,3 0.351021807 2.200714e-01 5.433462e-01 1971,4 0.255967546 1.528273e-01 3.460599e-01 1972,1 0.188484690 1.435902e-01 2.103283e-01 1972,2 0.507041599 1.102298e-01 1.829916e-01 1972,3 0.139408060 7.003233e-02 1.024373e-01 1972,4 0.490449252 3.409998e-01 6.917238e-01 1973,1 0.492990776 3.970479e-01 6.138358e-01 1973,2 0.499910849 3.214508e-01 7.698905e-01 1973,3 0.499991313 2.945143e-01 8.081880e-01 1973,4 0.500018737 2.915941e-01 8.123023e-01 1974,1 0.500147686 2.884921e-01 8.023779e-01 1974,2 0.151672258 7.175120e-02 2.008022e-01 1974,3 0.012416583 7.042549e-03 1.937715e-02 1974,4 0.015891791 8.087489e-03 2.263184e-02 1975,1 0.004537027 2.044287e-03 5.663460e-03 1975,2 0.001063627 6.894697e-05 1.270248e-04 1975,3 0.001810446 3.598038e-04 8.392808e-04 1975,4 0.015669425 5.299465e-04 9.369413e-05 1976,1 0.564602272 4.203864e-02 2.228423e-03 1976,2 0.942135523 4.106008e-03 5.753101e-04 1976,3 0.852918347 5.631212e-03 3.390887e-03 1976,4 0.991912936 8.298515e-03 3.590155e-03 1977,1 0.951954298 2.672832e-02 2.407075e-02 1977,2 0.754683628 1.299270e-01 2.469223e-01 1977,3 0.631470732 2.660465e-01 3.122179e-01 1977,4 0.904870595 8.569136e-02 7.311843e-02 1978,1 0.903640780 8.770934e-02 7.448154e-02 1978,2 0.959494994 3.669446e-02 1.747616e-02 1978,3 0.957974146 1.633910e-02 5.783233e-03 1978,4 0.969287571 1.943921e-02 1.157289e-02 1979,1 0.992171641 9.162045e-03 2.027961e-03 1979,2 0.676386336 4.969858e-01 6.592790e-02 1979,3 0.709954022 3.430862e-01 2.166248e-01 1979,4 0.514381699 5.050357e-01 4.750722e-01 1980,1 0.529242549 6.718814e-01 1.976519e-01 1980,2 0.544581852 2.772270e-01 7.225458e-01 1980,3 0.500297531 2.967903e-01 8.038790e-01 1980,4 0.500769079 2.939765e-01 7.992359e-01 1981,1 0.500534351 3.057662e-01 7.836768e-01 1981,2 0.494542313 3.087142e-01 7.728855e-01 1981,3 0.476241917 3.055662e-01 7.207130e-01 1981,4 0.606980138 2.141172e-01 5.064339e-01 1982,1 0.472092346 2.155635e-01 5.657996e-01 1982,2 0.241973625 1.146131e-01 2.922933e-01 1982,3 0.302519292 1.037350e-01 2.818245e-01 1982,4 0.494458321 1.743401e-02 4.574237e-02 1983,1 0.454551057 7.218381e-03 1.125733e-02 1983,2 0.027559012 6.401284e-04 6.091409e-04 1983,3 0.038027761 1.378685e-03 7.956357e-04 1983,4 0.849303793 4.409577e-03 5.182130e-04 1984,1 0.971860016 9.369946e-03 9.918901e-03 1984,2 0.642108805 4.332587e-02 8.138207e-02 1984,3 0.949106969 1.003744e-02 4.339836e-03 1984,4 0.993504285 4.315708e-03 5.998508e-03 1985,1 0.996335673 3.543513e-03 2.100451e-03 1985,2 0.996195984 1.952571e-03 1.273366e-03 1985,3 0.974228523 1.596615e-02 1.531883e-02 1985,4 0.993741615 2.777482e-03 1.432488e-03 1986,1 0.997040226 1.294362e-03 1.040506e-03 1986,2 0.960369441 1.181122e-02 3.937338e-03 1986,3 0.979425182 8.001150e-03 8.711392e-03 1986,4 0.994747932 1.648390e-03 1.131877e-03 1987,1 0.997865728 1.582826e-03 8.580332e-04 1987,2 0.946003800 2.451552e-02 5.173203e-02 1987,3 0.945697054 2.196842e-02 2.846222e-02 1987,4 0.942817570 2.133282e-02 2.680806e-02 1988,1 0.990159258 9.399791e-03 3.623265e-03 1988,2 0.996551308 2.208566e-03 1.282655e-03 1988,3 0.963270508 3.834766e-02 1.870684e-02 1988,4 0.993393664 7.113739e-03 4.039194e-03 1989,1 0.955547336 5.980816e-02 2.024715e-02 1989,2 0.941285142 8.412980e-02 1.795043e-02 1989,3 0.943844110 6.243231e-02 4.502217e-02 1989,4 0.869626010 1.770422e-01 5.678636e-02 1990,1 0.971991094 4.761323e-02 4.673047e-04 1990,2 0.792738333 2.441020e-01 1.174447e-02 1990,3 0.527801235 8.123023e-01 2.676717e-03 1990,4 0.506421839 8.490182e-01 7.100716e-03 1991,1 0.500344580 8.645895e-01 1.170644e-03 1991,2 0.499644829 8.516987e-01 1.494193e-02 1991,3 0.538972780 7.189130e-01 1.023055e-01 1991,4 0.559779169 4.987891e-01 3.224627e-01 1992,1 0.706187464 2.664803e-01 3.385371e-01 1992,2 0.528694468 3.032983e-01 6.555406e-01 1992,3 0.506094415 3.060867e-01 7.135887e-01 1992,4 0.496635970 2.993713e-01 7.873498e-01 1993,1 0.497784297 2.873223e-01 8.116972e-01 1993,2 0.469209439 2.708383e-01 7.649864e-01 1993,3 0.487955583 2.821961e-01 7.896483e-01 1993,4 0.532740773 2.652777e-01 7.474265e-01 1994,1 0.475720615 2.656492e-01 7.108565e-01 1994,2 0.298731317 1.173712e-02 2.477121e-02 1994,3 0.868389933 2.976452e-03 2.779640e-03 1994,4 0.771248149 1.640027e-03 1.068292e-03 > > > > cleanEx(); ..nameEx <- "rda" > > ### * rda > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: rda > ### Title: Regularized Discriminant Analysis (RDA) > ### Aliases: rda rda.default rda.formula plot.rda print.rda > ### Keywords: multivariate > > ### ** Examples > > data(iris) > x <- rda(Species ~ ., data = iris, gamma = 0.05, lambda = 0.2) > predict(x, iris) $class [1] setosa setosa setosa setosa setosa setosa [7] setosa setosa setosa setosa setosa setosa [13] setosa setosa setosa setosa setosa setosa [19] setosa setosa setosa setosa setosa setosa [25] setosa setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa setosa [37] setosa setosa setosa setosa setosa setosa [43] setosa setosa setosa setosa setosa setosa [49] setosa setosa versicolor versicolor versicolor versicolor [55] versicolor versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor versicolor [67] versicolor versicolor versicolor versicolor virginica versicolor [73] versicolor versicolor versicolor versicolor versicolor versicolor [79] versicolor versicolor versicolor versicolor versicolor virginica [85] versicolor versicolor versicolor versicolor versicolor versicolor [91] versicolor versicolor versicolor versicolor versicolor versicolor [97] versicolor versicolor versicolor versicolor virginica virginica [103] virginica virginica virginica virginica virginica virginica [109] virginica virginica virginica virginica virginica virginica [115] virginica virginica virginica virginica virginica virginica [121] virginica virginica virginica virginica virginica virginica [127] virginica virginica virginica virginica virginica virginica [133] virginica versicolor virginica virginica virginica virginica [139] virginica virginica virginica virginica virginica virginica [145] virginica virginica virginica virginica virginica virginica Levels: setosa versicolor virginica $posterior setosa versicolor virginica [1,] 1.000000e+00 3.374052e-22 2.238500e-36 [2,] 1.000000e+00 1.004371e-16 4.882247e-31 [3,] 1.000000e+00 6.190112e-19 1.536447e-32 [4,] 1.000000e+00 3.894442e-16 1.105635e-28 [5,] 1.000000e+00 5.871159e-23 1.573698e-36 [6,] 1.000000e+00 1.690683e-22 1.756676e-35 [7,] 1.000000e+00 1.130283e-18 6.200653e-31 [8,] 1.000000e+00 4.662823e-20 1.320080e-33 [9,] 1.000000e+00 1.136032e-14 3.399897e-27 [10,] 1.000000e+00 1.004444e-17 1.738683e-31 [11,] 1.000000e+00 2.874473e-24 9.290293e-39 [12,] 1.000000e+00 1.339773e-18 6.198687e-31 [13,] 1.000000e+00 2.357871e-17 2.207062e-31 [14,] 1.000000e+00 2.935151e-18 1.282383e-31 [15,] 1.000000e+00 2.303342e-31 4.377680e-48 [16,] 1.000000e+00 2.894819e-31 5.478051e-45 [17,] 1.000000e+00 3.090004e-26 1.114835e-40 [18,] 1.000000e+00 3.083450e-21 3.027448e-35 [19,] 1.000000e+00 3.083932e-23 1.074773e-37 [20,] 1.000000e+00 1.842475e-23 1.842665e-36 [21,] 1.000000e+00 3.931588e-19 2.711184e-33 [22,] 1.000000e+00 2.366874e-21 2.389675e-34 [23,] 1.000000e+00 1.104632e-24 3.244097e-38 [24,] 1.000000e+00 1.579444e-14 3.636831e-27 [25,] 1.000000e+00 1.008788e-15 1.110720e-26 [26,] 1.000000e+00 2.847415e-15 4.595319e-29 [27,] 1.000000e+00 3.454515e-17 5.558366e-30 [28,] 1.000000e+00 1.358255e-21 1.008652e-35 [29,] 1.000000e+00 2.253937e-21 3.774306e-36 [30,] 1.000000e+00 1.870069e-16 8.963785e-29 [31,] 1.000000e+00 8.296001e-16 1.121689e-28 [32,] 1.000000e+00 4.886127e-19 1.023649e-33 [33,] 1.000000e+00 3.528712e-29 7.213749e-42 [34,] 1.000000e+00 2.544073e-31 1.982600e-45 [35,] 1.000000e+00 7.037118e-17 1.392365e-30 [36,] 1.000000e+00 2.060715e-20 1.238251e-35 [37,] 1.000000e+00 7.434255e-24 4.497988e-40 [38,] 1.000000e+00 1.369176e-23 8.458276e-37 [39,] 1.000000e+00 2.836429e-16 4.776307e-29 [40,] 1.000000e+00 2.623165e-20 3.029926e-34 [41,] 1.000000e+00 8.779641e-22 8.758384e-36 [42,] 1.000000e+00 6.350675e-10 1.066769e-23 [43,] 1.000000e+00 5.498786e-18 2.083363e-30 [44,] 1.000000e+00 6.940621e-16 8.957600e-28 [45,] 1.000000e+00 2.325599e-18 1.157260e-29 [46,] 1.000000e+00 1.197851e-15 2.388076e-29 [47,] 1.000000e+00 1.431360e-23 3.212755e-36 [48,] 1.000000e+00 7.523665e-18 1.200723e-30 [49,] 1.000000e+00 5.134773e-24 4.102791e-38 [50,] 1.000000e+00 6.888647e-20 4.559252e-34 [51,] 7.619881e-43 9.997318e-01 2.682240e-04 [52,] 1.450095e-40 9.987631e-01 1.236947e-03 [53,] 2.362227e-49 9.939245e-01 6.075452e-03 [54,] 7.082365e-34 9.977098e-01 2.290230e-03 [55,] 4.359412e-45 9.943165e-01 5.683455e-03 [56,] 1.778753e-40 9.881763e-01 1.182373e-02 [57,] 6.166469e-46 9.852686e-01 1.473138e-02 [58,] 6.769142e-19 9.999904e-01 9.574436e-06 [59,] 2.275620e-41 9.993911e-01 6.089421e-04 [60,] 2.235754e-32 9.952018e-01 4.798210e-03 [61,] 1.443308e-23 9.999368e-01 6.322143e-05 [62,] 1.345399e-36 9.978208e-01 2.179167e-03 [63,] 1.148363e-30 9.999720e-01 2.799999e-05 [64,] 4.583235e-45 9.818697e-01 1.813034e-02 [65,] 6.790245e-24 9.999782e-01 2.175359e-05 [66,] 1.568749e-37 9.999175e-01 8.250134e-05 [67,] 4.613236e-43 9.572172e-01 4.278279e-02 [68,] 3.943309e-29 9.999216e-01 7.842504e-05 [69,] 1.924596e-46 9.190936e-01 8.090643e-02 [70,] 2.571732e-28 9.999364e-01 6.359993e-05 [71,] 1.440634e-53 3.889136e-01 6.110864e-01 [72,] 8.517216e-31 9.999635e-01 3.649101e-05 [73,] 5.534533e-53 7.016668e-01 2.983332e-01 [74,] 1.546270e-42 9.883513e-01 1.164871e-02 [75,] 1.570656e-35 9.999116e-01 8.844410e-05 [76,] 2.940986e-38 9.998402e-01 1.598288e-04 [77,] 9.437226e-48 9.954649e-01 4.535141e-03 [78,] 1.265592e-55 8.053851e-01 1.946149e-01 [79,] 8.639237e-43 9.868730e-01 1.312703e-02 [80,] 6.825222e-20 9.999996e-01 4.317328e-07 [81,] 2.903450e-27 9.999463e-01 5.372001e-05 [82,] 2.365955e-24 9.999889e-01 1.114212e-05 [83,] 2.104365e-28 9.999668e-01 3.318261e-05 [84,] 1.091105e-58 1.588245e-01 8.411755e-01 [85,] 1.270751e-43 9.143387e-01 8.566131e-02 [86,] 7.415916e-42 9.897451e-01 1.025491e-02 [87,] 3.875702e-45 9.976641e-01 2.335883e-03 [88,] 3.001787e-41 9.980155e-01 1.984518e-03 [89,] 4.860773e-32 9.994961e-01 5.038800e-04 [90,] 6.146972e-33 9.987520e-01 1.248037e-03 [91,] 2.941524e-38 9.879763e-01 1.202369e-02 [92,] 1.798928e-42 9.939163e-01 6.083657e-03 [93,] 1.005014e-30 9.999051e-01 9.492713e-05 [94,] 3.131376e-19 9.999936e-01 6.397911e-06 [95,] 2.030346e-35 9.979250e-01 2.075045e-03 [96,] 3.911021e-32 9.996288e-01 3.711689e-04 [97,] 3.101508e-34 9.991648e-01 8.351581e-04 [98,] 1.414739e-35 9.997877e-01 2.122658e-04 [99,] 4.089675e-15 9.999990e-01 1.022588e-06 [100,] 6.141397e-33 9.995101e-01 4.898967e-04 [101,] 2.767363e-102 2.631718e-07 9.999997e-01 [102,] 3.941395e-66 3.121039e-03 9.968790e-01 [103,] 6.628994e-88 1.897266e-04 9.998103e-01 [104,] 4.052230e-74 5.017620e-03 9.949824e-01 [105,] 1.071933e-88 2.670125e-05 9.999733e-01 [106,] 1.224547e-108 2.475180e-06 9.999975e-01 [107,] 1.118573e-51 2.454419e-02 9.754558e-01 [108,] 1.668595e-93 7.781144e-05 9.999222e-01 [109,] 9.528132e-82 2.810513e-04 9.997189e-01 [110,] 9.200983e-101 3.136694e-06 9.999969e-01 [111,] 6.946121e-64 3.794630e-02 9.620537e-01 [112,] 1.006358e-69 4.899731e-03 9.951003e-01 [113,] 1.828255e-77 8.950302e-04 9.991050e-01 [114,] 9.482035e-68 2.041397e-04 9.997959e-01 [115,] 9.921376e-81 9.027461e-08 9.999999e-01 [116,] 5.510354e-78 3.807128e-05 9.999619e-01 [117,] 2.078624e-70 2.502491e-02 9.749751e-01 [118,] 8.851902e-108 6.271618e-05 9.999373e-01 [119,] 2.612860e-126 1.770041e-09 1.000000e+00 [120,] 3.203035e-57 8.694135e-02 9.130586e-01 [121,] 6.147472e-87 3.578267e-05 9.999642e-01 [122,] 2.429092e-64 1.106121e-03 9.988939e-01 [123,] 2.549210e-111 5.736816e-07 9.999994e-01 [124,] 9.950080e-58 1.384121e-01 8.615879e-01 [125,] 6.997680e-81 1.460068e-03 9.985399e-01 [126,] 4.013168e-82 5.170859e-03 9.948291e-01 [127,] 5.464735e-55 2.421162e-01 7.578838e-01 [128,] 2.452232e-56 2.505623e-01 7.494377e-01 [129,] 5.319408e-82 8.390581e-05 9.999161e-01 [130,] 5.037102e-74 2.845196e-02 9.715480e-01 [131,] 1.927968e-90 1.967398e-04 9.998033e-01 [132,] 3.298328e-93 2.730162e-03 9.972698e-01 [133,] 8.027852e-85 1.392729e-05 9.999861e-01 [134,] 9.199376e-56 5.793808e-01 4.206192e-01 [135,] 1.142992e-68 3.106159e-03 9.968938e-01 [136,] 5.007892e-99 5.722710e-06 9.999943e-01 [137,] 6.409086e-88 6.838325e-06 9.999932e-01 [138,] 5.647382e-70 3.099884e-02 9.690012e-01 [139,] 2.573895e-54 2.870968e-01 7.129032e-01 [140,] 4.968089e-74 2.953225e-03 9.970468e-01 [141,] 1.512354e-88 1.987583e-06 9.999980e-01 [142,] 1.020049e-72 8.544253e-05 9.999146e-01 [143,] 3.941395e-66 3.121039e-03 9.968790e-01 [144,] 1.752433e-92 1.608792e-05 9.999839e-01 [145,] 3.621681e-93 5.354998e-07 9.999995e-01 [146,] 3.774501e-76 2.589253e-05 9.999741e-01 [147,] 7.441418e-64 6.323582e-03 9.936764e-01 [148,] 1.123578e-67 9.926968e-03 9.900730e-01 [149,] 5.416835e-80 6.750667e-05 9.999325e-01 [150,] 9.969191e-62 6.324651e-02 9.367535e-01 > > > > cleanEx(); ..nameEx <- "shardsplot" > > ### * shardsplot > > flush(stderr()); flush(stdout()) > > ### Name: shardsplot > ### Title: Plotting Eight Direction Arranged Maps or Self-Organizing Maps > ### Aliases: shardsplot plot.EDAM > ### Keywords: hplot > > ### ** Examples > > # Compute clusters and an Eight Directions Arranged Map for the > # country data. Plotting the result. > data(countries) > logcount <- log(countries[,2:7]) > sdlogcount <- apply(logcount, 2, sd) > logstand <- t((t(logcount) / sdlogcount) * c(1,2,6,5,5,3)) > cclasses <- cutree(hclust(dist(logstand)), k = 6) > countryEDAM <- EDAM(logstand, classes = cclasses, sa = FALSE, + iter.max = 10, random = FALSE) 1 / 60 2 / 60 0.5819664 3 / 60 0.6019728 4 / 60 0.6032748 5 / 54 0.6032748 6 / 45 0.6032748 7 / 36 0.6032748 8 / 27 0.6032748 > plot(countryEDAM, vertices = FALSE, label = TRUE, stck = FALSE) > > # Compute and plot a Self-Organizing Map for the iris data > data(iris) > library(som) > irissom <- som(iris[,1:4], xdim = 6, ydim = 14) > shardsplot(irissom, data.or = iris, vertices = FALSE) > opar <- par(xpd = NA) > legend(7.5, 6.1, col = rainbow(3), xjust = 0.5, yjust = 0, + legend = levels(iris[, 5]), pch = 16, horiz = TRUE) > par(opar) > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "sknn" > > ### * sknn > > flush(stderr()); flush(stdout()) > > ### Name: sknn > ### Title: Simple k nearest Neighbours > ### Aliases: sknn sknn.default sknn.formula sknn.matrix sknn.data.frame > ### Keywords: classif > > ### ** Examples > > data(iris) > x <- sknn(Species ~ ., data = iris) > x <- sknn(Species ~ ., gamma = 4, data = iris) > > > > cleanEx(); ..nameEx <- "stepclass" > > ### * stepclass > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: stepclass > ### Title: Stepwise variable selection for classification > ### Aliases: stepclass stepclass.default stepclass.formula print.stepclass > ### plot.stepclass > ### Keywords: multivariate > > ### ** Examples > > data(iris) > library(MASS) > iris.d <- iris[,1:4] # the data > iris.c <- iris[,5] # the classes > sc_obj <- stepclass(iris.d, iris.c, "lda", start.vars = "Sepal.Width") `stepwise classification', using 10-fold cross-validated correctness rate of method lda'. 150 observations of 4 variables in 3 classes; direction: both stop criterion: improvement less than 5 %. correctness rate: 0.56667; starting variables (1): Sepal.Width correctness rate: 0.96; in: "Petal.Width"; variables (2): Sepal.Width, Petal.Width hr min sec 0.00 0.00 1.69 > sc_obj method : lda final model : iris.c ~ Sepal.Width + Petal.Width correctness rate = 0.96 > plot(sc_obj) > > ## or using formulas: > sc_obj <- stepclass(Species ~ ., data = iris, method = "qda", + start.vars = "Sepal.Width", criterion = "AS") # same as above `stepwise classification', using 10-fold cross-validated abiltity to seperate of method qda'. 150 observations of 4 variables in 3 classes; direction: both stop criterion: improvement less than 5 %. abiltity to seperate: 0.33515; starting variables (1): Sepal.Width abiltity to seperate: 0.946; in: "Petal.Width"; variables (2): Sepal.Width, Petal.Width hr min sec 0.00 0.00 1.84 > sc_obj method : qda final model : Species ~ Sepal.Width + Petal.Width abiltity to seperate = 0.946 > ## now you can say stuff like > ## qda(sc_obj$formula, data = B3) > > > > cleanEx(); ..nameEx <- "svmlight" > > ### * svmlight > > flush(stderr()); flush(stdout()) > > ### Name: svmlight > ### Title: Interface to SVMlight > ### Aliases: svmlight svmlight.default svmlight.formula svmlight.matrix > ### svmlight.data.frame svmlight.file > ### Keywords: classif > > ### ** Examples > > ## Not run: > ##D ## Only works if the svmlight binaries are in the path. > ##D data(iris) > ##D x <- svmlight(Species ~ ., data = iris) > ##D ## Using RBF-Kernel with gamma=0.1: > ##D data(B3) > ##D x <- svmlight(PHASEN ~ ., data = B3, svm.options = "-t 2 -g 0.1") > ## End(Not run) > > > > cleanEx(); ..nameEx <- "triframe" > > ### * triframe > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: triframe > ### Title: Barycentric plots > ### Aliases: triframe > ### Keywords: aplot > > ### ** Examples > > triplot(grid = TRUE, frame = FALSE) # plot without frame > some.triangle <- rbind(c(0, 0.65, 0.35), c(0.53, 0.47, 0), + c(0.72, 0, 0.28))[c(1:3, 1), ] > trilines(some.triangle, col = "red", pch = 16, type = "b") > triframe(label = c("left", "top", "right"), col = "blue", + label.col = "green3") # frame on top of points > > > > cleanEx(); ..nameEx <- "trigrid" > > ### * trigrid > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: trigrid > ### Title: Barycentric plots > ### Aliases: trigrid > ### Keywords: aplot > > ### ** Examples > > triplot(grid = FALSE) > trigrid(c(1/3, 0.5)) # same grid for all 3 dimensions > > triplot(grid = c(1/3, 0.5)) # (same effect) > > triplot(grid = FALSE) > # different grids for all dimensions: > trigrid(x = 1/3, y = 0.5, z = seq(0.2, 0.8, by=0.2)) > > triplot(grid = FALSE) > # grid for third dimension only: > trigrid(x = NA, y = NA, z = c(0.1, 0.2, 0.4, 0.8)) > > > > cleanEx(); ..nameEx <- "triperplines" > > ### * triperplines > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: triperplines > ### Title: Barycentric plots > ### Aliases: triperplines > ### Keywords: aplot > > ### ** Examples > > triplot() # empty plot > triperplines(1/2, 1/3, 1/6) > > > > cleanEx(); ..nameEx <- "triplot" > > ### * triplot > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: triplot > ### Title: Barycentric plots > ### Aliases: triplot > ### Keywords: aplot > > ### ** Examples > > # illustrating probabilities: > triplot(label = c("1, 2 or 3", "4 or 5", "6"), + main = "die rolls: probabilities", pch = 17) > triperplines(1/2, 1/3, 1/6) > > # expected... > triplot(1/2, 1/3, 1/6, label = c("1, 2 or 3", "4 or 5", "6"), + main = "die rolls: expected and observed frequencies", pch = 17) > # ... and observed frequencies. > dierolls <- matrix(sample(1:3, size = 50*20, prob = c(1/2, 1/3, 1/6), + replace = TRUE), ncol = 50) > frequencies <- t(apply(dierolls, 1, + function(x)(summary(factor(x, levels = 1:3)))) / 50) > tripoints(frequencies) > > # LDA classification posterior: > data(iris) > require(MASS) [1] TRUE > pred <- predict(lda(Species ~ ., data = iris),iris) > plotchar <- rep(1,150) > plotchar[pred$class != iris$Species] <- 19 > triplot(pred$posterior, label = colnames(pred$posterior), + main = "LDA posterior assignments", center = TRUE, + pch = plotchar, col = rep(c("blue", "green3", "red"), rep(50, 3)), + grid = TRUE) > legend(x = -0.6, y = 0.7, col = c("blue", "green3", "red"), + pch = 15, legend = colnames(pred$posterior)) > > > > cleanEx(); ..nameEx <- "tripoints" > > ### * tripoints > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: tripoints > ### Title: Barycentric plots > ### Aliases: tripoints trilines > ### Keywords: aplot > > ### ** Examples > > triplot() # empty plot > tripoints(0.1, 0.2, 0.7) # a point > tripoints(c(0.2, 0.6), c(0.3, 0.3), c(0.5, 0.1), + pch = c(2, 6)) # two points > trilines(c(0.1, 0.6), c(0.2, 0.3), c(0.7, 0.1), + col = "blue", lty = "dotted") # a line > > trilines(centerlines(3)) > > > > cleanEx(); ..nameEx <- "tritrafo" > > ### * tritrafo > > flush(stderr()); flush(stdout()) > > ### Encoding: latin1 > > ### Name: tritrafo > ### Title: Barycentric plots > ### Aliases: tritrafo > ### Keywords: dplot > > ### ** Examples > > tritrafo(0.1, 0.2, 0.7) x y [1,] 0.3464102 -0.1333333 > tritrafo(0.1, 0.2, 0.6) # warning Warning in tritrafo(0.1, 0.2, 0.6) : components do not sum to one x y [1,] 0.2886751 -0.1 > > triplot() > points(tritrafo(0.1, 0.2, 0.7), col="red") > tripoints(0.1, 0.2, 0.7, col="green") # the same > > tritrafo(c(0.1,0.2), c(0.3,0.4), c(0.6,0.4)) x y [1,] 0.2886751 -0.03333333 [2,] 0.1154701 0.06666667 > tritrafo(diag(3)) x y [1,] -5.773503e-01 -0.3333333 [2,] 7.544614e-17 0.6666667 [3,] 5.773503e-01 -0.3333333 > > point <- c(0.25,0.6,0.15) > triplot(point, pch=16) > text(tritrafo(point), "(0.25, 0.60, 0.15)", adj=c(0.5,2)) # add a label > > > > cleanEx(); ..nameEx <- "ucpm" > > ### * ucpm > > flush(stderr()); flush(stdout()) > > ### Name: ucpm > ### Title: Uschi's classification performance measures > ### Aliases: ucpm > ### Keywords: classif > > ### ** Examples > > library(MASS) > data(iris) > ucpm(predict(lda(Species ~ ., data = iris))$posterior, iris$Species) $CR [1] 0.98 $AC [1] 0.95274 $AS [1] 0.971972 $CF [1] 0.983818 $CFvec setosa versicolor virginica 1.0000000 0.9791587 0.9722954 > > > > ### *