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Type 'q()' to quit R. > ## VT::15.09.2013 - this will render the output independent > ## from the version of the package > suppressPackageStartupMessages(library(rrcov)) > library(MASS) > > ## VT::14.01.2020 > ## On some platforms minor differences are shown - use > ## IGNORE_RDIFF_BEGIN > ## IGNORE_RDIFF_END > > dodata <- function(method) { + + options(digits = 5) + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed + + tmp <- sys.call() + cat("\nCall: ", deparse(substitute(tmp)),"\n") + cat("===================================================\n") + + cat("\nData: ", "hemophilia\n") + data(hemophilia) + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) + show(predict(rlda)) + + cat("\nData: ", "anorexia\n") + data(anorexia) + show(rlda <- Linda(Treat~., data=anorexia, method=method)) + show(predict(rlda)) + + cat("\nData: ", "Pima\n") + data(Pima.tr) + show(rlda <- Linda(type~., data=Pima.tr, method=method)) + show(predict(rlda)) + + cat("\nData: ", "Forest soils\n") + data(soil) + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) + + ## This will not work within the function, of course + ## - comment it out + ## IGNORE_RDIFF_BEGIN + rlda <- Linda(F~., data=soil1983, method=method) + ## show(rlda) + ## IGNORE_RDIFF_END + show(predict(rlda)) + + cat("\nData: ", "Raven and Miller diabetes data\n") + data(diabetes) + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) + show(predict(rlda)) + + cat("\nData: ", "iris\n") + data(iris) + if(method != "mcdA") + { + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) + show(predict(rlda)) + } + + cat("\nData: ", "crabs\n") + data(crabs) + show(rlda <- Linda(sp~., data=crabs, method=method)) + show(predict(rlda)) + + cat("\nData: ", "fish\n") + data(fish) + fish <- fish[-14,] # remove observation #14 containing missing value + + # The height and width are calculated as percentages + # of the third length variable + fish[,5] <- fish[,5]*fish[,4]/100 + fish[,6] <- fish[,6]*fish[,4]/100 + + ## There is one class with only 6 observations (p=6). Normally + ## Linda will fail, therefore use l1med=TRUE. + ## This works only for methods mcdB and mcdC + + table(fish$Species) + if(method != "mcdA") + { + ## IGNORE_RDIFF_BEGIN + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) + ## show(rlda) + ## IGNORE_RDIFF_END + show(predict(rlda)) + } + + cat("\nData: ", "pottery\n") + data(pottery) + show(rlda <- Linda(origin~., data=pottery, method=method)) + show(predict(rlda)) + + cat("\nData: ", "olitos\n") + data(olitos) + if(method != "mcdA") + { + ## IGNORE_RDIFF_BEGIN + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) + ## show(rlda) + ## IGNORE_RDIFF_END + show(predict(rlda)) + } + + cat("===================================================\n") + } > > > ## -- now do it: > dodata(method="mcdA") Call: dodata(method = "mcdA") =================================================== Data: hemophilia Call: Linda(as.factor(gr) ~ ., data = hemophilia, method = method) Prior Probabilities of Groups: carrier normal 0.6 0.4 Group means: AHFactivity AHFantigen carrier -0.30795 -0.0059911 normal -0.12920 -0.0603000 Within-groups Covariance Matrix: AHFactivity AHFantigen AHFactivity 0.018036 0.011853 AHFantigen 0.011853 0.019185 Linear Coeficients: AHFactivity AHFantigen carrier -28.4029 17.2368 normal -8.5834 2.1602 Constants: carrier normal -4.8325 -1.4056 Apparent error rate 0.1333 Classification table Predicted Actual carrier normal carrier 39 6 normal 4 26 Confusion matrix Predicted Actual carrier normal carrier 0.867 0.133 normal 0.133 0.867 Data: anorexia Call: Linda(Treat ~ ., data = anorexia, method = method) Prior Probabilities of Groups: CBT Cont FT 0.40278 0.36111 0.23611 Group means: Prewt Postwt CBT 82.633 82.950 Cont 81.558 81.108 FT 84.331 94.762 Within-groups Covariance Matrix: Prewt Postwt Prewt 26.9291 3.3862 Postwt 3.3862 18.2368 Linear Coeficients: Prewt Postwt CBT 2.5563 4.0738 Cont 2.5284 3.9780 FT 2.5374 4.7250 Constants: CBT Cont FT -275.49 -265.45 -332.31 Apparent error rate 0.3889 Classification table Predicted Actual CBT Cont FT CBT 16 5 8 Cont 11 15 0 FT 0 4 13 Confusion matrix Predicted Actual CBT Cont FT CBT 0.552 0.172 0.276 Cont 0.423 0.577 0.000 FT 0.000 0.235 0.765 Data: Pima Call: Linda(type ~ ., data = Pima.tr, method = method) Prior Probabilities of Groups: No Yes 0.66 0.34 Group means: npreg glu bp skin bmi ped age No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 Within-groups Covariance Matrix: npreg glu bp skin bmi ped age npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 Linear Coeficients: npreg glu bp skin bmi ped age No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 Constants: No Yes -37.050 -51.586 Apparent error rate 0.22 Classification table Predicted Actual No Yes No 107 25 Yes 19 49 Confusion matrix Predicted Actual No Yes No 0.811 0.189 Yes 0.279 0.721 Data: Forest soils Apparent error rate 0.3103 Classification table Predicted Actual 1 2 3 1 7 2 2 2 3 13 7 3 1 3 20 Confusion matrix Predicted Actual 1 2 3 1 0.636 0.182 0.182 2 0.130 0.565 0.304 3 0.042 0.125 0.833 Data: Raven and Miller diabetes data Call: Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) Prior Probabilities of Groups: normal chemical overt 0.52414 0.24828 0.22759 Group means: insulin glucose sspg normal 163.939 345.8 99.076 chemical 299.448 476.9 223.621 overt 95.958 1026.4 343.000 Within-groups Covariance Matrix: insulin glucose sspg insulin 7582.0 -1263.1 1095.8 glucose -1263.1 18952.4 4919.3 sspg 1095.8 4919.3 3351.2 Linear Coeficients: insulin glucose sspg normal 0.027694 0.023859 -0.014514 chemical 0.040288 0.022532 0.020479 overt 0.017144 0.048768 0.025158 Constants: normal chemical overt -6.3223 -15.0879 -31.6445 Apparent error rate 0.1862 Classification table Predicted Actual normal chemical overt normal 69 7 0 chemical 13 23 0 overt 2 5 26 Confusion matrix Predicted Actual normal chemical overt normal 0.908 0.092 0.000 chemical 0.361 0.639 0.000 overt 0.061 0.152 0.788 Data: iris Data: crabs Call: Linda(sp ~ ., data = crabs, method = method) Prior Probabilities of Groups: B O 0.5 0.5 Group means: sexM index FL RW CL CW BD B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 Within-groups Covariance Matrix: sexM index FL RW CL CW BD sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 Linear Coeficients: sexM index FL RW CL CW BD B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 Constants: B O -78.317 -159.259 Apparent error rate 0 Classification table Predicted Actual B O B 100 0 O 0 100 Confusion matrix Predicted Actual B O B 1 0 O 0 1 Data: fish Data: pottery Call: Linda(origin ~ ., data = pottery, method = method) Prior Probabilities of Groups: Attic Eritrean 0.48148 0.51852 Group means: SI AL FE MG CA TI Attic 55.36 13.73 9.82 5.45 6.03 0.863 Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 Within-groups Covariance Matrix: SI AL FE MG CA TI SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 Linear Coeficients: SI AL FE MG CA TI Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 Constants: Attic Eritrean -1578.14 -901.13 Apparent error rate 0.1111 Classification table Predicted Actual Attic Eritrean Attic 12 1 Eritrean 2 12 Confusion matrix Predicted Actual Attic Eritrean Attic 0.923 0.077 Eritrean 0.143 0.857 Data: olitos =================================================== > dodata(method="mcdB") Call: dodata(method = "mcdB") =================================================== Data: hemophilia Call: Linda(as.factor(gr) ~ ., data = hemophilia, method = method) Prior Probabilities of Groups: carrier normal 0.6 0.4 Group means: AHFactivity AHFantigen carrier -0.31456 -0.014775 normal -0.13582 -0.069084 Within-groups Covariance Matrix: AHFactivity AHFantigen AHFactivity 0.0125319 0.0086509 AHFantigen 0.0086509 0.0182424 Linear Coeficients: AHFactivity AHFantigen carrier -36.486 16.4923 normal -12.226 2.0107 Constants: carrier normal -6.1276 -1.6771 Apparent error rate 0.16 Classification table Predicted Actual carrier normal carrier 38 7 normal 5 25 Confusion matrix Predicted Actual carrier normal carrier 0.844 0.156 normal 0.167 0.833 Data: anorexia Call: Linda(Treat ~ ., data = anorexia, method = method) Prior Probabilities of Groups: CBT Cont FT 0.40278 0.36111 0.23611 Group means: Prewt Postwt CBT 83.254 82.381 Cont 82.178 80.539 FT 84.951 94.193 Within-groups Covariance Matrix: Prewt Postwt Prewt 19.1751 8.8546 Postwt 8.8546 25.2326 Linear Coeficients: Prewt Postwt CBT 3.3822 2.0780 Cont 3.3555 2.0144 FT 3.2299 2.5996 Constants: CBT Cont FT -227.29 -220.01 -261.06 Apparent error rate 0.4444 Classification table Predicted Actual CBT Cont FT CBT 16 5 8 Cont 12 11 3 FT 0 4 13 Confusion matrix Predicted Actual CBT Cont FT CBT 0.552 0.172 0.276 Cont 0.462 0.423 0.115 FT 0.000 0.235 0.765 Data: Pima Call: Linda(type ~ ., data = Pima.tr, method = method) Prior Probabilities of Groups: No Yes 0.66 0.34 Group means: npreg glu bp skin bmi ped age No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 Within-groups Covariance Matrix: npreg glu bp skin bmi ped age npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 Linear Coeficients: npreg glu bp skin bmi ped age No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 Constants: No Yes -38.774 -53.654 Apparent error rate 0.25 Classification table Predicted Actual No Yes No 104 28 Yes 22 46 Confusion matrix Predicted Actual No Yes No 0.788 0.212 Yes 0.324 0.676 Data: Forest soils Apparent error rate 0.3448 Classification table Predicted Actual 1 2 3 1 4 3 4 2 2 14 7 3 2 2 20 Confusion matrix Predicted Actual 1 2 3 1 0.364 0.273 0.364 2 0.087 0.609 0.304 3 0.083 0.083 0.833 Data: Raven and Miller diabetes data Call: Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) Prior Probabilities of Groups: normal chemical overt 0.52414 0.24828 0.22759 Group means: insulin glucose sspg normal 152.405 346.55 99.387 chemical 288.244 478.80 226.226 overt 84.754 1028.28 345.605 Within-groups Covariance Matrix: insulin glucose sspg insulin 5061.46 289.69 2071.71 glucose 289.69 1983.07 385.31 sspg 2071.71 385.31 3000.17 Linear Coeficients: insulin glucose sspg normal 0.021952 0.17236 -0.0041671 chemical 0.034852 0.23217 0.0215200 overt -0.045700 0.50940 0.0813292 Constants: normal chemical overt -31.976 -64.433 -275.502 Apparent error rate 0.0966 Classification table Predicted Actual normal chemical overt normal 73 3 0 chemical 4 32 0 overt 0 7 26 Confusion matrix Predicted Actual normal chemical overt normal 0.961 0.039 0.000 chemical 0.111 0.889 0.000 overt 0.000 0.212 0.788 Data: iris Call: Linda(Species ~ ., data = iris, method = method, l1med = TRUE) Prior Probabilities of Groups: setosa versicolor virginica 0.33333 0.33333 0.33333 Group means: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 4.9834 3.4153 1.4532 0.22474 versicolor 5.8947 2.8149 4.2263 1.35024 virginica 6.5255 3.0017 5.4485 2.06756 Within-groups Covariance Matrix: Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length 0.201176 0.084299 0.102984 0.037019 Sepal.Width 0.084299 0.108394 0.050253 0.031757 Petal.Length 0.102984 0.050253 0.120215 0.045016 Petal.Width 0.037019 0.031757 0.045016 0.032825 Linear Coeficients: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 22.536 27.422168 -3.6855 -40.0445 versicolor 17.559 6.374082 24.1965 -18.0178 virginica 16.488 0.015576 29.9586 3.2926 Constants: setosa versicolor virginica -96.901 -100.790 -139.937 Apparent error rate 0.0267 Classification table Predicted Actual setosa versicolor virginica setosa 50 0 0 versicolor 0 48 2 virginica 0 2 48 Confusion matrix Predicted Actual setosa versicolor virginica setosa 1 0.00 0.00 versicolor 0 0.96 0.04 virginica 0 0.04 0.96 Data: crabs Call: Linda(sp ~ ., data = crabs, method = method) Prior Probabilities of Groups: B O 0.5 0.5 Group means: sexM index FL RW CL CW BD B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 Within-groups Covariance Matrix: sexM index FL RW CL CW BD sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 Linear Coeficients: sexM index FL RW CL CW BD B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 Constants: B O -66.688 -134.375 Apparent error rate 0 Classification table Predicted Actual B O B 100 0 O 0 100 Confusion matrix Predicted Actual B O B 1 0 O 0 1 Data: fish Apparent error rate 0.0949 Classification table Predicted Actual 1 2 3 4 5 6 7 1 34 0 0 0 0 0 0 2 0 6 0 0 0 0 0 3 0 0 20 0 0 0 0 4 0 0 0 11 0 0 0 5 0 0 0 0 13 0 1 6 0 0 0 0 0 17 0 7 0 13 0 0 1 0 42 Confusion matrix Predicted Actual 1 2 3 4 5 6 7 1 1 0.000 0 0 0.000 0 0.000 2 0 1.000 0 0 0.000 0 0.000 3 0 0.000 1 0 0.000 0 0.000 4 0 0.000 0 1 0.000 0 0.000 5 0 0.000 0 0 0.929 0 0.071 6 0 0.000 0 0 0.000 1 0.000 7 0 0.232 0 0 0.018 0 0.750 Data: pottery Call: Linda(origin ~ ., data = pottery, method = method) Prior Probabilities of Groups: Attic Eritrean 0.48148 0.51852 Group means: SI AL FE MG CA TI Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 Within-groups Covariance Matrix: SI AL FE MG CA TI SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 Linear Coeficients: SI AL FE MG CA TI Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 Constants: Attic Eritrean -3252.6 -1961.9 Apparent error rate 0.1111 Classification table Predicted Actual Attic Eritrean Attic 12 1 Eritrean 2 12 Confusion matrix Predicted Actual Attic Eritrean Attic 0.923 0.077 Eritrean 0.143 0.857 Data: olitos Apparent error rate 0.15 Classification table Predicted Actual 1 2 3 4 1 44 1 4 1 2 2 23 0 0 3 6 1 26 1 4 1 1 0 9 Confusion matrix Predicted Actual 1 2 3 4 1 0.880 0.020 0.080 0.020 2 0.080 0.920 0.000 0.000 3 0.176 0.029 0.765 0.029 4 0.091 0.091 0.000 0.818 =================================================== > dodata(method="mcdC") Call: dodata(method = "mcdC") =================================================== Data: hemophilia Call: Linda(as.factor(gr) ~ ., data = hemophilia, method = method) Prior Probabilities of Groups: carrier normal 0.6 0.4 Group means: AHFactivity AHFantigen carrier -0.32583 -0.011545 normal -0.12783 -0.071377 Within-groups Covariance Matrix: AHFactivity AHFantigen AHFactivity 0.0120964 0.0075536 AHFantigen 0.0075536 0.0164883 Linear Coeficients: AHFactivity AHFantigen carrier -37.117 16.30377 normal -11.015 0.71742 Constants: carrier normal -6.4636 -1.5947 Apparent error rate 0.16 Classification table Predicted Actual carrier normal carrier 38 7 normal 5 25 Confusion matrix Predicted Actual carrier normal carrier 0.844 0.156 normal 0.167 0.833 Data: anorexia Call: Linda(Treat ~ ., data = anorexia, method = method) Prior Probabilities of Groups: CBT Cont FT 0.40278 0.36111 0.23611 Group means: Prewt Postwt CBT 82.477 82.073 Cont 82.039 80.835 FT 85.242 94.750 Within-groups Covariance Matrix: Prewt Postwt Prewt 19.6589 8.3891 Postwt 8.3891 22.8805 Linear Coeficients: Prewt Postwt CBT 3.1590 2.4288 Cont 3.1599 2.3743 FT 3.0454 3.0245 Constants: CBT Cont FT -230.85 -226.60 -274.53 Apparent error rate 0.4583 Classification table Predicted Actual CBT Cont FT CBT 16 5 8 Cont 14 10 2 FT 0 4 13 Confusion matrix Predicted Actual CBT Cont FT CBT 0.552 0.172 0.276 Cont 0.538 0.385 0.077 FT 0.000 0.235 0.765 Data: Pima Call: Linda(type ~ ., data = Pima.tr, method = method) Prior Probabilities of Groups: No Yes 0.66 0.34 Group means: npreg glu bp skin bmi ped age No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 Within-groups Covariance Matrix: npreg glu bp skin bmi ped age npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 Linear Coeficients: npreg glu bp skin bmi ped age No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 Constants: No Yes -41.716 -55.056 Apparent error rate 0.235 Classification table Predicted Actual No Yes No 107 25 Yes 22 46 Confusion matrix Predicted Actual No Yes No 0.811 0.189 Yes 0.324 0.676 Data: Forest soils Apparent error rate 0.3276 Classification table Predicted Actual 1 2 3 1 5 2 4 2 2 13 8 3 1 2 21 Confusion matrix Predicted Actual 1 2 3 1 0.455 0.182 0.364 2 0.087 0.565 0.348 3 0.042 0.083 0.875 Data: Raven and Miller diabetes data Call: Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) Prior Probabilities of Groups: normal chemical overt 0.52414 0.24828 0.22759 Group means: insulin glucose sspg normal 167.31 348.69 106.44 chemical 247.18 478.18 213.36 overt 101.83 932.92 322.42 Within-groups Covariance Matrix: insulin glucose sspg insulin 4070.84 118.89 1701.54 glucose 118.89 2195.95 426.95 sspg 1701.54 426.95 2664.49 Linear Coeficients: insulin glucose sspg normal 0.041471 0.15888 -0.011992 chemical 0.048103 0.21216 0.015359 overt -0.013579 0.41323 0.063462 Constants: normal chemical overt -31.177 -59.703 -203.775 Apparent error rate 0.0828 Classification table Predicted Actual normal chemical overt normal 72 4 0 chemical 2 34 0 overt 0 6 27 Confusion matrix Predicted Actual normal chemical overt normal 0.947 0.053 0.000 chemical 0.056 0.944 0.000 overt 0.000 0.182 0.818 Data: iris Call: Linda(Species ~ ., data = iris, method = method, l1med = TRUE) Prior Probabilities of Groups: setosa versicolor virginica 0.33333 0.33333 0.33333 Group means: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 5.0163 3.4510 1.4653 0.2449 versicolor 5.9435 2.7891 4.2543 1.3239 virginica 6.3867 3.0033 5.3767 2.0700 Within-groups Covariance Matrix: Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length 0.186186 0.082478 0.094998 0.035445 Sepal.Width 0.082478 0.100137 0.049723 0.030678 Petal.Length 0.094998 0.049723 0.113105 0.043078 Petal.Width 0.035445 0.030678 0.043078 0.030885 Linear Coeficients: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 23.678 30.2896 -3.1124 -44.9900 versicolor 20.342 4.6372 27.3265 -23.2006 virginica 18.377 -2.0004 31.4235 4.0906 Constants: setosa versicolor virginica -104.96 -110.79 -145.49 Apparent error rate 0.0333 Classification table Predicted Actual setosa versicolor virginica setosa 50 0 0 versicolor 0 48 2 virginica 0 3 47 Confusion matrix Predicted Actual setosa versicolor virginica setosa 1 0.00 0.00 versicolor 0 0.96 0.04 virginica 0 0.06 0.94 Data: crabs Call: Linda(sp ~ ., data = crabs, method = method) Prior Probabilities of Groups: B O 0.5 0.5 Group means: sexM index FL RW CL CW BD B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 Within-groups Covariance Matrix: sexM index FL RW CL CW BD sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 Linear Coeficients: sexM index FL RW CL CW BD B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 Constants: B O -72.687 -142.365 Apparent error rate 0 Classification table Predicted Actual B O B 100 0 O 0 100 Confusion matrix Predicted Actual B O B 1 0 O 0 1 Data: fish Apparent error rate 0.0316 Classification table Predicted Actual 1 2 3 4 5 6 7 1 34 0 0 0 0 0 0 2 0 5 0 0 1 0 0 3 0 0 20 0 0 0 0 4 0 0 0 11 0 0 0 5 0 0 0 0 13 0 1 6 0 0 0 0 0 17 0 7 0 0 0 0 3 0 53 Confusion matrix Predicted Actual 1 2 3 4 5 6 7 1 1 0.000 0 0 0.000 0 0.000 2 0 0.833 0 0 0.167 0 0.000 3 0 0.000 1 0 0.000 0 0.000 4 0 0.000 0 1 0.000 0 0.000 5 0 0.000 0 0 0.929 0 0.071 6 0 0.000 0 0 0.000 1 0.000 7 0 0.000 0 0 0.054 0 0.946 Data: pottery Call: Linda(origin ~ ., data = pottery, method = method) Prior Probabilities of Groups: Attic Eritrean 0.48148 0.51852 Group means: SI AL FE MG CA TI Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 Within-groups Covariance Matrix: SI AL FE MG CA TI SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 Linear Coeficients: SI AL FE MG CA TI Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 Constants: Attic Eritrean -5924.2 -3802.9 Apparent error rate 0.1111 Classification table Predicted Actual Attic Eritrean Attic 12 1 Eritrean 2 12 Confusion matrix Predicted Actual Attic Eritrean Attic 0.923 0.077 Eritrean 0.143 0.857 Data: olitos Apparent error rate 0.1667 Classification table Predicted Actual 1 2 3 4 1 44 1 2 3 2 2 22 0 1 3 5 2 25 2 4 1 1 0 9 Confusion matrix Predicted Actual 1 2 3 4 1 0.880 0.020 0.040 0.060 2 0.080 0.880 0.000 0.040 3 0.147 0.059 0.735 0.059 4 0.091 0.091 0.000 0.818 =================================================== > dodata(method="mrcd") Call: dodata(method = "mrcd") =================================================== Data: hemophilia Call: Linda(as.factor(gr) ~ ., data = hemophilia, method = method) Prior Probabilities of Groups: carrier normal 0.6 0.4 Group means: AHFactivity AHFantigen carrier -0.34048 -0.055943 normal -0.13566 -0.081191 Within-groups Covariance Matrix: AHFactivity AHFantigen AHFactivity 0.0133676 0.0088055 AHFantigen 0.0088055 0.0221225 Linear Coeficients: AHFactivity AHFantigen carrier -32.264 10.31334 normal -10.478 0.50044 Constants: carrier normal -5.7149 -1.6067 Apparent error rate 0.16 Classification table Predicted Actual carrier normal carrier 38 7 normal 5 25 Confusion matrix Predicted Actual carrier normal carrier 0.844 0.156 normal 0.167 0.833 Data: anorexia Call: Linda(Treat ~ ., data = anorexia, method = method) Prior Probabilities of Groups: CBT Cont FT 0.40278 0.36111 0.23611 Group means: Prewt Postwt CBT 83.114 84.009 Cont 80.327 80.125 FT 85.161 94.371 Within-groups Covariance Matrix: Prewt Postwt Prewt 22.498 11.860 Postwt 11.860 20.426 Linear Coeficients: Prewt Postwt CBT 2.1994 2.8357 Cont 2.1653 2.6654 FT 1.9451 3.4907 Constants: CBT Cont FT -211.42 -194.77 -248.97 Apparent error rate 0.3889 Classification table Predicted Actual CBT Cont FT CBT 15 6 8 Cont 6 16 4 FT 0 4 13 Confusion matrix Predicted Actual CBT Cont FT CBT 0.517 0.207 0.276 Cont 0.231 0.615 0.154 FT 0.000 0.235 0.765 Data: Pima Call: Linda(type ~ ., data = Pima.tr, method = method) Prior Probabilities of Groups: No Yes 0.66 0.34 Group means: npreg glu bp skin bmi ped age No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 Within-groups Covariance Matrix: npreg glu bp skin bmi ped age npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 Linear Coeficients: npreg glu bp skin bmi ped age No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 Constants: No Yes -33.429 -45.434 Apparent error rate 0.28 Classification table Predicted Actual No Yes No 105 27 Yes 29 39 Confusion matrix Predicted Actual No Yes No 0.795 0.205 Yes 0.426 0.574 Data: Forest soils Apparent error rate 0.3448 Classification table Predicted Actual 1 2 3 1 7 2 2 2 4 14 5 3 3 4 17 Confusion matrix Predicted Actual 1 2 3 1 0.636 0.182 0.182 2 0.174 0.609 0.217 3 0.125 0.167 0.708 Data: Raven and Miller diabetes data Call: Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) Prior Probabilities of Groups: normal chemical overt 0.52414 0.24828 0.22759 Group means: insulin glucose sspg normal 154.014 346.07 91.606 chemical 248.841 451.10 221.936 overt 89.766 1064.16 335.100 Within-groups Covariance Matrix: insulin glucose sspg insulin 4948.1 1007.61 1471.12 glucose 1007.6 2597.38 358.57 sspg 1471.1 358.57 3180.04 Linear Coeficients: insulin glucose sspg normal 0.00027839 0.13121 0.013882 chemical 0.00148074 0.16615 0.050371 overt -0.10102404 0.43466 0.103100 Constants: normal chemical overt -24.008 -44.642 -245.497 Apparent error rate 0.0966 Classification table Predicted Actual normal chemical overt normal 71 5 0 chemical 2 34 0 overt 0 7 26 Confusion matrix Predicted Actual normal chemical overt normal 0.934 0.066 0.000 chemical 0.056 0.944 0.000 overt 0.000 0.212 0.788 Data: iris Call: Linda(Species ~ ., data = iris, method = method, l1med = TRUE) Prior Probabilities of Groups: setosa versicolor virginica 0.33333 0.33333 0.33333 Group means: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 4.9755 3.3826 1.4608 0.22053 versicolor 5.8868 2.7823 4.2339 1.34603 virginica 6.5176 2.9691 5.4560 2.06335 Within-groups Covariance Matrix: Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length 0.238417 0.136325 0.086377 0.036955 Sepal.Width 0.136325 0.148452 0.067500 0.034804 Petal.Length 0.086377 0.067500 0.100934 0.035968 Petal.Width 0.036955 0.034804 0.035968 0.023856 Linear Coeficients: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 17.106 15.693 7.8806 -52.031 versicolor 21.744 -15.813 38.0139 -11.505 virginica 23.032 -26.567 43.6391 23.777 Constants: setosa versicolor virginica -70.214 -115.832 -180.294 Apparent error rate 0.02 Classification table Predicted Actual setosa versicolor virginica setosa 50 0 0 versicolor 0 49 1 virginica 0 2 48 Confusion matrix Predicted Actual setosa versicolor virginica setosa 1 0.00 0.00 versicolor 0 0.98 0.02 virginica 0 0.04 0.96 Data: crabs Call: Linda(sp ~ ., data = crabs, method = method) Prior Probabilities of Groups: B O 0.5 0.5 Group means: sexM index FL RW CL CW BD B 0 25.5 13.270 12.138 28.102 32.624 11.816 O 1 25.5 16.626 12.262 33.688 37.188 15.324 Within-groups Covariance Matrix: sexM index FL RW CL CW BD sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 Linear Coeficients: sexM index FL RW CL CW BD B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 Constants: B O -2.1491e+01 -3.2776e+06 Apparent error rate 0.5 Classification table Predicted Actual B O B 50 50 O 50 50 Confusion matrix Predicted Actual B O B 0.5 0.5 O 0.5 0.5 Data: fish Apparent error rate 0.2532 Classification table Predicted Actual 1 2 3 4 5 6 7 1 33 0 0 1 0 0 0 2 0 3 0 0 0 0 3 3 0 2 5 0 0 0 13 4 0 0 0 11 0 0 0 5 0 0 0 0 14 0 0 6 0 0 0 0 0 17 0 7 0 19 0 0 2 0 35 Confusion matrix Predicted Actual 1 2 3 4 5 6 7 1 0.971 0.000 0.00 0.029 0.000 0 0.000 2 0.000 0.500 0.00 0.000 0.000 0 0.500 3 0.000 0.100 0.25 0.000 0.000 0 0.650 4 0.000 0.000 0.00 1.000 0.000 0 0.000 5 0.000 0.000 0.00 0.000 1.000 0 0.000 6 0.000 0.000 0.00 0.000 0.000 1 0.000 7 0.000 0.339 0.00 0.000 0.036 0 0.625 Data: pottery Call: Linda(origin ~ ., data = pottery, method = method) Prior Probabilities of Groups: Attic Eritrean 0.48148 0.51852 Group means: SI AL FE MG CA TI Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 Within-groups Covariance Matrix: SI AL FE MG CA TI SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 Linear Coeficients: SI AL FE MG CA TI Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 Constants: Attic Eritrean -4000.1 -3776.1 Apparent error rate 0.1111 Classification table Predicted Actual Attic Eritrean Attic 12 1 Eritrean 2 12 Confusion matrix Predicted Actual Attic Eritrean Attic 0.923 0.077 Eritrean 0.143 0.857 Data: olitos Apparent error rate 0.125 Classification table Predicted Actual 1 2 3 4 1 44 2 3 1 2 1 23 1 0 3 4 1 27 2 4 0 0 0 11 Confusion matrix Predicted Actual 1 2 3 4 1 0.880 0.040 0.060 0.020 2 0.040 0.920 0.040 0.000 3 0.118 0.029 0.794 0.059 4 0.000 0.000 0.000 1.000 =================================================== > dodata(method="ogk") Call: dodata(method = "ogk") =================================================== Data: hemophilia Call: Linda(as.factor(gr) ~ ., data = hemophilia, method = method) Prior Probabilities of Groups: carrier normal 0.6 0.4 Group means: AHFactivity AHFantigen carrier -0.29043 -0.00052902 normal -0.12463 -0.06715037 Within-groups Covariance Matrix: AHFactivity AHFantigen AHFactivity 0.015688 0.010544 AHFantigen 0.010544 0.016633 Linear Coeficients: AHFactivity AHFantigen carrier -32.2203 20.3935 normal -9.1149 1.7409 Constants: carrier normal -5.1843 -1.4259 Apparent error rate 0.1467 Classification table Predicted Actual carrier normal carrier 38 7 normal 4 26 Confusion matrix Predicted Actual carrier normal carrier 0.844 0.156 normal 0.133 0.867 Data: anorexia Call: Linda(Treat ~ ., data = anorexia, method = method) Prior Probabilities of Groups: CBT Cont FT 0.40278 0.36111 0.23611 Group means: Prewt Postwt CBT 82.634 82.060 Cont 81.605 80.459 FT 85.157 93.822 Within-groups Covariance Matrix: Prewt Postwt Prewt 15.8294 4.4663 Postwt 4.4663 19.6356 Linear Coeficients: Prewt Postwt CBT 4.3183 3.1970 Cont 4.2734 3.1256 FT 4.3080 3.7983 Constants: CBT Cont FT -310.50 -301.12 -363.05 Apparent error rate 0.4583 Classification table Predicted Actual CBT Cont FT CBT 15 5 9 Cont 14 11 1 FT 0 4 13 Confusion matrix Predicted Actual CBT Cont FT CBT 0.517 0.172 0.310 Cont 0.538 0.423 0.038 FT 0.000 0.235 0.765 Data: Pima Call: Linda(type ~ ., data = Pima.tr, method = method) Prior Probabilities of Groups: No Yes 0.66 0.34 Group means: npreg glu bp skin bmi ped age No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 Within-groups Covariance Matrix: npreg glu bp skin bmi ped age npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 Linear Coeficients: npreg glu bp skin bmi ped age No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 Constants: No Yes -43.449 -60.176 Apparent error rate 0.23 Classification table Predicted Actual No Yes No 108 24 Yes 22 46 Confusion matrix Predicted Actual No Yes No 0.818 0.182 Yes 0.324 0.676 Data: Forest soils Apparent error rate 0.3621 Classification table Predicted Actual 1 2 3 1 7 3 1 2 4 13 6 3 3 4 17 Confusion matrix Predicted Actual 1 2 3 1 0.636 0.273 0.091 2 0.174 0.565 0.261 3 0.125 0.167 0.708 Data: Raven and Miller diabetes data Call: Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) Prior Probabilities of Groups: normal chemical overt 0.52414 0.24828 0.22759 Group means: insulin glucose sspg normal 159.540 344.06 99.486 chemical 252.992 478.16 219.442 overt 79.635 1094.96 338.517 Within-groups Covariance Matrix: insulin glucose sspg insulin 3844.877 67.238 1456.55 glucose 67.238 2228.396 324.21 sspg 1456.548 324.205 2181.73 Linear Coeficients: insulin glucose sspg normal 0.040407 0.15379 -0.0042303 chemical 0.047858 0.20764 0.0377766 overt -0.026158 0.47736 0.1016873 Constants: normal chemical overt -30.115 -61.233 -278.996 Apparent error rate 0.0966 Classification table Predicted Actual normal chemical overt normal 71 5 0 chemical 2 34 0 overt 0 7 26 Confusion matrix Predicted Actual normal chemical overt normal 0.934 0.066 0.000 chemical 0.056 0.944 0.000 overt 0.000 0.212 0.788 Data: iris Call: Linda(Species ~ ., data = iris, method = method, l1med = TRUE) Prior Probabilities of Groups: setosa versicolor virginica 0.33333 0.33333 0.33333 Group means: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 4.9654 3.3913 1.4507 0.21639 versicolor 5.8767 2.7909 4.2238 1.34189 virginica 6.5075 2.9777 5.4459 2.05921 Within-groups Covariance Matrix: Sepal.Length Sepal.Width Petal.Length Petal.Width Sepal.Length 0.180280 0.068876 0.101512 0.036096 Sepal.Width 0.068876 0.079556 0.047722 0.029409 Petal.Length 0.101512 0.047722 0.111492 0.038658 Petal.Width 0.036096 0.029409 0.038658 0.029965 Linear Coeficients: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa 28.582 46.5236 -13.859 -54.9877 versicolor 19.837 11.9601 20.865 -17.7704 virginica 16.999 1.9046 29.808 7.9189 Constants: setosa versicolor virginica -134.94 -108.22 -148.56 Apparent error rate 0.0133 Classification table Predicted Actual setosa versicolor virginica setosa 50 0 0 versicolor 0 49 1 virginica 0 1 49 Confusion matrix Predicted Actual setosa versicolor virginica setosa 1 0.00 0.00 versicolor 0 0.98 0.02 virginica 0 0.02 0.98 Data: crabs Call: Linda(sp ~ ., data = crabs, method = method) Prior Probabilities of Groups: B O 0.5 0.5 Group means: sexM index FL RW CL CW BD B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 Within-groups Covariance Matrix: sexM index FL RW CL CW BD sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 Linear Coeficients: sexM index FL RW CL CW BD B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 Constants: B O -77.695 -147.287 Apparent error rate 0 Classification table Predicted Actual B O B 100 0 O 0 100 Confusion matrix Predicted Actual B O B 1 0 O 0 1 Data: fish Apparent error rate 0.0063 Classification table Predicted Actual 1 2 3 4 5 6 7 1 34 0 0 0 0 0 0 2 0 6 0 0 0 0 0 3 0 0 20 0 0 0 0 4 0 0 0 11 0 0 0 5 0 0 0 0 14 0 0 6 0 0 0 0 0 17 0 7 0 0 0 0 1 0 55 Confusion matrix Predicted Actual 1 2 3 4 5 6 7 1 1 0 0 0 0.000 0 0.000 2 0 1 0 0 0.000 0 0.000 3 0 0 1 0 0.000 0 0.000 4 0 0 0 1 0.000 0 0.000 5 0 0 0 0 1.000 0 0.000 6 0 0 0 0 0.000 1 0.000 7 0 0 0 0 0.018 0 0.982 Data: pottery Call: Linda(origin ~ ., data = pottery, method = method) Prior Probabilities of Groups: Attic Eritrean 0.48148 0.51852 Group means: SI AL FE MG CA TI Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 Within-groups Covariance Matrix: SI AL FE MG CA TI SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 Linear Coeficients: SI AL FE MG CA TI Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 Constants: Attic Eritrean -4346 -4139 Apparent error rate 0.1111 Classification table Predicted Actual Attic Eritrean Attic 12 1 Eritrean 2 12 Confusion matrix Predicted Actual Attic Eritrean Attic 0.923 0.077 Eritrean 0.143 0.857 Data: olitos Apparent error rate 0.1 Classification table Predicted Actual 1 2 3 4 1 45 2 2 1 2 0 25 0 0 3 4 1 27 2 4 0 0 0 11 Confusion matrix Predicted Actual 1 2 3 4 1 0.900 0.040 0.040 0.020 2 0.000 1.000 0.000 0.000 3 0.118 0.029 0.794 0.059 4 0.000 0.000 0.000 1.000 =================================================== > #dodata(method="fsa") > > proc.time() user system elapsed 3.98 0.50 4.46