R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1.3866 + ,126.64 + ,40.7819 + ,1.3582 + ,126.81 + ,39.5915 + ,1.3332 + ,125.84 + ,38.8859 + ,1.3595 + ,126.77 + ,39.9068 + ,1.3617 + ,124.34 + ,41.47 + ,1.3684 + ,124.4 + ,41.5613 + ,1.3394 + ,120.48 + ,41.6005 + ,1.3262 + ,118.54 + ,41.4113 + ,1.3173 + ,117.66 + ,41.84 + ,1.3085 + ,116.97 + ,42.2892 + ,1.327 + ,120.11 + ,43.1521 + ,1.3182 + ,119.16 + ,43.5998 + ,1.293 + ,116.9 + ,43.116 + ,1.291 + ,116.11 + ,42.4185 + ,1.2984 + ,114.98 + ,42.3687 + ,1.2795 + ,113.65 + ,42.2975 + ,1.299 + ,115.82 + ,42.8528 + ,1.3174 + ,117.59 + ,43.535 + ,1.326 + ,118.57 + ,44.7265 + ,1.3111 + ,118.07 + ,45.7293 + ,1.2816 + ,114.98 + ,45.7585 + ,1.276 + ,114.04 + ,46.1685 + ,1.2849 + ,115.02 + ,46.5075 + ,1.2818 + ,114.28 + ,46.527 + ,1.2829 + ,115.04 + ,46.601 + ,1.2796 + ,116.7 + ,46.4607 + ,1.3008 + ,119.21 + ,46.7135 + ,1.2967 + ,118.39 + ,46.4113 + ,1.2938 + ,116.5 + ,45.55 + ,1.2833 + ,115.46 + ,44.6081 + ,1.2823 + ,117.59 + ,44.4395 + ,1.2765 + ,117.33 + 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,1.2744 + ,106.86 + ,39.2537 + ,1.2697 + ,106.41 + ,39.2301 + ,1.2715 + ,106.46 + ,39.2763 + ,1.2725 + ,106.84 + ,39.282 + ,1.2801 + ,107.69 + ,39.3325 + ,1.285 + ,107.04 + ,39.557 + ,1.2989 + ,111.04 + ,40.1 + ,1.3078 + ,111.93 + ,40.5875 + ,1.306 + ,111.98 + ,40.485 + ,1.3074 + ,112.07 + ,40.55 + ,1.312 + ,112.05 + ,40.7955 + ,1.3364 + ,113.14 + ,41.456 + ,1.3323 + ,112.49 + ,41.3557 + ,1.3412 + ,113.2 + ,41.374 + ,1.3477 + ,113.52 + ,41.2235 + ,1.346 + ,113.22 + ,41.15 + ,1.3611 + ,113.85 + ,41.3725 + ,1.3648 + ,113.68 + ,41.6923 + ,1.3726 + ,114.26 + ,41.8 + ,1.3705 + ,114.1 + ,41.8045 + ,1.378 + ,114.8 + ,41.64 + ,1.3856 + ,114.98 + ,41.36 + ,1.397 + ,115.1 + ,41.5745 + ,1.3874 + ,114.21 + ,41.593 + ,1.3936 + ,114.24 + ,41.575 + ,1.3833 + ,113.35 + ,41.68 + ,1.3958 + ,114.23 + ,42.0055 + ,1.4101 + ,114.43 + ,42.3188 + ,1.4089 + ,114.28 + ,42.565 + ,1.3896 + ,113 + ,42.3575 + ,1.3859 + ,113.16 + ,42.29 + ,1.3861 + ,112.59 + ,42.695 + ,1.4016 + ,113.65 + ,43.0028 + ,1.3934 + ,113.18 + ,42.4507 + ,1.4031 + ,113.21 + ,42.4705 + ,1.3912 + ,113.11 + ,42.2875 + ,1.3803 + ,112.78 + ,42.3172 + ,1.3857 + ,112.57 + ,42.55 + ,1.3857 + ,111.87 + ,42.7523 + ,1.3926 + ,111.94 + ,42.8993 + ,1.4018 + ,113.18 + ,43.1555 + ,1.4014 + ,113.67 + ,43.1885 + ,1.4244 + ,115.15 + ,43.43 + ,1.4084 + ,114.41 + ,43.31 + ,1.3917 + ,112.88 + ,42.815 + ,1.3945 + ,112.44 + ,42.7017 + ,1.377 + ,113.48 + ,42.28 + ,1.37 + ,112.78 + ,41.922 + ,1.3711 + ,112.59 + ,42.17 + ,1.3626 + ,113.31 + ,42.1962 + ,1.3612 + ,113.21 + ,42.3215 + ,1.3481 + ,112.5 + ,42.3173 + ,1.3647 + ,113.72 + ,42.391 + ,1.3674 + ,114.09 + ,42.463 + ,1.3647 + ,113.97 + ,42.4125 + ,1.3496 + ,112.5 + ,42.304 + ,1.3339 + ,111.28 + ,41.813 + ,1.3321 + ,111.35 + ,41.651 + ,1.3225 + ,110.92 + ,41.539 + ,1.3146 + ,110.73 + ,41.1575 + ,1.2998 + ,109 + ,40.9545) + ,dim=c(3 + ,491) + ,dimnames=list(c('Dollar' + ,'Yen' + ,'Roebel') + ,1:491)) > y <- array(NA,dim=c(3,491),dimnames=list(c('Dollar','Yen','Roebel'),1:491)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '3' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Roebel" > x[,par1] [1] 40.7819 39.5915 38.8859 39.9068 41.4700 41.5613 41.6005 41.4113 41.8400 [10] 42.2892 43.1521 43.5998 43.1160 42.4185 42.3687 42.2975 42.8528 43.5350 [19] 44.7265 45.7293 45.7585 46.1685 46.5075 46.5270 46.6010 46.4607 46.7135 [28] 46.4113 45.5500 44.6081 44.4395 44.9847 45.7558 45.3942 45.6970 45.5664 [37] 46.0205 45.9195 45.8005 45.5350 45.4977 45.5782 45.7697 45.2445 45.0615 [46] 45.2865 44.7910 44.7625 44.7644 44.9973 44.7265 45.1465 44.7465 45.1795 [55] 45.6515 45.4920 45.2775 45.2115 45.4110 45.4005 44.7692 44.8913 45.0320 [64] 44.8790 44.8330 44.8257 44.7815 44.4790 44.6317 44.5043 44.3217 44.1005 [73] 44.0470 43.6835 43.7864 44.1807 43.9595 43.9370 43.9910 43.8650 43.6710 [82] 43.9300 43.8630 43.7095 43.9435 43.7360 43.6295 43.5980 43.8726 43.8935 [91] 43.5957 43.7155 43.5280 43.3415 43.3374 43.3320 43.3869 43.5016 43.4875 [100] 43.6023 43.3886 43.3105 43.4455 43.5185 43.5755 43.6217 43.6440 43.5789 [109] 43.5215 43.5033 43.6320 43.2630 43.3717 43.2745 43.2647 43.3240 43.4455 [118] 43.4098 43.4100 43.9300 43.8104 43.5400 43.8580 43.8375 43.8810 43.8870 [127] 43.8009 43.7877 43.8110 44.0625 44.1250 44.5200 45.4005 45.8900 45.1890 [136] 44.9035 44.9351 44.8010 43.9800 44.1100 44.2661 44.3610 44.0990 43.8435 [145] 43.8914 44.2170 44.5060 44.5400 44.4465 44.8420 44.8946 44.9510 45.4450 [154] 45.0035 45.7690 46.0900 45.4120 45.1200 45.4800 45.1050 45.0560 45.2200 [163] 45.3900 45.0410 44.9399 44.9315 45.1935 45.3466 45.4645 45.5685 45.3921 [172] 45.3400 45.1308 45.1005 45.3700 45.2000 44.9614 44.8015 44.9152 45.0950 [181] 44.9271 44.6026 44.5000 44.5400 44.5532 44.4070 44.2590 44.1365 44.1120 [190] 43.8814 43.9800 43.7294 43.9119 43.9550 43.9000 43.7065 43.6939 43.6587 [199] 43.5885 43.8885 43.8216 43.7510 43.6990 43.7425 43.6390 43.5890 43.6060 [208] 43.5325 43.3850 43.3745 43.2360 43.1957 43.0100 43.1401 43.0487 43.1972 [217] 43.2461 43.0866 43.0865 43.0194 43.0800 43.0070 42.9278 42.9545 42.7995 [226] 42.9048 42.9468 43.0800 43.1274 43.1625 43.4500 43.8310 43.7769 43.9800 [235] 43.9200 44.1100 44.0300 44.1582 44.1400 45.0700 44.8737 44.8505 44.3730 [244] 44.0750 43.9725 44.0940 44.1910 43.9685 43.7900 43.6041 43.1707 42.7100 [253] 42.7550 43.3316 43.5000 43.1540 43.1600 43.1000 42.8500 42.6175 42.5000 [262] 42.6285 42.6974 43.0400 42.6730 42.5015 42.5380 42.3735 42.0140 41.8618 [271] 42.1824 42.6050 42.7345 42.6150 42.4650 42.3400 42.2510 42.0475 41.8600 [280] 41.6850 41.7350 41.7060 41.7640 41.5800 41.3730 41.0880 41.1370 41.1587 [289] 41.1850 40.8190 40.6330 40.8580 40.7940 40.6900 40.5950 40.7305 40.5471 [298] 40.5145 40.7000 40.7000 40.5220 40.6165 40.3985 40.2815 40.2450 40.3055 [307] 40.2696 40.2510 40.1270 39.9500 39.6750 39.9540 39.8828 39.6200 39.5415 [316] 39.5250 39.8145 39.6675 39.6950 39.5985 39.2735 39.1435 39.1742 39.2025 [325] 39.3946 39.5025 39.4845 39.3300 39.2950 39.2675 39.2535 38.9845 38.9285 [334] 38.8592 38.7700 38.7900 38.8205 38.7577 38.8390 38.7800 38.5400 38.5110 [343] 38.6150 38.8980 38.8691 38.3840 38.0277 37.7200 37.7325 37.6260 37.6030 [352] 37.7800 38.5590 39.0459 38.4500 38.5050 38.2885 37.7950 37.9200 38.0340 [361] 38.0290 38.0630 37.9828 37.7450 37.9690 38.0070 38.0615 38.0912 38.0910 [370] 38.4310 38.4800 38.3500 38.2140 38.3840 38.1375 38.0075 38.0524 38.2350 [379] 38.3100 38.2615 38.1300 38.2820 38.5810 39.0801 39.0387 39.1015 39.1503 [388] 39.1400 39.0275 38.7665 38.6910 38.8490 39.1644 39.4907 39.5095 39.2795 [397] 39.0437 39.1355 39.1430 39.1850 39.3550 39.2970 39.4514 39.4173 39.4305 [406] 39.3840 39.3261 39.3010 39.3500 39.6400 39.4723 39.3685 39.1906 39.1183 [415] 39.1325 39.1144 39.1614 39.0908 38.9199 38.9130 38.9655 39.0290 39.0890 [424] 39.0700 39.0046 39.1038 39.3572 39.3880 39.3820 39.4398 39.2537 39.2301 [433] 39.2763 39.2820 39.3325 39.5570 40.1000 40.5875 40.4850 40.5500 40.7955 [442] 41.4560 41.3557 41.3740 41.2235 41.1500 41.3725 41.6923 41.8000 41.8045 [451] 41.6400 41.3600 41.5745 41.5930 41.5750 41.6800 42.0055 42.3188 42.5650 [460] 42.3575 42.2900 42.6950 43.0028 42.4507 42.4705 42.2875 42.3172 42.5500 [469] 42.7523 42.8993 43.1555 43.1885 43.4300 43.3100 42.8150 42.7017 42.2800 [478] 41.9220 42.1700 42.1962 42.3215 42.3173 42.3910 42.4630 42.4125 42.3040 [487] 41.8130 41.6510 41.5390 41.1575 40.9545 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$centers,1:par3),ncol=2) + clm <- clm[sort.list(clm[,1]),] + for (i in 1:par3) { + cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') + } + cl$cluster <- as.factor(cl$cluster) + print(cl$cluster) + x[,par1] <- cl$cluster + } > if (par2 == 'quantiles') { + x[,par1] <- cut2(x[,par1],g=par3) + } > if (par2 == 'hclust') { + hc <- hclust(dist(x[,par1])^2, 'cen') + print(hc) + memb <- cutree(hc, k = par3) + dum <- c(mean(x[memb==1,par1])) + for (i in 2:par3) { + dum <- c(dum, mean(x[memb==i,par1])) + } + hcm <- matrix(cbind(dum,1:par3),ncol=2) + hcm <- hcm[sort.list(hcm[,1]),] + for (i in 1:par3) { + memb[memb==hcm[i,2]] <- paste('C',i,sep='') + } + memb <- as.factor(memb) + print(memb) + x[,par1] <- memb + } > if (par2=='equal') { + ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) + x[,par1] <- as.factor(ed) + } > table(x[,par1]) 37.603 37.626 37.72 37.7325 37.745 37.78 37.795 37.92 37.969 37.9828 1 1 1 1 1 1 1 1 1 1 38.007 38.0075 38.0277 38.029 38.034 38.0524 38.0615 38.063 38.091 38.0912 1 1 1 1 1 1 1 1 1 1 38.13 38.1375 38.214 38.235 38.2615 38.282 38.2885 38.31 38.35 38.384 1 1 1 1 1 1 1 1 1 2 38.431 38.45 38.48 38.505 38.511 38.54 38.559 38.581 38.615 38.691 1 1 1 1 1 1 1 1 1 1 38.7577 38.7665 38.77 38.78 38.79 38.8205 38.839 38.849 38.8592 38.8691 1 1 1 1 1 1 1 1 1 1 38.8859 38.898 38.913 38.9199 38.9285 38.9655 38.9845 39.0046 39.0275 39.029 1 1 1 1 1 1 1 1 1 1 39.0387 39.0437 39.0459 39.07 39.0801 39.089 39.0908 39.1015 39.1038 39.1144 1 1 1 1 1 1 1 1 1 1 39.1183 39.1325 39.1355 39.14 39.143 39.1435 39.1503 39.1614 39.1644 39.1742 1 1 1 1 1 1 1 1 1 1 39.185 39.1906 39.2025 39.2301 39.2535 39.2537 39.2675 39.2735 39.2763 39.2795 1 1 1 1 1 1 1 1 1 1 39.282 39.295 39.297 39.301 39.3261 39.33 39.3325 39.35 39.355 39.3572 1 1 1 1 1 1 1 1 1 1 39.3685 39.382 39.384 39.388 39.3946 39.4173 39.4305 39.4398 39.4514 39.4723 1 1 1 1 1 1 1 1 1 1 39.4845 39.4907 39.5025 39.5095 39.525 39.5415 39.557 39.5915 39.5985 39.62 1 1 1 1 1 1 1 1 1 1 39.64 39.6675 39.675 39.695 39.8145 39.8828 39.9068 39.95 39.954 40.1 1 1 1 1 1 1 1 1 1 1 40.127 40.245 40.251 40.2696 40.2815 40.3055 40.3985 40.485 40.5145 40.522 1 1 1 1 1 1 1 1 1 1 40.5471 40.55 40.5875 40.595 40.6165 40.633 40.69 40.7 40.7305 40.7819 1 1 1 1 1 1 1 2 1 1 40.794 40.7955 40.819 40.858 40.9545 41.088 41.137 41.15 41.1575 41.1587 1 1 1 1 1 1 1 1 1 1 41.185 41.2235 41.3557 41.36 41.3725 41.373 41.374 41.4113 41.456 41.47 1 1 1 1 1 1 1 1 1 1 41.539 41.5613 41.5745 41.575 41.58 41.593 41.6005 41.64 41.651 41.68 1 1 1 1 1 1 1 1 1 1 41.685 41.6923 41.706 41.735 41.764 41.8 41.8045 41.813 41.84 41.86 1 1 1 1 1 1 1 1 1 1 41.8618 41.922 42.0055 42.014 42.0475 42.17 42.1824 42.1962 42.251 42.28 1 1 1 1 1 1 1 1 1 1 42.2875 42.2892 42.29 42.2975 42.304 42.3172 42.3173 42.3188 42.3215 42.34 1 1 1 1 1 1 1 1 1 1 42.3575 42.3687 42.3735 42.391 42.4125 42.4185 42.4507 42.463 42.465 42.4705 1 1 1 1 1 1 1 1 1 1 42.5 42.5015 42.538 42.55 42.565 42.605 42.615 42.6175 42.6285 42.673 1 1 1 1 1 1 1 1 1 1 42.695 42.6974 42.7017 42.71 42.7345 42.7523 42.755 42.7995 42.815 42.85 1 1 1 1 1 1 1 1 1 1 42.8528 42.8993 42.9048 42.9278 42.9468 42.9545 43.0028 43.007 43.01 43.0194 1 1 1 1 1 1 1 1 1 1 43.04 43.0487 43.08 43.0865 43.0866 43.1 43.116 43.1274 43.1401 43.1521 1 1 2 1 1 1 1 1 1 1 43.154 43.1555 43.16 43.1625 43.1707 43.1885 43.1957 43.1972 43.236 43.2461 1 1 1 1 1 1 1 1 1 1 43.263 43.2647 43.2745 43.31 43.3105 43.324 43.3316 43.332 43.3374 43.3415 1 1 1 1 1 1 1 1 1 1 43.3717 43.3745 43.385 43.3869 43.3886 43.4098 43.41 43.43 43.4455 43.45 1 1 1 1 1 1 1 1 2 1 43.4875 43.5 43.5016 43.5033 43.5185 43.5215 43.528 43.5325 43.535 43.54 1 1 1 1 1 1 1 1 1 1 43.5755 43.5789 43.5885 43.589 43.5957 43.598 43.5998 43.6023 43.6041 43.606 1 1 1 1 1 1 1 1 1 1 43.6217 43.6295 43.632 43.639 43.644 43.6587 43.671 43.6835 43.6939 43.699 1 1 1 1 1 1 1 1 1 1 43.7065 43.7095 43.7155 43.7294 43.736 43.7425 43.751 43.7769 43.7864 43.7877 1 1 1 1 1 1 1 1 1 1 43.79 43.8009 43.8104 43.811 43.8216 43.831 43.8375 43.8435 43.858 43.863 1 1 1 1 1 1 1 1 1 1 43.865 43.8726 43.881 43.8814 43.887 43.8885 43.8914 43.8935 43.9 43.9119 1 1 1 1 1 1 1 1 1 1 43.92 43.93 43.937 43.9435 43.955 43.9595 43.9685 43.9725 43.98 43.991 1 2 1 1 1 1 1 1 3 1 44.03 44.047 44.0625 44.075 44.094 44.099 44.1005 44.11 44.112 44.125 1 1 1 1 1 1 1 2 1 1 44.1365 44.14 44.1582 44.1807 44.191 44.217 44.259 44.2661 44.3217 44.361 1 1 1 1 1 1 1 1 1 1 44.373 44.407 44.4395 44.4465 44.479 44.5 44.5043 44.506 44.52 44.54 1 1 1 1 1 1 1 1 1 2 44.5532 44.6026 44.6081 44.6317 44.7265 44.7465 44.7625 44.7644 44.7692 44.7815 1 1 1 1 2 1 1 1 1 1 44.791 44.801 44.8015 44.8257 44.833 44.842 44.8505 44.8737 44.879 44.8913 1 1 1 1 1 1 1 1 1 1 44.8946 44.9035 44.9152 44.9271 44.9315 44.9351 44.9399 44.951 44.9614 44.9847 1 1 1 1 1 1 1 1 1 1 44.9973 45.0035 45.032 45.041 45.056 45.0615 45.07 45.095 45.1005 45.105 1 1 1 1 1 1 1 1 1 1 45.12 45.1308 45.1465 45.1795 45.189 45.1935 45.2 45.2115 45.22 45.2445 1 1 1 1 1 1 1 1 1 1 45.2775 45.2865 45.34 45.3466 45.37 45.39 45.3921 45.3942 45.4005 45.411 1 1 1 1 1 1 1 1 2 1 45.412 45.445 45.4645 45.48 45.492 45.4977 45.535 45.55 45.5664 45.5685 1 1 1 1 1 1 1 1 1 1 45.5782 45.6515 45.697 45.7293 45.7558 45.7585 45.769 45.7697 45.8005 45.89 1 1 1 1 1 1 1 1 1 1 45.9195 46.0205 46.09 46.1685 46.4113 46.4607 46.5075 46.527 46.601 46.7135 1 1 1 1 1 1 1 1 1 1 > colnames(x) [1] "Dollar" "Yen" "Roebel" > colnames(x)[par1] [1] "Roebel" > x[,par1] [1] 40.7819 39.5915 38.8859 39.9068 41.4700 41.5613 41.6005 41.4113 41.8400 [10] 42.2892 43.1521 43.5998 43.1160 42.4185 42.3687 42.2975 42.8528 43.5350 [19] 44.7265 45.7293 45.7585 46.1685 46.5075 46.5270 46.6010 46.4607 46.7135 [28] 46.4113 45.5500 44.6081 44.4395 44.9847 45.7558 45.3942 45.6970 45.5664 [37] 46.0205 45.9195 45.8005 45.5350 45.4977 45.5782 45.7697 45.2445 45.0615 [46] 45.2865 44.7910 44.7625 44.7644 44.9973 44.7265 45.1465 44.7465 45.1795 [55] 45.6515 45.4920 45.2775 45.2115 45.4110 45.4005 44.7692 44.8913 45.0320 [64] 44.8790 44.8330 44.8257 44.7815 44.4790 44.6317 44.5043 44.3217 44.1005 [73] 44.0470 43.6835 43.7864 44.1807 43.9595 43.9370 43.9910 43.8650 43.6710 [82] 43.9300 43.8630 43.7095 43.9435 43.7360 43.6295 43.5980 43.8726 43.8935 [91] 43.5957 43.7155 43.5280 43.3415 43.3374 43.3320 43.3869 43.5016 43.4875 [100] 43.6023 43.3886 43.3105 43.4455 43.5185 43.5755 43.6217 43.6440 43.5789 [109] 43.5215 43.5033 43.6320 43.2630 43.3717 43.2745 43.2647 43.3240 43.4455 [118] 43.4098 43.4100 43.9300 43.8104 43.5400 43.8580 43.8375 43.8810 43.8870 [127] 43.8009 43.7877 43.8110 44.0625 44.1250 44.5200 45.4005 45.8900 45.1890 [136] 44.9035 44.9351 44.8010 43.9800 44.1100 44.2661 44.3610 44.0990 43.8435 [145] 43.8914 44.2170 44.5060 44.5400 44.4465 44.8420 44.8946 44.9510 45.4450 [154] 45.0035 45.7690 46.0900 45.4120 45.1200 45.4800 45.1050 45.0560 45.2200 [163] 45.3900 45.0410 44.9399 44.9315 45.1935 45.3466 45.4645 45.5685 45.3921 [172] 45.3400 45.1308 45.1005 45.3700 45.2000 44.9614 44.8015 44.9152 45.0950 [181] 44.9271 44.6026 44.5000 44.5400 44.5532 44.4070 44.2590 44.1365 44.1120 [190] 43.8814 43.9800 43.7294 43.9119 43.9550 43.9000 43.7065 43.6939 43.6587 [199] 43.5885 43.8885 43.8216 43.7510 43.6990 43.7425 43.6390 43.5890 43.6060 [208] 43.5325 43.3850 43.3745 43.2360 43.1957 43.0100 43.1401 43.0487 43.1972 [217] 43.2461 43.0866 43.0865 43.0194 43.0800 43.0070 42.9278 42.9545 42.7995 [226] 42.9048 42.9468 43.0800 43.1274 43.1625 43.4500 43.8310 43.7769 43.9800 [235] 43.9200 44.1100 44.0300 44.1582 44.1400 45.0700 44.8737 44.8505 44.3730 [244] 44.0750 43.9725 44.0940 44.1910 43.9685 43.7900 43.6041 43.1707 42.7100 [253] 42.7550 43.3316 43.5000 43.1540 43.1600 43.1000 42.8500 42.6175 42.5000 [262] 42.6285 42.6974 43.0400 42.6730 42.5015 42.5380 42.3735 42.0140 41.8618 [271] 42.1824 42.6050 42.7345 42.6150 42.4650 42.3400 42.2510 42.0475 41.8600 [280] 41.6850 41.7350 41.7060 41.7640 41.5800 41.3730 41.0880 41.1370 41.1587 [289] 41.1850 40.8190 40.6330 40.8580 40.7940 40.6900 40.5950 40.7305 40.5471 [298] 40.5145 40.7000 40.7000 40.5220 40.6165 40.3985 40.2815 40.2450 40.3055 [307] 40.2696 40.2510 40.1270 39.9500 39.6750 39.9540 39.8828 39.6200 39.5415 [316] 39.5250 39.8145 39.6675 39.6950 39.5985 39.2735 39.1435 39.1742 39.2025 [325] 39.3946 39.5025 39.4845 39.3300 39.2950 39.2675 39.2535 38.9845 38.9285 [334] 38.8592 38.7700 38.7900 38.8205 38.7577 38.8390 38.7800 38.5400 38.5110 [343] 38.6150 38.8980 38.8691 38.3840 38.0277 37.7200 37.7325 37.6260 37.6030 [352] 37.7800 38.5590 39.0459 38.4500 38.5050 38.2885 37.7950 37.9200 38.0340 [361] 38.0290 38.0630 37.9828 37.7450 37.9690 38.0070 38.0615 38.0912 38.0910 [370] 38.4310 38.4800 38.3500 38.2140 38.3840 38.1375 38.0075 38.0524 38.2350 [379] 38.3100 38.2615 38.1300 38.2820 38.5810 39.0801 39.0387 39.1015 39.1503 [388] 39.1400 39.0275 38.7665 38.6910 38.8490 39.1644 39.4907 39.5095 39.2795 [397] 39.0437 39.1355 39.1430 39.1850 39.3550 39.2970 39.4514 39.4173 39.4305 [406] 39.3840 39.3261 39.3010 39.3500 39.6400 39.4723 39.3685 39.1906 39.1183 [415] 39.1325 39.1144 39.1614 39.0908 38.9199 38.9130 38.9655 39.0290 39.0890 [424] 39.0700 39.0046 39.1038 39.3572 39.3880 39.3820 39.4398 39.2537 39.2301 [433] 39.2763 39.2820 39.3325 39.5570 40.1000 40.5875 40.4850 40.5500 40.7955 [442] 41.4560 41.3557 41.3740 41.2235 41.1500 41.3725 41.6923 41.8000 41.8045 [451] 41.6400 41.3600 41.5745 41.5930 41.5750 41.6800 42.0055 42.3188 42.5650 [460] 42.3575 42.2900 42.6950 43.0028 42.4507 42.4705 42.2875 42.3172 42.5500 [469] 42.7523 42.8993 43.1555 43.1885 43.4300 43.3100 42.8150 42.7017 42.2800 [478] 41.9220 42.1700 42.1962 42.3215 42.3173 42.3910 42.4630 42.4125 42.3040 [487] 41.8130 41.6510 41.5390 41.1575 40.9545 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/1l7dh1292970753.tab") + } + } > m Conditional inference tree with 6 terminal nodes Response: Roebel Inputs: Dollar, Yen Number of observations: 491 1) Yen <= 126.81; criterion = 1, statistic = 210.438 2) Dollar <= 1.2548; criterion = 1, statistic = 24.943 3) Dollar <= 1.2271; criterion = 0.976, statistic = 6.314 4)* weights = 19 3) Dollar > 1.2271 5)* weights = 18 2) Dollar > 1.2548 6)* weights = 231 1) Yen > 126.81 7) Dollar <= 1.4783; criterion = 0.959, statistic = 5.343 8) Yen <= 127.74; criterion = 0.988, statistic = 7.52 9)* weights = 8 8) Yen > 127.74 10)* weights = 178 7) Dollar > 1.4783 11)* weights = 37 > postscript(file="/var/www/html/freestat/rcomp/tmp/2l7dh1292970753.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/www/html/freestat/rcomp/tmp/3ezu21292970753.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > if (par2 == 'none') { + forec <- predict(m) + result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) + colnames(result) <- c('Actuals','Forecasts','Residuals') + print(result) + } Actuals Forecasts Residuals 1 40.7819 41.26518 -0.4832800866 2 39.5915 41.26518 -1.6736800866 3 38.8859 41.26518 -2.3792800866 4 39.9068 41.26518 -1.3583800866 5 41.4700 41.26518 0.2048199134 6 41.5613 41.26518 0.2961199134 7 41.6005 41.26518 0.3353199134 8 41.4113 41.26518 0.1461199134 9 41.8400 41.26518 0.5748199134 10 42.2892 41.26518 1.0240199134 11 43.1521 41.26518 1.8869199134 12 43.5998 41.26518 2.3346199134 13 43.1160 41.26518 1.8508199134 14 42.4185 41.26518 1.1533199134 15 42.3687 41.26518 1.1035199134 16 42.2975 41.26518 1.0323199134 17 42.8528 41.26518 1.5876199134 18 43.5350 41.26518 2.2698199134 19 44.7265 41.26518 3.4613199134 20 45.7293 41.26518 4.4641199134 21 45.7585 41.26518 4.4933199134 22 46.1685 41.26518 4.9033199134 23 46.5075 41.26518 5.2423199134 24 46.5270 41.26518 5.2618199134 25 46.6010 41.26518 5.3358199134 26 46.4607 41.26518 5.1955199134 27 46.7135 41.26518 5.4483199134 28 46.4113 41.26518 5.1461199134 29 45.5500 41.26518 4.2848199134 30 44.6081 41.26518 3.3429199134 31 44.4395 41.26518 3.1743199134 32 44.9847 41.26518 3.7195199134 33 45.7558 41.26518 4.4906199134 34 45.3942 41.26518 4.1290199134 35 45.6970 41.26518 4.4318199134 36 45.5664 41.26518 4.3012199134 37 46.0205 41.26518 4.7553199134 38 45.9195 41.26518 4.6543199134 39 45.8005 41.26518 4.5353199134 40 45.5350 41.26518 4.2698199134 41 45.4977 41.26518 4.2325199134 42 45.5782 41.26518 4.3130199134 43 45.7697 41.26518 4.5045199134 44 45.2445 41.26518 3.9793199134 45 45.0615 41.26518 3.7963199134 46 45.2865 41.26518 4.0213199134 47 44.7910 41.26518 3.5258199134 48 44.7625 41.26518 3.4973199134 49 44.7644 41.26518 3.4992199134 50 44.9973 41.26518 3.7321199134 51 44.7265 42.54977 2.1767250000 52 45.1465 44.13925 1.0072544944 53 44.7465 44.13925 0.6072544944 54 45.1795 44.13925 1.0402544944 55 45.6515 44.13925 1.5122544944 56 45.4920 44.13925 1.3527544944 57 45.2775 44.13925 1.1382544944 58 45.2115 44.13925 1.0722544944 59 45.4110 44.13925 1.2717544944 60 45.4005 44.13925 1.2612544944 61 44.7692 44.13925 0.6299544944 62 44.8913 44.13925 0.7520544944 63 45.0320 44.13925 0.8927544944 64 44.8790 44.13925 0.7397544944 65 44.8330 44.13925 0.6937544944 66 44.8257 44.13925 0.6864544944 67 44.7815 44.13925 0.6422544944 68 44.4790 44.13925 0.3397544944 69 44.6317 44.13925 0.4924544944 70 44.5043 44.13925 0.3650544944 71 44.3217 44.13925 0.1824544944 72 44.1005 44.13925 -0.0387455056 73 44.0470 44.13925 -0.0922455056 74 43.6835 44.13925 -0.4557455056 75 43.7864 44.13925 -0.3528455056 76 44.1807 42.54977 1.6309250000 77 43.9595 42.54977 1.4097250000 78 43.9370 44.13925 -0.2022455056 79 43.9910 44.13925 -0.1482455056 80 43.8650 42.54977 1.3152250000 81 43.6710 41.26518 2.4058199134 82 43.9300 44.13925 -0.2092455056 83 43.8630 44.13925 -0.2762455056 84 43.7095 44.13925 -0.4297455056 85 43.9435 44.13925 -0.1957455056 86 43.7360 44.13925 -0.4032455056 87 43.6295 44.13925 -0.5097455056 88 43.5980 44.13925 -0.5412455056 89 43.8726 44.13925 -0.2666455056 90 43.8935 44.13925 -0.2457455056 91 43.5957 44.13925 -0.5435455056 92 43.7155 44.13925 -0.4237455056 93 43.5280 44.13925 -0.6112455056 94 43.3415 44.13925 -0.7977455056 95 43.3374 44.13925 -0.8018455056 96 43.3320 44.13925 -0.8072455056 97 43.3869 44.13925 -0.7523455056 98 43.5016 44.13925 -0.6376455056 99 43.4875 44.13925 -0.6517455056 100 43.6023 44.13925 -0.5369455056 101 43.3886 44.13925 -0.7506455056 102 43.3105 44.13925 -0.8287455056 103 43.4455 44.13925 -0.6937455056 104 43.5185 44.13925 -0.6207455056 105 43.5755 44.13925 -0.5637455056 106 43.6217 44.13925 -0.5175455056 107 43.6440 44.13925 -0.4952455056 108 43.5789 44.13925 -0.5603455056 109 43.5215 44.13925 -0.6177455056 110 43.5033 44.13925 -0.6359455056 111 43.6320 44.13925 -0.5072455056 112 43.2630 44.13925 -0.8762455056 113 43.3717 44.13925 -0.7675455056 114 43.2745 44.13925 -0.8647455056 115 43.2647 44.13925 -0.8745455056 116 43.3240 44.13925 -0.8152455056 117 43.4455 44.13925 -0.6937455056 118 43.4098 44.13925 -0.7294455056 119 43.4100 44.13925 -0.7292455056 120 43.9300 44.13925 -0.2092455056 121 43.8104 44.13925 -0.3288455056 122 43.5400 44.13925 -0.5992455056 123 43.8580 44.13925 -0.2812455056 124 43.8375 44.13925 -0.3017455056 125 43.8810 44.13925 -0.2582455056 126 43.8870 44.13925 -0.2522455056 127 43.8009 44.13925 -0.3383455056 128 43.7877 44.13925 -0.3515455056 129 43.8110 44.13925 -0.3282455056 130 44.0625 44.13925 -0.0767455056 131 44.1250 44.13925 -0.0142455056 132 44.5200 44.13925 0.3807544944 133 45.4005 44.13925 1.2612544944 134 45.8900 44.13925 1.7507544944 135 45.1890 44.13925 1.0497544944 136 44.9035 44.13925 0.7642544944 137 44.9351 44.13925 0.7958544944 138 44.8010 44.13925 0.6617544944 139 43.9800 44.13925 -0.1592455056 140 44.1100 44.13925 -0.0292455056 141 44.2661 44.13925 0.1268544944 142 44.3610 44.13925 0.2217544944 143 44.0990 44.13925 -0.0402455056 144 43.8435 44.13925 -0.2957455056 145 43.8914 44.13925 -0.2478455056 146 44.2170 44.13925 0.0777544944 147 44.5060 44.13925 0.3667544944 148 44.5400 44.13925 0.4007544944 149 44.4465 44.13925 0.3072544944 150 44.8420 44.13925 0.7027544944 151 44.8946 44.13925 0.7553544944 152 44.9510 44.13925 0.8117544944 153 45.4450 44.13925 1.3057544944 154 45.0035 44.13925 0.8642544944 155 45.7690 44.13925 1.6297544944 156 46.0900 44.13925 1.9507544944 157 45.4120 44.13925 1.2727544944 158 45.1200 44.13925 0.9807544944 159 45.4800 44.13925 1.3407544944 160 45.1050 44.13925 0.9657544944 161 45.0560 44.13925 0.9167544944 162 45.2200 44.13925 1.0807544944 163 45.3900 44.13925 1.2507544944 164 45.0410 44.13925 0.9017544944 165 44.9399 44.13925 0.8006544944 166 44.9315 44.13925 0.7922544944 167 45.1935 44.13925 1.0542544944 168 45.3466 44.13925 1.2073544944 169 45.4645 44.13925 1.3252544944 170 45.5685 44.13925 1.4292544944 171 45.3921 44.13925 1.2528544944 172 45.3400 44.13925 1.2007544944 173 45.1308 44.13925 0.9915544944 174 45.1005 44.13925 0.9612544944 175 45.3700 44.13925 1.2307544944 176 45.2000 44.13925 1.0607544944 177 44.9614 44.13925 0.8221544944 178 44.8015 44.13925 0.6622544944 179 44.9152 44.13925 0.7759544944 180 45.0950 44.13925 0.9557544944 181 44.9271 44.13925 0.7878544944 182 44.6026 44.13925 0.4633544944 183 44.5000 44.13925 0.3607544944 184 44.5400 44.13925 0.4007544944 185 44.5532 44.13925 0.4139544944 186 44.4070 44.13925 0.2677544944 187 44.2590 44.13925 0.1197544944 188 44.1365 44.13925 -0.0027455056 189 44.1120 44.13925 -0.0272455056 190 43.8814 44.13925 -0.2578455056 191 43.9800 44.13925 -0.1592455056 192 43.7294 44.13925 -0.4098455056 193 43.9119 44.13925 -0.2273455056 194 43.9550 44.13925 -0.1842455056 195 43.9000 44.13925 -0.2392455056 196 43.7065 44.13925 -0.4327455056 197 43.6939 44.13925 -0.4453455056 198 43.6587 44.13925 -0.4805455056 199 43.5885 44.13925 -0.5507455056 200 43.8885 43.44041 0.4480864865 201 43.8216 43.44041 0.3811864865 202 43.7510 43.44041 0.3105864865 203 43.6990 43.44041 0.2585864865 204 43.7425 43.44041 0.3020864865 205 43.6390 43.44041 0.1985864865 206 43.5890 43.44041 0.1485864865 207 43.6060 43.44041 0.1655864865 208 43.5325 43.44041 0.0920864865 209 43.3850 43.44041 -0.0554135135 210 43.3745 43.44041 -0.0659135135 211 43.2360 43.44041 -0.2044135135 212 43.1957 43.44041 -0.2447135135 213 43.0100 43.44041 -0.4304135135 214 43.1401 44.13925 -0.9991455056 215 43.0487 44.13925 -1.0905455056 216 43.1972 44.13925 -0.9420455056 217 43.2461 43.44041 -0.1943135135 218 43.0866 43.44041 -0.3538135135 219 43.0865 43.44041 -0.3539135135 220 43.0194 43.44041 -0.4210135135 221 43.0800 43.44041 -0.3604135135 222 43.0070 43.44041 -0.4334135135 223 42.9278 43.44041 -0.5126135135 224 42.9545 43.44041 -0.4859135135 225 42.7995 43.44041 -0.6409135135 226 42.9048 43.44041 -0.5356135135 227 42.9468 43.44041 -0.4936135135 228 43.0800 43.44041 -0.3604135135 229 43.1274 43.44041 -0.3130135135 230 43.1625 43.44041 -0.2779135135 231 43.4500 43.44041 0.0095864865 232 43.8310 43.44041 0.3905864865 233 43.7769 43.44041 0.3364864865 234 43.9800 43.44041 0.5395864865 235 43.9200 43.44041 0.4795864865 236 44.1100 43.44041 0.6695864865 237 44.0300 43.44041 0.5895864865 238 44.1582 43.44041 0.7177864865 239 44.1400 43.44041 0.6995864865 240 45.0700 44.13925 0.9307544944 241 44.8737 44.13925 0.7344544944 242 44.8505 44.13925 0.7112544944 243 44.3730 44.13925 0.2337544944 244 44.0750 44.13925 -0.0642455056 245 43.9725 44.13925 -0.1667455056 246 44.0940 44.13925 -0.0452455056 247 44.1910 44.13925 0.0517544944 248 43.9685 44.13925 -0.1707455056 249 43.7900 44.13925 -0.3492455056 250 43.6041 44.13925 -0.5351455056 251 43.1707 44.13925 -0.9685455056 252 42.7100 44.13925 -1.4292455056 253 42.7550 44.13925 -1.3842455056 254 43.3316 44.13925 -0.8076455056 255 43.5000 44.13925 -0.6392455056 256 43.1540 44.13925 -0.9852455056 257 43.1600 44.13925 -0.9792455056 258 43.1000 44.13925 -1.0392455056 259 42.8500 44.13925 -1.2892455056 260 42.6175 44.13925 -1.5217455056 261 42.5000 44.13925 -1.6392455056 262 42.6285 44.13925 -1.5107455056 263 42.6974 44.13925 -1.4418455056 264 43.0400 44.13925 -1.0992455056 265 42.6730 44.13925 -1.4662455056 266 42.5015 44.13925 -1.6377455056 267 42.5380 44.13925 -1.6012455056 268 42.3735 44.13925 -1.7657455056 269 42.0140 44.13925 -2.1252455056 270 41.8618 44.13925 -2.2774455056 271 42.1824 42.54977 -0.3673750000 272 42.6050 42.54977 0.0552250000 273 42.7345 41.26518 1.4693199134 274 42.6150 41.26518 1.3498199134 275 42.4650 41.26518 1.1998199134 276 42.3400 41.26518 1.0748199134 277 42.2510 41.26518 0.9858199134 278 42.0475 41.26518 0.7823199134 279 41.8600 41.26518 0.5948199134 280 41.6850 41.26518 0.4198199134 281 41.7350 41.26518 0.4698199134 282 41.7060 41.26518 0.4408199134 283 41.7640 41.26518 0.4988199134 284 41.5800 41.26518 0.3148199134 285 41.3730 41.26518 0.1078199134 286 41.0880 41.26518 -0.1771800866 287 41.1370 41.26518 -0.1281800866 288 41.1587 41.26518 -0.1064800866 289 41.1850 41.26518 -0.0801800866 290 40.8190 41.26518 -0.4461800866 291 40.6330 41.26518 -0.6321800866 292 40.8580 41.26518 -0.4071800866 293 40.7940 41.26518 -0.4711800866 294 40.6900 41.26518 -0.5751800866 295 40.5950 41.26518 -0.6701800866 296 40.7305 41.26518 -0.5346800866 297 40.5471 41.26518 -0.7180800866 298 40.5145 41.26518 -0.7506800866 299 40.7000 41.26518 -0.5651800866 300 40.7000 41.26518 -0.5651800866 301 40.5220 41.26518 -0.7431800866 302 40.6165 41.26518 -0.6486800866 303 40.3985 41.26518 -0.8666800866 304 40.2815 41.26518 -0.9836800866 305 40.2450 41.26518 -1.0201800866 306 40.3055 41.26518 -0.9596800866 307 40.2696 41.26518 -0.9955800866 308 40.2510 41.26518 -1.0141800866 309 40.1270 41.26518 -1.1381800866 310 39.9500 41.26518 -1.3151800866 311 39.6750 41.26518 -1.5901800866 312 39.9540 41.26518 -1.3111800866 313 39.8828 41.26518 -1.3823800866 314 39.6200 41.26518 -1.6451800866 315 39.5415 41.26518 -1.7236800866 316 39.5250 41.26518 -1.7401800866 317 39.8145 41.26518 -1.4506800866 318 39.6675 41.26518 -1.5976800866 319 39.6950 41.26518 -1.5701800866 320 39.5985 41.26518 -1.6666800866 321 39.2735 41.26518 -1.9916800866 322 39.1435 41.26518 -2.1216800866 323 39.1742 41.26518 -2.0909800866 324 39.2025 41.26518 -2.0626800866 325 39.3946 42.54977 -3.1551750000 326 39.5025 41.26518 -1.7626800866 327 39.4845 42.54977 -3.0652750000 328 39.3300 41.26518 -1.9351800866 329 39.2950 41.26518 -1.9701800866 330 39.2675 41.26518 -1.9976800866 331 39.2535 41.26518 -2.0116800866 332 38.9845 41.26518 -2.2806800866 333 38.9285 41.26518 -2.3366800866 334 38.8592 41.26518 -2.4059800866 335 38.7700 41.26518 -2.4951800866 336 38.7900 41.26518 -2.4751800866 337 38.8205 41.26518 -2.4446800866 338 38.7577 41.26518 -2.5074800866 339 38.8390 41.26518 -2.4261800866 340 38.7800 41.26518 -2.4851800866 341 38.5400 41.26518 -2.7251800866 342 38.5110 41.26518 -2.7541800866 343 38.6150 41.26518 -2.6501800866 344 38.8980 41.26518 -2.3671800866 345 38.8691 41.26518 -2.3960800866 346 38.3840 41.26518 -2.8811800866 347 38.0277 41.26518 -3.2374800866 348 37.7200 41.26518 -3.5451800866 349 37.7325 38.31198 -0.5794833333 350 37.6260 38.31198 -0.6859833333 351 37.6030 38.31198 -0.7089833333 352 37.7800 38.09144 -0.3114421053 353 38.5590 38.31198 0.2470166667 354 39.0459 38.31198 0.7339166667 355 38.4500 38.31198 0.1380166667 356 38.5050 38.09144 0.4135578947 357 38.2885 38.31198 -0.0234833333 358 37.7950 38.09144 -0.2964421053 359 37.9200 38.31198 -0.3919833333 360 38.0340 38.31198 -0.2779833333 361 38.0290 38.09144 -0.0624421053 362 38.0630 38.09144 -0.0284421053 363 37.9828 38.09144 -0.1086421053 364 37.7450 38.09144 -0.3464421053 365 37.9690 38.09144 -0.1224421053 366 38.0070 38.09144 -0.0844421053 367 38.0615 38.09144 -0.0299421053 368 38.0912 38.09144 -0.0002421053 369 38.0910 38.09144 -0.0004421053 370 38.4310 38.09144 0.3395578947 371 38.4800 38.09144 0.3885578947 372 38.3500 38.31198 0.0380166667 373 38.2140 38.31198 -0.0979833333 374 38.3840 38.31198 0.0720166667 375 38.1375 38.31198 -0.1744833333 376 38.0075 38.09144 -0.0839421053 377 38.0524 38.09144 -0.0390421053 378 38.2350 38.09144 0.1435578947 379 38.3100 38.31198 -0.0019833333 380 38.2615 38.31198 -0.0504833333 381 38.1300 38.09144 0.0385578947 382 38.2820 38.09144 0.1905578947 383 38.5810 38.31198 0.2690166667 384 39.0801 38.31198 0.7681166667 385 39.0387 38.31198 0.7267166667 386 39.1015 41.26518 -2.1636800866 387 39.1503 41.26518 -2.1148800866 388 39.1400 41.26518 -2.1251800866 389 39.0275 41.26518 -2.2376800866 390 38.7665 41.26518 -2.4986800866 391 38.6910 41.26518 -2.5741800866 392 38.8490 41.26518 -2.4161800866 393 39.1644 41.26518 -2.1007800866 394 39.4907 41.26518 -1.7744800866 395 39.5095 41.26518 -1.7556800866 396 39.2795 41.26518 -1.9856800866 397 39.0437 41.26518 -2.2214800866 398 39.1355 41.26518 -2.1296800866 399 39.1430 41.26518 -2.1221800866 400 39.1850 41.26518 -2.0801800866 401 39.3550 41.26518 -1.9101800866 402 39.2970 41.26518 -1.9681800866 403 39.4514 41.26518 -1.8137800866 404 39.4173 41.26518 -1.8478800866 405 39.4305 41.26518 -1.8346800866 406 39.3840 41.26518 -1.8811800866 407 39.3261 41.26518 -1.9390800866 408 39.3010 41.26518 -1.9641800866 409 39.3500 41.26518 -1.9151800866 410 39.6400 41.26518 -1.6251800866 411 39.4723 41.26518 -1.7928800866 412 39.3685 41.26518 -1.8966800866 413 39.1906 41.26518 -2.0745800866 414 39.1183 41.26518 -2.1468800866 415 39.1325 41.26518 -2.1326800866 416 39.1144 41.26518 -2.1507800866 417 39.1614 41.26518 -2.1037800866 418 39.0908 41.26518 -2.1743800866 419 38.9199 41.26518 -2.3452800866 420 38.9130 41.26518 -2.3521800866 421 38.9655 41.26518 -2.2996800866 422 39.0290 41.26518 -2.2361800866 423 39.0890 41.26518 -2.1761800866 424 39.0700 41.26518 -2.1951800866 425 39.0046 41.26518 -2.2605800866 426 39.1038 41.26518 -2.1613800866 427 39.3572 41.26518 -1.9079800866 428 39.3880 41.26518 -1.8771800866 429 39.3820 41.26518 -1.8831800866 430 39.4398 41.26518 -1.8253800866 431 39.2537 41.26518 -2.0114800866 432 39.2301 41.26518 -2.0350800866 433 39.2763 41.26518 -1.9888800866 434 39.2820 41.26518 -1.9831800866 435 39.3325 41.26518 -1.9326800866 436 39.5570 41.26518 -1.7081800866 437 40.1000 41.26518 -1.1651800866 438 40.5875 41.26518 -0.6776800866 439 40.4850 41.26518 -0.7801800866 440 40.5500 41.26518 -0.7151800866 441 40.7955 41.26518 -0.4696800866 442 41.4560 41.26518 0.1908199134 443 41.3557 41.26518 0.0905199134 444 41.3740 41.26518 0.1088199134 445 41.2235 41.26518 -0.0416800866 446 41.1500 41.26518 -0.1151800866 447 41.3725 41.26518 0.1073199134 448 41.6923 41.26518 0.4271199134 449 41.8000 41.26518 0.5348199134 450 41.8045 41.26518 0.5393199134 451 41.6400 41.26518 0.3748199134 452 41.3600 41.26518 0.0948199134 453 41.5745 41.26518 0.3093199134 454 41.5930 41.26518 0.3278199134 455 41.5750 41.26518 0.3098199134 456 41.6800 41.26518 0.4148199134 457 42.0055 41.26518 0.7403199134 458 42.3188 41.26518 1.0536199134 459 42.5650 41.26518 1.2998199134 460 42.3575 41.26518 1.0923199134 461 42.2900 41.26518 1.0248199134 462 42.6950 41.26518 1.4298199134 463 43.0028 41.26518 1.7376199134 464 42.4507 41.26518 1.1855199134 465 42.4705 41.26518 1.2053199134 466 42.2875 41.26518 1.0223199134 467 42.3172 41.26518 1.0520199134 468 42.5500 41.26518 1.2848199134 469 42.7523 41.26518 1.4871199134 470 42.8993 41.26518 1.6341199134 471 43.1555 41.26518 1.8903199134 472 43.1885 41.26518 1.9233199134 473 43.4300 41.26518 2.1648199134 474 43.3100 41.26518 2.0448199134 475 42.8150 41.26518 1.5498199134 476 42.7017 41.26518 1.4365199134 477 42.2800 41.26518 1.0148199134 478 41.9220 41.26518 0.6568199134 479 42.1700 41.26518 0.9048199134 480 42.1962 41.26518 0.9310199134 481 42.3215 41.26518 1.0563199134 482 42.3173 41.26518 1.0521199134 483 42.3910 41.26518 1.1258199134 484 42.4630 41.26518 1.1978199134 485 42.4125 41.26518 1.1473199134 486 42.3040 41.26518 1.0388199134 487 41.8130 41.26518 0.5478199134 488 41.6510 41.26518 0.3858199134 489 41.5390 41.26518 0.2738199134 490 41.1575 41.26518 -0.1076800866 491 40.9545 41.26518 -0.3106800866 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/html/freestat/rcomp/tmp/46qt41292970753.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/5a8ss1292970753.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/6drqg1292970753.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/freestat/rcomp/tmp/7oip11292970753.tab") + } > > try(system("convert tmp/2l7dh1292970753.ps tmp/2l7dh1292970753.png",intern=TRUE)) character(0) > try(system("convert tmp/3ezu21292970753.ps tmp/3ezu21292970753.png",intern=TRUE)) character(0) > try(system("convert tmp/46qt41292970753.ps tmp/46qt41292970753.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.074 0.843 11.272