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4530 5258 4608 5060 4608 c +2980 4608 l +2980 5269 l +2980 5811 3475 6192 4201 6192 c +4828 6192 5620 6061 5719 6061 c +6624 fwd_x +end_ol + } def +/pAA { start_ol +1584 4608 m +538 4608 l +340 4608 241 4530 241 4387 c +241 4233 340 4156 538 4156 c +1132 4156 l +1132 -1584 l +538 -1584 l +340 -1584 241 -1661 241 -1804 c +241 -1958 340 -2035 538 -2035 c +2661 -2035 l +2859 -2035 2958 -1958 2958 -1804 c +2958 -1661 2859 -1584 2661 -1584 c +1584 -1584 l +1584 1181 l +2089 454 2705 124 3552 124 c +4906 124 5951 1137 5951 2448 c +5951 3750 4916 4752 3552 4752 c +2716 4752 2166 4453 1584 3695 c +1584 4608 l +3541 4300 m +4653 4300 5500 3487 5500 2421 c +5500 1399 4619 576 3541 576 c +2452 576 1584 1399 1584 2443 c +1584 3476 2452 4300 3541 4300 c +6624 fwd_x +end_ol + } def +/qAA { start_ol +2045 4608 m +2045 5898 l +2045 6104 1980 6192 1825 6192 c +1671 6192 1594 6094 1594 5898 c +1594 4608 l +780 4608 l +571 4608 472 4530 472 4387 c +472 4233 571 4156 780 4156 c +1594 4156 l +1594 1206 l 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c +5070 0 l +6115 0 l +6313 0 6412 76 6412 230 c +6412 373 6313 450 6115 450 c +5521 450 l +5521 6642 l +3101 4300 m +4213 4300 5070 3417 5070 2270 c +5070 1192 4190 306 3112 306 c +2012 306 1143 1192 1143 2308 c +1143 3414 2012 4300 3101 4300 c +6624 fwd_x +end_ol + } def +/xAA { start_ol +2277 1605 m +1506 -1144 l +1495 -1188 1485 -1221 1485 -1265 c +1485 -1441 1627 -1584 1803 -1584 c +1946 -1584 2034 -1518 2144 -1309 c +3739 1605 l +2277 1605 l +6624 fwd_x +end_ol + } def +/yAA { start_ol +3520 4608 m +1770 4608 l +1572 4608 1473 4530 1473 4387 c +1473 4233 1572 4156 1770 4156 c +3069 4156 l +3069 450 l +1308 450 l +1110 450 1011 373 1011 230 c +1011 76 1110 0 1308 0 c +5279 0 l +5477 0 5587 76 5587 230 c +5587 373 5488 450 5279 450 c +3520 450 l +3520 4608 l +3497 6903 m +2848 6903 l +2848 5760 l +3497 5760 l +3497 6903 l +6624 fwd_x +end_ol + } def +/zAA { start_ol +3508 4300 m +4333 4300 4828 3793 4828 3174 c +4828 450 l +4455 450 l +4257 450 4158 373 4158 230 c +4158 76 4257 0 4455 0 c +5654 0 l +5852 0 5951 76 5951 230 c +5951 373 5852 450 5654 450 c +5279 450 l +5279 3252 l +5279 4101 4531 4752 3552 4752 c +2871 4752 2475 4542 1836 3837 c +1836 4608 l +1011 4608 l +813 4608 714 4530 714 4387 c +714 4233 813 4156 1011 4156 c +1386 4156 l +1386 450 l +891 450 l +681 450 582 373 582 230 c +582 76 681 0 891 0 c +2332 0 l +2530 0 2629 76 2629 230 c +2629 373 2530 450 2332 450 c +1836 450 l +1836 3249 l +2079 3558 2530 4309 3508 4309 c +3508 4300 l +6624 fwd_x +end_ol + } def +/ABA { start_ol +4851 0 m +5763 4156 l +5972 4156 l +6170 4156 6269 4233 6269 4387 c +6269 4530 6170 4608 5972 4608 c +4752 4608 l +4554 4608 4455 4530 4455 4387 c +4455 4233 4554 4156 4752 4156 c +5302 4156 l +4542 618 l +3564 3438 l +3002 3438 l +2056 618 l +1264 4156 l +1847 4156 l +2045 4156 2144 4233 2144 4387 c +2144 4530 2045 4608 1847 4608 c +626 4608 l +428 4608 329 4530 329 4387 c +329 4233 428 4156 626 4156 c +835 4156 l +1759 0 l +2320 0 l +3299 2863 l +4300 0 l +4851 0 l +6624 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1605 450 c +1231 450 l +1231 3396 l +1649 4024 2001 4300 2386 4300 c +2760 4300 3101 3913 3101 3484 c +3101 0 l +3916 0 l +4114 0 4213 76 4213 230 c +4213 373 4114 450 3916 450 c +3541 450 l +3541 3396 l +3960 4035 4289 4300 4685 4300 c +5070 4300 5411 3936 5411 3516 c +5411 0 l +6225 0 l +6423 0 6522 76 6522 230 c +6522 373 6423 450 6225 450 c +5852 450 l +5852 3561 l +5852 4212 5334 4752 4696 4752 c +4245 4752 3882 4520 3453 3969 c +3211 4498 2859 4752 2386 4752 c +1924 4752 1605 4552 1231 4024 c +1231 4608 l +6624 fwd_x +end_ol + } def +/FBA { start_ol +1584 6642 m +538 6642 l +340 6642 241 6565 241 6422 c +241 6268 340 6192 538 6192 c +1132 6192 l +1132 450 l +538 450 l +340 450 241 373 241 230 c +241 76 340 0 538 0 c +1584 0 l +1584 968 l +2100 229 2760 -144 3564 -144 c +4883 -144 5951 963 5951 2331 c +5951 3687 4906 4752 3564 4752 c +2749 4752 2133 4399 1584 3622 c +1584 6642 l +3541 4300 m +4641 4300 5500 3414 5500 2275 c +5500 1203 4609 306 3541 306 c +2452 306 1584 1203 1584 2308 c +1584 3414 2452 4300 3541 4300 c +6624 fwd_x +end_ol + } def +/GBA { start_ol +5060 5740 m +6115 5740 l +6313 5740 6412 5817 6412 5971 c +6412 6114 6313 6192 6115 6192 c +2859 6192 l +2661 6192 2562 6114 2562 5971 c +2562 5817 2661 5740 2859 5740 c +4609 5740 l +4609 1837 l +4609 984 3893 306 3002 306 c +2452 306 1935 546 1374 1049 c +1374 2471 l +1374 2668 1297 2766 1154 2766 c +1000 2766 923 2668 923 2471 c +923 842 l +1715 130 2287 -144 2991 -144 c +4124 -144 5060 754 5060 1837 c +5060 5740 l +6624 fwd_x +end_ol + } def +/HBA { start_ol +3530 450 m +3530 5740 l +5357 5740 l +5357 4937 l +5357 4761 5456 4640 5587 4640 c +5742 4640 5807 4728 5807 4937 c +5807 6192 l +792 6192 l +792 4937 l +792 4739 868 4640 1011 4640 c +1165 4640 1242 4739 1242 4937 c +1242 5740 l +3079 5740 l +3079 450 l +1924 450 l +1726 450 1627 373 1627 230 c +1627 76 1726 0 1924 0 c +4685 0 l +4883 0 4982 76 4982 230 c +4982 373 4883 450 4685 450 c +3530 450 l +6624 fwd_x +end_ol + } def +/IBA { start_ol 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76 4289 0 4487 0 c +6203 0 l +6401 0 6500 76 6500 230 c +6500 373 6401 450 6203 450 c +5797 450 l +3629 6192 l +1386 6192 l +1188 6192 1089 6114 1089 5971 c +1089 5817 1188 5740 1386 5740 c +2705 5740 l +736 450 l +396 450 l +198 450 99 373 99 230 c +99 76 198 0 396 0 c +2056 0 l +2254 0 2353 76 2353 230 c +2353 373 2254 450 2056 450 c +1188 450 l +1782 2068 l +4708 2068 l +4542 2520 m +1957 2520 l +3145 5740 l +3310 5740 l +4542 2520 l +6624 fwd_x +end_ol + } def +/OBA { start_ol +2034 1987 m +2540 2419 l +4520 450 l +4213 450 l +4015 450 3916 373 3916 230 c +3916 76 4015 0 4213 0 c +5654 0 l +5852 0 5951 76 5951 230 c +5951 373 5852 450 5654 450 c +5159 450 l +2881 2706 l +4609 4156 l +5125 4156 l +5323 4156 5422 4233 5422 4387 c +5422 4530 5323 4608 5125 4608 c +3695 4608 l +3497 4608 3398 4530 3398 4387 c +3398 4233 3497 4156 3695 4156 c +3937 4156 l +2034 2530 l +2034 6642 l +990 6642 l +792 6642 693 6565 693 6422 c +693 6268 792 6192 990 6192 c +1584 6192 l +1584 450 l +990 450 l 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450 6225 450 c +5852 450 l +5852 5740 l +6126 5740 l +6324 5740 6423 5817 6423 5971 c +6423 6114 6324 6192 6126 6192 c +5026 6192 l +3332 2363 l +1605 6192 l +516 6192 l +318 6192 219 6114 219 5971 c +219 5817 318 5740 516 5740 c +792 5740 l +792 450 l +417 450 l +219 450 120 373 120 230 c +120 76 219 0 417 0 c +2056 0 l +2254 0 2364 76 2364 230 c +2364 373 2265 450 2056 450 c +1242 450 l +1242 5740 l +1330 5740 l +3079 1858 l +3585 1858 l +6624 fwd_x +end_ol + } def +/oBA { start_ol +4257 4156 m +4257 -294 l +4257 -1045 3718 -1584 2991 -1584 c +1584 -1584 l +1386 -1584 1287 -1661 1287 -1804 c +1287 -1958 1386 -2035 1584 -2035 c +3002 -2035 l +4003 -2035 4708 -1319 4708 -294 c +4708 4608 l +1605 4608 l +1407 4608 1308 4530 1308 4387 c +1308 4233 1407 4156 1605 4156 c +4257 4156 l +4223 6903 m +3574 6903 l +3574 5760 l +4223 5760 l +4223 6903 l +6624 fwd_x +end_ol + } def +/pBA { start_ol +3574 3166 m +5544 5740 l +5708 5740 l +5906 5740 6005 5817 6005 5971 c +6005 6125 5906 6192 5708 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def + +% override setpagedevice if it is not defined +/setpagedevice where { + pop % get rid of its dictionary + /setpagesize { + 3 dict begin + /pageheight exch def + /pagewidth exch def + /orientation 0 def + % Exchange pagewidth and pageheight so that pagewidth is bigger + pagewidth pageheight gt { + pagewidth + /pagewidth pageheight def + /pageheight exch def + /orientation 3 def + } if + 2 dict + dup /PageSize [pagewidth pageheight] put + dup /Orientation orientation put + setpagedevice + end + } def +} +{ + /setpagesize { pop pop } def +} ifelse +/duplex { + statusdict /setduplexmode known + { statusdict begin setduplexmode end } {pop} ifelse +} def +/tumble { + statusdict /settumble known + { statusdict begin settumble end } {pop} ifelse +} def +% Turn the page around +/turnpage { + 90 rotate + 0 pageheight neg translate +} def +% User settings +/pagewidth 595 def +/pageheight 841 def +pagewidth pageheight setpagesize +/column_width 523 def +/bodyheight 755 def +/lmarg 36 def +/ytop 791 def +/do_separation_line true def +/do_landscape false def +/do_tumble true def +/do_duplex true def +% Procedures to translate position to first and second column +/lw 20 def % whatever +/setnumcolumns { + /numcolumns exch def + /firstcolumn { /xpos lmarg def /ypos ytop def} def + /nextcolumn { + do_separation_line { + xpos column_width add gutter_width 2 div add % x start + ytop lw add moveto % y start + 0 bodyheight lw add neg rlineto 0 setlinewidth stroke + } if + /xpos xpos column_width add gutter_width add def + /ypos ytop def + } def +} def + +1 setnumcolumns +/showline { + /y exch def + /s exch def + xpos y moveto + column_width 0 rlineto stroke + xpos y moveto /Helvetica findfont 20 scalefont setfont s show +} def +/paps_bop { % Beginning of page definitions + papsdict begin + gsave + do_landscape {turnpage} if + % ps2pdf gets wrong orientation without this! + /Helvetica findfont setfont 100 100 moveto ( ) show + firstcolumn + end +} def + +/paps_eop { % End of page cleanups + grestore +} def +%%BeginProlog +/papsdict 1 dict def +papsdict begin + +/conicto { + /to_y exch def + /to_x exch def + /conic_cntrl_y exch def + /conic_cntrl_x exch def + currentpoint + /p0_y exch def + /p0_x exch def + /p1_x p0_x conic_cntrl_x p0_x sub 2 3 div mul add def + /p1_y p0_y conic_cntrl_y p0_y sub 2 3 div mul add def + /p2_x p1_x to_x p0_x sub 1 3 div mul add def + /p2_y p1_y to_y p0_y sub 1 3 div mul add def + p1_x p1_y p2_x p2_y to_x to_y curveto +} bind def +/start_ol { gsave } bind def +/end_ol { closepath fill grestore } bind def +/draw_char { fontdict begin gsave 0.001000 dup scale last_x last_y translate load exec end grestore} def +/goto_xy { fontdict begin /last_y exch def /last_x exch def end } def +/goto_x { fontdict begin /last_x exch def end } def +/fwd_x { fontdict begin /last_x exch last_x add def end } def +/c /curveto load def +/x /conicto load def +/l /lineto load def +/m /moveto load def +end +/paps_exec { + 1 dict begin + /ps exch def + /len ps length def + /pos 0 def + + % Loop over all the characters of the string + { + pos len eq {exit} if + + % Get character at pos + /ch ps pos 1 getinterval def + + % check for + + (+) ch eq { + /pos 1 pos add def + /xp ps pos 8 getinterval cvi def + /yp ps pos 8 add 8 getinterval cvi def + /pos 16 pos add def + papsdict begin xp yp goto_xy end + } { + (*) ch eq { + /pos 1 pos add def + /xp ps pos 8 getinterval cvi def + /pos 8 pos add def + papsdict begin xp goto_x end + } { (>) ch eq { + /pos 1 pos add def + /xp ps pos 4 getinterval cvi def + /pos 4 pos add def + papsdict begin xp 2 mul fwd_x end + } { (-) ch eq { + /pos 1 pos add def + /xp ps pos 4 getinterval cvi def + /pos 4 pos add def + papsdict begin xp neg 2 mul fwd_x end + } { + % Must be a 3 char sym. Load and exec + /name ps pos 3 getinterval def + papsdict begin name draw_char end + /pos 3 pos add def + } ifelse + } ifelse + } ifelse + } ifelse + } loop + end +} def +/fontdict 1 dict def +papsdict begin fontdict begin +/AAA { start_ol +4464 0 m +2736 0 l +2736 7272 l +504 7272 l +504 8784 l +6696 8784 l +6696 7272 l +4464 7272 l +4464 0 l +7200 fwd_x +end_ol + } def +/BAA { start_ol +6264 4213 m +6264 0 l +4536 0 l +4536 3952 l +4536 4653 4340 4954 x +4144 5256 3701 5256 x +3246 5256 2991 4847 x +2736 4439 2736 3714 x +2736 0 l +1008 0 l +1008 9144 l +2736 9144 l +2736 5526 l +2916 6084 3351 6390 x +3786 6696 4401 6696 x +5323 6696 5793 6071 x +6264 5448 6264 4213 x +7200 fwd_x +end_ol + } def +/CAA { start_ol +936 2336 m +936 6552 l +2664 6552 l +2664 2596 l +2664 1896 2857 1596 x +3052 1296 3504 1296 x +3955 1296 4209 1699 x +4464 2104 4464 2833 x +4464 6552 l +6192 6552 l +6192 0 l +4464 0 l +4464 1019 l +4284 464 3852 159 x +3420 -144 2810 -144 x +1882 -144 1408 481 x +936 1108 936 2336 x +7200 fwd_x +end_ol + } def +/EAA { start_ol +6552 7272 m +2808 7272 l +2808 5328 l +6192 5328 l +6192 3816 l +2808 3816 l +2808 0 l +1080 0 l +1080 8784 l +6552 8784 l +6552 7272 l +7200 fwd_x +end_ol + } def +/FAA { start_ol +6480 360 m +5883 108 5263 -18 x +4644 -144 3954 -144 x +2311 -144 1443 731 x +576 1606 576 3252 x +576 4846 1413 5771 x +2251 6696 3697 6696 x +5157 6696 5962 5823 x +6768 4950 6768 3364 x +6768 2664 l +2304 2664 l +2309 1938 2760 1580 x +3213 1224 4110 1224 x +4702 1224 5277 1396 x +5852 1569 6480 1944 x +6480 360 l +5040 3960 m +5028 4635 4695 4981 x +4363 5328 3717 5328 x +3133 5328 2786 4970 x +2440 4612 2376 3954 x +5040 3960 l +7200 fwd_x +end_ol + } def +/GAA { start_ol +4968 3276 m +4968 4203 4653 4729 x +4339 5256 3785 5256 x +3232 5256 2911 4729 x +2592 4203 2592 3276 x +2592 2349 2911 1822 x +3232 1296 3785 1296 x +4339 1296 4653 1822 x +4968 2349 4968 3276 x +2592 5598 m +2908 6147 3326 6421 x +3745 6696 4272 6696 x +5437 6696 6066 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def +/wAA { start_ol +5510 2860 m +5708 2860 5807 2937 5807 3091 c +5807 3234 5708 3312 5510 3312 c +1089 3312 l +891 3312 792 3234 792 3091 c +792 2937 891 2860 1089 2860 c +5510 2860 l +6624 fwd_x +end_ol + } def +/xAA { start_ol +3695 0 m +5521 4156 l +5972 4156 l +6170 4156 6269 4233 6269 4387 c +6269 4530 6170 4608 5972 4608 c +4300 4608 l +4102 4608 4003 4530 4003 4387 c +4003 4233 4102 4156 4300 4156 c +5049 4156 l +3431 450 l +3223 450 l +1572 4156 l +2287 4156 l +2496 4156 2595 4233 2595 4387 c +2595 4530 2496 4608 2287 4608 c +626 4608 l +428 4608 329 4530 329 4387 c +329 4233 428 4156 626 4156 c +1077 4156 l +2926 0 l +3695 0 l +6624 fwd_x +end_ol + } def +/yAA { start_ol +1374 4171 m +1374 4053 1473 3955 1584 3955 c +1902 3955 2376 4300 3255 4300 c +4059 4300 4609 3945 4609 3417 c +4609 2648 l +3960 2824 3619 2880 3112 2880 c +1737 2880 792 2229 792 1267 c +792 427 1485 -144 2507 -144 c +3277 -144 3937 141 4609 770 c +4609 0 l +5654 0 l +5852 0 5951 76 5951 230 c +5951 373 5852 450 5654 450 c +5060 450 l +5060 3417 l +5060 4181 4312 4752 3288 4752 c +2926 4752 1374 4531 1374 4171 c +4609 1248 m +3871 571 3277 306 2496 306 c +1737 306 1242 681 1242 1256 c +1242 1953 2034 2428 3200 2428 c +3684 2428 4213 2362 4609 2262 c +4609 1248 l +6624 fwd_x +end_ol + } def +/zAA { start_ol +4839 3685 m +4344 4421 3762 4752 2958 4752 c +1715 4752 693 3718 693 2443 c +693 1168 1715 124 2958 124 c +3750 124 4344 465 4839 1190 c +4839 -228 l +4839 -977 4245 -1584 3497 -1584 c +2221 -1584 l +2023 -1584 1924 -1661 1924 -1804 c +1924 -1958 2023 -2035 2221 -2035 c +3475 -2035 l +4498 -2035 5290 -1220 5290 -294 c +5290 4156 l +5884 4156 l +6082 4156 6181 4233 6181 4387 c +6181 4530 6082 4608 5884 4608 c +4839 4608 l +4839 3685 l +2991 4300 m +4036 4300 4839 3487 4839 2421 c +4839 1399 4015 576 2991 576 c +1968 576 1143 1399 1143 2443 c +1143 3476 1957 4300 2991 4300 c +6624 fwd_x +end_ol + } def +/ABA { start_ol +3530 450 m +3530 5740 l +5357 5740 l +5357 4937 l +5357 4761 5456 4640 5587 4640 c +5742 4640 5807 4728 5807 4937 c +5807 6192 l +792 6192 l +792 4937 l +792 4739 868 4640 1011 4640 c +1165 4640 1242 4739 1242 4937 c +1242 5740 l +3079 5740 l +3079 450 l +1924 450 l +1726 450 1627 373 1627 230 c +1627 76 1726 0 1924 0 c +4685 0 l +4883 0 4982 76 4982 230 c +4982 373 4883 450 4685 450 c +3530 450 l +6624 fwd_x +end_ol + } def +/BBA { start_ol +1374 5845 m +1374 5734 1473 5635 1594 5635 c +1913 5635 1957 6388 3299 6388 c +4146 6388 4795 5845 4795 5125 c +4795 4150 4025 3906 3069 3906 c +2881 3906 2772 3829 2772 3686 c +2772 3532 2871 3456 3069 3456 c +4135 3456 5037 2729 5037 1869 c +5037 1022 4201 306 3200 306 c +1968 306 1506 988 1264 988 c +1154 988 1055 889 1055 779 c +1055 405 2332 -144 3189 -144 c +4455 -144 5488 758 5488 1869 c +5488 2619 4971 3279 4114 3631 c +4554 3741 5247 4172 5247 5136 c +5247 6099 4411 6840 3310 6840 c +2079 6840 1374 6044 1374 5845 c +6624 fwd_x +end_ol + } def +/CBA { start_ol +4135 1852 m +4135 450 l +3299 450 l 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start_ol +1231 4608 m +417 4608 l +219 4608 120 4530 120 4387 c +120 4233 219 4156 417 4156 c +792 4156 l +792 450 l +417 450 l +219 450 120 373 120 230 c +120 76 219 0 406 0 c +1605 0 l +1792 0 1902 76 1902 230 c +1902 373 1803 450 1605 450 c +1231 450 l +1231 3396 l +1649 4024 2001 4300 2386 4300 c +2760 4300 3101 3913 3101 3484 c +3101 0 l +3916 0 l +4114 0 4213 76 4213 230 c +4213 373 4114 450 3916 450 c +3541 450 l +3541 3396 l +3960 4035 4289 4300 4685 4300 c +5070 4300 5411 3936 5411 3516 c +5411 0 l +6225 0 l +6423 0 6522 76 6522 230 c +6522 373 6423 450 6225 450 c +5852 450 l +5852 3561 l +5852 4212 5334 4752 4696 4752 c +4245 4752 3882 4520 3453 3969 c +3211 4498 2859 4752 2386 4752 c +1924 4752 1605 4552 1231 4024 c +1231 4608 l +6624 fwd_x +end_ol + } def +/IBA { start_ol +5719 6061 m +5852 6061 5951 6148 5951 6279 c +5951 6390 5884 6466 5753 6499 c +5401 6565 4641 6642 4223 6642 c +3233 6642 2530 6061 2530 5269 c +2530 4608 l +1561 4608 l +1363 4608 1264 4530 1264 4387 c 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6642 l +990 6642 l +792 6642 693 6565 693 6422 c +693 6268 792 6192 990 6192 c +1584 6192 l +1584 450 l +990 450 l +792 450 693 373 693 230 c +693 76 792 0 990 0 c +2034 0 l +2034 1987 l +6624 fwd_x +end_ol + } def +/NBA { start_ol +3520 5740 m +5049 5740 l +5258 5740 5357 5817 5357 5971 c +5357 6114 5258 6192 5049 6192 c +1539 6192 l +1341 6192 1242 6114 1242 5971 c +1242 5817 1341 5740 1539 5740 c +3069 5740 l +3069 450 l +1539 450 l +1341 450 1242 373 1242 230 c +1242 76 1341 0 1539 0 c +5049 0 l +5247 0 5357 76 5357 230 c +5357 373 5258 450 5049 450 c +3520 450 l +3520 5740 l +6624 fwd_x +end_ol + } def +/OBA { start_ol +4257 4156 m +4257 -294 l +4257 -1045 3718 -1584 2991 -1584 c +1584 -1584 l +1386 -1584 1287 -1661 1287 -1804 c +1287 -1958 1386 -2035 1584 -2035 c +3002 -2035 l +4003 -2035 4708 -1319 4708 -294 c +4708 4608 l +1605 4608 l +1407 4608 1308 4530 1308 4387 c +1308 4233 1407 4156 1605 4156 c +4257 4156 l +4223 6903 m +3574 6903 l +3574 5760 l +4223 5760 l +4223 6903 l 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21.0 75 1 "mercury monarch" +15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick" +16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina" +15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air" +16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury" +14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd" +17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century" +16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu" +15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador" +18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury" +21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk" +20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" +13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" +29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" +23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" +20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" +23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" +24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" +25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" +24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" +18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" +29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" +19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" +23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" +23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" +22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" +25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" +33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" +28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" +25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" +25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" +26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" +27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" +17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" +16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" +15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" +14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" +22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" +22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" +24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" +22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" +29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" +24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" +29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" +33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" +20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" +18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" +18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" +17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" +29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" +32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" +28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" +26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" +20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" +13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" +19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" +19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" +16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" +16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" +13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" +13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" +13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" +31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" +30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" +36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" +25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" +33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" +17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" +17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" +15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" +15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" +17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" +20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" +19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" +18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" +16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" +15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" +15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" +16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" +29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" +24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" +26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" +25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" +30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" +33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" +30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" +30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" +22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" +21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" +21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" +43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" +36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" +32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" +39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" +36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" +19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" +19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" +20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" +19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" +20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" +20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" +25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" +20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" +19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" +20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" +20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" +18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" +18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" +19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" +17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" +18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" +17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" +30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" +27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" +27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" +30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" +21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" +23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" +23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" +23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" +20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" +17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" +21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" +16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" +31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" +29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" +21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" +19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" +22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" +20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" +20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" +17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" +17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" +16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" +18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" +16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" +15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" +19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" +18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" +31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" +34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" +35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" +27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" +25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" +23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" +27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" +23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" +34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" +34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" +31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" +37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" +28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" +28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" +26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" +33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" +41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" +38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" +32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" +37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" +28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" +26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" +24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" +19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" +34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" +29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" +31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" +37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" +32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" +46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" +27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" +40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" +44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" +43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" +36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" +30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" +44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" +40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" +33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" +29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" +32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" +23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" +35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" +23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" +32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" +27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" +26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" +25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" +23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" +30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" +39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" +39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" +35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" +32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" +37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" +37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" +34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" +34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" +34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" +29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" +33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" +34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" +33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" +32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" +32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" +31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" +28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" +30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" +25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" +24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" +22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" +26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" +20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" +17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" +28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" +27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" +34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" +31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" +29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" +27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" +24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" +36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" +37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" +31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" +38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" +36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" +36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" +36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" +34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" +38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" +32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" +38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" +25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" +38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" +26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" +22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" +32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" +36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" +27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" +27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" +44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" +32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" +28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" +31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" diff --git a/hw2/fit_quality.pdf b/hw2/fit_quality.pdf new file mode 100755 index 0000000..8c14b11 Binary files /dev/null and b/hw2/fit_quality.pdf differ diff --git a/hw2/mpg_horsepower_regression.pdf b/hw2/mpg_horsepower_regression.pdf new file mode 100755 index 0000000..e1f0f42 Binary files /dev/null and b/hw2/mpg_horsepower_regression.pdf differ diff --git a/hw3/Rhistory b/hw3/Rhistory new file mode 100644 index 0000000..9c9a9b1 --- /dev/null +++ b/hw3/Rhistory @@ -0,0 +1,462 @@ +auto = read.table("auto.data",header=T,na.strings="?") +length(x=auto$mpg) +glm +glm.pred +help(rep) +glm.pred=rep(FALSE,397) +glm.pred +medium(auto$mpg) +median(auto$mpg) +glm.pred[auto$mpg>median(auto$mpg)]=T +glm.pred +contour(auto) +contour(glm.pred ~ auto$mpg) +contour(glm.pred,auto$mpg) +help(contour) +contour(auto$mpg,auto$horsepower,glm.pred) +glm.pred +length(glm.pred) +table(glm.pred,auto$mpg) +table(glm.pred,auto$mpg,auto$horsepower) +glm.pred=rep(0,397) +glm.pred[auto$mpg>median(auto$mpg)]=1 +glm.pred +auto$mpg01=rep(0,397) +auto$mpg01[auto$mpg>median(auto$mpg)]=1 +auto$mpg01 +auto$mpg01 +auto$mpg01 +plots(auto) +plot(auto) +boxplot(auto) +boxplot.matrix(auto) +help(boxplot) +boxplot(auto$mpg01,auto) +boxplot(auto$mpg,auto) +boxplot(auto$mpg) +boxplot(auto) +boxplot(mpg01 ~ auto) +boxplot(mpg01 ~) +boxplot(auto$mpg01 ~ auto) +attach(auto) +boxplot(mpg01) +boxplot(mpg01 ~ auto) +boxplot(mpg01 ~ auto,auto) +boxplot(mpg01 ~ auto,data = auto) +help(plot.table) +plot.table(auto) +help(plot.table) +plot(auto) +plot(auto,t="box") +help(plot.table) +help(plot.table,plot.frame=1) +help(plot.table) +help(plot.table,frame.plot=1) +help(plot.table) +help(plot.table,frame.plot=is.num) +help(plot.table) +plot(auto,t="box",frame.plot=1) +plot(auto,frame.plot=1) +plot(auto,frame.plot=1) +plot(auto,frame.plot=is.num) +plot(auto,frame.plot=0) +plot(auto,frame.plot="0") +plot(auto,frame.plot="1") +plot(auto,frame.plot=TRUE) +plot(auto,frame.plot=FALSE) +plot(auto,frame.plot=TRUE) +plot(auto,frame.plot=T) +plot(auto,frame.plot=1) +boxplot(mpg~mpg01,auto) +boxplot(mpg01 ~ mpg,auto) +boxplot(mpg01 ~ *,auto) +boxplot(mpg01 ~ ,auto) +boxplot(mpg01 ~ auto,auto) +boxplot(mpg01,auto) +boxplot(auto) +boxplot(auto,y=mpg01) +boxplot(auto,y=mpg) +boxplot(data = auto) +boxplot(auto) +help(for) +plot(auto,frame.plot=1) +plot(auto) +names(auto) +auto$name +help(sample) +x <- 1:12 +x +sample(x) +help(sample) +sample(x,replace=T) +sample(x,replace=T) +sample(x,replace=F) +c +x +sample(x,replace=T) +x +help(sample) +sample(x[x>9]) +sample(x[x>8]) +help(sample) +x <- 1:10 +sample(x[x>8]) +sample(x[x>]) +help(sample) +help(sample) +help(sample) +sample(auto,size=length(mpg01)/2) +x <- length(mpg01) +sample(x,size=length(mpg01)/2) +auto[sample(x,size=length(mpg01)/2)] +auto$mpg[sample(x,size=length(mpg01)/2)] +help(data.frame) +data.frame( +help(data.frame) +auto[sample(x,size=length(mpg01)/2)] +train = sample(x,size=length(mpg01)/2) +train = +auto[train] +auto$mpg[train] +auto$mpg[train,] +auto$mpg[train] +auto$mpg[23] +auto$mpg[228] +auto$mpg[391] +auto.test=auto[!train] +auto.train=auto[train] +auto.test +summary(auto.test) +train=(mpg<15) +train +train = (sample(x,size=length(mpg01)/2)) +train +head(auto) +auto[,train[ +auto[,train] +train +help(contains) +auto[1,train] +train +auto[[,train]] +auto[[1,train]] +autoi +head(auto) +head(auto[sample(nrow(auto),397/2)]) +head(auto[sample(nrow(auto),3)]) +data = data.frame(auto) +data +head(data[sample(nrow(data),3)]) +nrow(data) +head(data[sample(ncol(data),3)]) +head(data[sample(ncol(data),397/2)]) +head(data[sample(ncol(data),3)]) +head(data[sample(ncol(data),3)]) +head(data[sample(ncol(data),3)]) +head(data[sample(ncol(data),3)]) +head(data[,sample(ncol(data),3)]) +head(data[,sample(ncol(data),3)]) +head(data[,sample(ncol(data),3)]) +head(data[,sample(ncol(data),3)]) +head(data[,sample(ncol(data),3)]) +head(data[sample(ncol(data),3),]) +head(data[sample(ncol(data),3),]) +head(data[sample(ncol(data),3),]) +head(data[sample(nrow(data),3),]) +head(data[sample(nrow(data),397/2),]) +head(data[sample(nrow(data),397/2),]) +head(data[sample(nrow(data),397/2),]) +head(data[sample(nrow(data),397/2),]) +head(data[sample(nrow(data),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +head(auto[sample(nrow(auto),397/2),]) +train = auto[sample(nrow(auto),397/2),] +[sample(nrow(auto),397/2),] +sample(nrow(auto),397/2) +train sample(nrow(auto),397/2) +train = sample(nrow(auto),397/2) +autp[train,] +auto[train,] +train = sample(nrow(auto),397/2) +head(auto[train,]) +head(auto[!train,]) +traindata = auto[train,] +testdata = auto[!train,] +testdata +traindata +length(traindata) +length(traindata$mpg) +198*2 +summary(testdata) +testdata = auto[!train] +testdata +testdata = auto[!train,] +train +summary(train) +names(train) +head(traindata) +testdata = auto[!train,] +testdata +!train +train +?sample +sort(train) +train_vals = train +train = rep(false,397) +train = rep(F,397) +train +help for +?for +?for +help)for) +help(for) +help(for) +help lapply() +?lapply +sapply(train, +?sapply +sapply(train, +?sapply +train[train_vals]=T +train +traindata = auto[train,] +traindata +length(auto) +length(traindata) +length(traindata$mpg) +testdata=auto[!train,] +length(testdate$mpg) +length(testdata$mpg) +training_indices = sample(nrow(auto),397/2) +train_bools = rep(F,length(auto$mpg)) +train_bools[training_indices]=T +head(train_bools) +length(train_bools) +train_data = auto[train_bools,] +test_data = auto[!train_bools,] +summary(train_data) +summary(test_data) +lda.fit +library(MASS) +lda.fit +lda() +detach(auto) +mpg01 +mpg +attach(test_data) +mpg01 +names() +names(test_data) +ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data) +detach(test_data) +ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data) +lda.fit +lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data) +lda.fit +summary(lda.fit) +coefficients(lda.fit) +plot(lda.fit) +lda.pred=predict(lda.fit,test_data) +lda.pred=predict(lda.fit, !training_bools) +lda.pred=predict(lda.fit, !training_indices) +test_data +lda.pred=predict(lda.fit, test_data) +lda.pred +plot(lda.pred) +names(lda.pred) +lda.class=lda.pres$class +lda.class=lda.pred$class +table(lda.class,testdata) +table(lda.class,test_data) +length(lda.class) +length(test_data) +table(lda.class,test_data$mpg01) +mean(lda.class==test_data$mpg01) +sum(lda.pred$posterior[,1]>=.5) +sum(lda.pred$posterior[,1]<.5) +lda.pred$posterior[,1] +sum(lda.pred$posterior<.5) +lda.pred$posterior +lda.pred$posterior<5 +lda.pred$posterior<.5 +sum(lda.pred$posterior<.5) +sum(lda.pred$posterior<.5[,1]) +sum(lda.pred$posterior<.5[1]) +sum(lda.pred$posterior<.5[2]) +lda.pred$posterior<.5[2] +lda.pred$posterior<.5 +lda.pred$posterior +lda.pred$posterior[,1] +lda.pred$posterior[1,] +lda.pred$posterior[,2] +lda.pred$posterior[,1] +lda.pred$posterior[,1]>.5 +sum(lda.pred$posterior[,1]>.5) +sum.bool(lda.pred$posterior[,1]>.5) +?sum +sum.bool(lda.pred$posterior[,1]>.5,na.rm=T) +sum(lda.pred$posterior[,1]>.5,na.rm=T) +sum(lda.pred$posterior[,1]>.5) +sum(lda.pred$posterior[,1]>.5,na.rm=T) +sum(lda.pred$posterior[,1]>=.5,na.rm=T) +sum(lda.pred$posterior[,1]<.5,na.rm=T) +mean(lda.pred$[,1]==test_data,na.rm=T) +lda.pred +lda.pred$class +lda.pred$class==test_data$mpg01 +mean(lda.pred$class==test_data$mpg01,na.rm=T) +mean(lda.pred$class!=test_data$mpg01,na.rm=T) +lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data) +lda.fit +mean(lda.pred$class==test_data$mpg01,na.rm=T) +lda.pred=predict(lda.fit, test_data) +mean(lda.pred$class==test_data$mpg01,na.rm=T) +mean(lda.pred$class!=test_data$mpg01,na.rm=T) +train_data == test_data +train_data$mpg01 == test_data$mpg01 +lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data) +lda.pred=predict(lda.fit, test_data) +mean(lda.pred$class!=test_data$mpg01,na.rm=T) +lda.pred +lda.pred$posterior[,1] +summary(lda.fit) +lda.fit +lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data) +lda.fit +mean(lda.pred$class!=test_data$mpg01,na.rm=T) +lda.pred=predict(lda.fit, test_data) +mean(lda.pred$class!=test_data$mpg01,na.rm=T) +head(lda.pred) +lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data) +lda.pred=predict(lda.fit, test_data) +head(lda.pred) +mean(lda.pred$class!=test_data$mpg01,na.rm=T) +qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data) +qda.fit +qda.class=predict(qda.fit,test_data)$class +qda.class=predict(qda.fit,test_data,na.rm=T)$class +qda.class=predict(qda.fit,test_data)$class +qda.class +mean(qda.pred$class!=test_data$mpg01,na.rm=T) +qda.pred=predict(qda.fit,test_data) +qda.pred=predict(qda.fit,test_data,na.rm=T) +mean(qda.pred$class!=test_data$mpg01,na.rm=T) +glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial) +glm.probs=predict(glm.fit,test_data,type="response") +glm.pred=rep(0,199) +glm.pred[glm.probs>.5]=1 +table(glm.pred,test_data$mpg01) +mean(glm.pred!=test_data$mpg01) +library(class) +?cbind +?knn +knn.fit = knn(train_data,test_data,auto$mpg01[training_indices]) +knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1) +knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1) +?knn +training_indices +train_bools +knn.fit = knn(train_data,test_data,auto$mpg01[train_bools],k=1) +sdf = (mpg01<1) +sdf = (auto$mpg01<1) +sdf +train_bools +cbind(horsepower,displacement) +cbind(train_data$horsepower,displacement) +cbind(train_data$horsepower,train_data$displacement) +cbind(auto$horsepower,auto$displacement)[train_bools] +cbind(auto$horsepower,auto$displacement)[train_bools,] +cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.mpg01 = auto[train_bools] +train.mpg01 = auto$mpg01[train_bools] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.mpg01 = auto$mpg01[train_bools] +set.seed(56) +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +?cbind +?Knn +?knn +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.mpg01 = auto$mpg01[train_bools] +train.X = train.X[!is.na(train.X)] +test.X = data.frame(test.X, +train.mpg01 = train.mpg01[!is.na(train.mpg01)] +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +length(train.mpg01) +length(test.X) +text.X +test.X +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +length(test.X) +test.X +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +train.X +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +train.X +test.X +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +?knn +length(train.X) +length(train.X[1,]) +length(train.X[,1]) +?knn +plot(auto) +train.X = cbind(auto$horsepower,auto$displacement)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement)[!train_bools,] +train.mpg01 = auto$mpg01[train_bools] +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +train.X +test.X +train.mpg01 +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +q() +train +train.X +train.X +test.X +p1 = seq(1:1)) +p1 = 1:10 +p1 +p1 = ,1:10 +p2 = 10:20 +p2 +cbind(p1,p2) +p3=c(1,2,3,4,5,7,9,8,10) +p4=c(10,11,12,13,14,15,16,17,18,29,20) +px = cbind(p1,p2) +py = cbind(p3,p4) +py +?formula +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +train.mpg01 = auto$mpg01[train_bools] +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +import(library) +library(MASS) +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +library(library) +library(class) +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +knn.pred +summary(test_data) +summary(train_data) +fix(train_data) +?fix +fix(test_data) +test_data.fix +test_data.fix() +fix(test_data) +q() diff --git a/hw3/answers.ps b/hw3/answers.ps new file mode 100644 index 0000000..9515fa2 --- /dev/null +++ b/hw3/answers.ps @@ -0,0 +1,3137 @@ +%!PS-Adobe-3.0 +%%Title: Otho Ulrich - HW #4 +%%Creator: paps version 0.6.7 by Dov Grobgeld +%%Pages: (atend) +%%BoundingBox: 0 0 595 841 +%%BeginProlog +%%Orientation: Portrait +/papsdict 1 dict def +papsdict begin + +/inch {72 mul} bind def +/mm {1 inch 25.4 div mul} bind def + +% override setpagedevice if it is not defined +/setpagedevice where { + pop % get rid of its dictionary + /setpagesize { + 3 dict begin + /pageheight exch def + /pagewidth exch def + /orientation 0 def + % Exchange pagewidth and pageheight so that pagewidth is bigger + pagewidth pageheight gt { + pagewidth + /pagewidth pageheight def + /pageheight exch def + /orientation 3 def + } if + 2 dict + dup /PageSize [pagewidth pageheight] put + dup /Orientation orientation put + setpagedevice + end + } def +} +{ + /setpagesize { pop pop } def +} ifelse +/duplex { + statusdict /setduplexmode known + { statusdict begin setduplexmode end } {pop} ifelse +} def +/tumble { + statusdict /settumble known + { statusdict begin settumble end } {pop} ifelse +} def +% Turn the page around +/turnpage { + 90 rotate + 0 pageheight neg translate +} def +% User settings +/pagewidth 595 def +/pageheight 841 def +pagewidth pageheight setpagesize +/column_width 523 def +/bodyheight 755 def +/lmarg 36 def +/ytop 791 def +/do_separation_line true def +/do_landscape false def +/do_tumble true def +/do_duplex true def +% Procedures to translate position to first and second column +/lw 20 def % whatever +/setnumcolumns { + /numcolumns exch def + /firstcolumn { /xpos lmarg def /ypos ytop def} def + /nextcolumn { + do_separation_line { + xpos column_width add gutter_width 2 div add % x start + ytop lw add moveto % y start + 0 bodyheight lw add neg rlineto 0 setlinewidth stroke + } if + /xpos xpos column_width add gutter_width add def + /ypos ytop def + } def +} def + +1 setnumcolumns +/showline { + /y exch def + /s exch def + xpos y moveto + column_width 0 rlineto stroke + xpos y moveto /Helvetica findfont 20 scalefont setfont s show +} def +/paps_bop { % Beginning of page definitions + papsdict begin + gsave + do_landscape {turnpage} if + % ps2pdf gets wrong orientation without this! + /Helvetica findfont setfont 100 100 moveto ( ) show + firstcolumn + end +} def + +/paps_eop { % End of page cleanups + grestore +} def +%%BeginProlog +/papsdict 1 dict def +papsdict begin + +/conicto { + /to_y exch def + /to_x exch def + /conic_cntrl_y exch def + /conic_cntrl_x exch def + currentpoint + /p0_y exch def + /p0_x exch def + /p1_x p0_x conic_cntrl_x p0_x sub 2 3 div mul add def + /p1_y p0_y conic_cntrl_y p0_y sub 2 3 div mul add def + /p2_x p1_x to_x p0_x sub 1 3 div mul add def + /p2_y p1_y to_y p0_y sub 1 3 div mul add def + p1_x p1_y p2_x p2_y to_x to_y curveto +} bind def +/start_ol { gsave } bind def +/end_ol { closepath fill grestore } bind def +/draw_char { fontdict begin gsave 0.001000 dup scale last_x last_y translate load exec end grestore} def +/goto_xy { fontdict begin /last_y exch def /last_x exch def end } def +/goto_x { fontdict begin /last_x exch def end } def +/fwd_x { fontdict begin /last_x exch last_x add def end } def +/c /curveto load def +/x /conicto load def +/l /lineto load def +/m /moveto load def +end +/paps_exec { + 1 dict begin + /ps exch def + /len ps length def + /pos 0 def + + % Loop over all the characters of the string + { + pos len eq {exit} if + + % Get character at pos + /ch ps pos 1 getinterval def + + % check for + + (+) ch eq { + /pos 1 pos add def + /xp ps pos 8 getinterval cvi def + /yp ps pos 8 add 8 getinterval cvi def + /pos 16 pos add def + papsdict begin xp yp goto_xy end + } { + (*) ch eq { + /pos 1 pos add def + /xp ps pos 8 getinterval cvi def + /pos 8 pos add def + papsdict begin xp goto_x end + } { (>) ch eq { + /pos 1 pos add def + /xp ps pos 4 getinterval cvi def + /pos 4 pos add def + papsdict begin xp 2 mul fwd_x end + } { (-) ch eq { + /pos 1 pos add def + /xp ps pos 4 getinterval cvi def + /pos 4 pos add def + papsdict begin xp neg 2 mul fwd_x end + } { + % Must be a 3 char sym. Load and exec + /name ps pos 3 getinterval def + papsdict begin name draw_char end + /pos 3 pos add def + } ifelse + } ifelse + } ifelse + } ifelse + } loop + end +} def +/fontdict 1 dict def +papsdict begin fontdict begin +/AAA { start_ol +4464 0 m +2736 0 l +2736 7272 l +504 7272 l +504 8784 l +6696 8784 l +6696 7272 l +4464 7272 l +4464 0 l +7272 fwd_x +end_ol + } def +/BAA { start_ol +936 2336 m +936 6552 l +2664 6552 l +2664 2596 l +2664 1896 2857 1596 x +3052 1296 3504 1296 x +3955 1296 4209 1699 x +4464 2104 4464 2833 x +4464 6552 l +6192 6552 l +6192 0 l +4464 0 l +4464 1019 l +4284 464 3852 159 x +3420 -144 2810 -144 x +1882 -144 1408 481 x +936 1108 936 2336 x +7272 fwd_x +end_ol + } def +/CAA { start_ol +6480 360 m +5883 108 5263 -18 x +4644 -144 3954 -144 x +2311 -144 1443 731 x +576 1606 576 3252 x +576 4846 1413 5771 x +2251 6696 3697 6696 x +5157 6696 5962 5823 x +6768 4950 6768 3364 x +6768 2664 l +2304 2664 l +2309 1938 2760 1580 x +3213 1224 4110 1224 x +4702 1224 5277 1396 x +5852 1569 6480 1944 x +6480 360 l +5040 3960 m +5028 4635 4695 4981 x +4363 5328 3717 5328 x +3133 5328 2786 4970 x +2440 4612 2376 3954 x +5040 3960 l +7272 fwd_x +end_ol + } def +/EAA { start_ol +6552 7272 m +2808 7272 l +2808 5328 l +6192 5328 l +6192 3816 l +2808 3816 l +2808 0 l +1080 0 l +1080 8784 l +6552 8784 l +6552 7272 l +7272 fwd_x +end_ol + } def +/FAA { start_ol +4968 3276 m +4968 4203 4653 4729 x +4339 5256 3785 5256 x +3232 5256 2911 4729 x +2592 4203 2592 3276 x +2592 2349 2911 1822 x +3232 1296 3785 1296 x +4339 1296 4653 1822 x +4968 2349 4968 3276 x +2592 5598 m +2908 6147 3326 6421 x +3745 6696 4272 6696 x +5437 6696 6066 5811 x +6696 4927 6696 3276 x +6696 1647 6075 751 x +5455 -144 4331 -144 x +3739 -144 3299 148 x +2860 441 2592 1019 x +2592 0 l +864 0 l +864 9144 l +2592 9144 l +2592 5598 l +7272 fwd_x +end_ol + } def +/GAA { start_ol +2543 1512 m +6203 1512 l +6203 0 l +648 0 l +648 1465 l +1591 2466 l +3272 4248 3632 4659 x +4074 5166 4269 5556 x 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+28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" +26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" +20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" +13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" +19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" +19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" +16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" +16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" +13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" +13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" +13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" +31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" +30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" +36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" +25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" +33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" +17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" +17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" +15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" +15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" +17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" +20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" +19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" +18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" +16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" +15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" +15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" +16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" +29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" +24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" +26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" +25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" +30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" +33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" +30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" +30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" +22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" +21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" +21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" +43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" +36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" +32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" +39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" +36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" +19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" +19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" +20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" +19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" +20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" +20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" +25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" +20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" +19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" +20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" +20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" +18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" +18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" +19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" +17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" +18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" +17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" +30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" +27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" +27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" +30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" +21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" +23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" +23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" +23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" +20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" +17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" +21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" +16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" +31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" +29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" +21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" +19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" +22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" +20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" +20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" +17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" +17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" +16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" +18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" +16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" +15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" +19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" +18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" +31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" +34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" +35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" +27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" +25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" +23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" +27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" +23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" +34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" +34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" +31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" +37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" +28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" +28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" +26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" +33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" +41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" +38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" +32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" +37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" +28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" +26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" +24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" +19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" +34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" +29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" +31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" +37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" +32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" +46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" +27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" +40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" +44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" +43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" +36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" +30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" +44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" +40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" +33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" +29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" +32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" +23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" +35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" +23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" +32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" +27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" +26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" +25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" +23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" +30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" +39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" +39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" +35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" +32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" +37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" +37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" +34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" +34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" +34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" +29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" +33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" +34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" +33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" +32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" +32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" +31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" +28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" +30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" +25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" +24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" +22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" +26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" +20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" +17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" +28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" +27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" +34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" +31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" +29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" +27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" +24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" +36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" +37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" +31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" +38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" +36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" +36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" +36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" +34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" +38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" +32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" +38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" +25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" +38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" +26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" +22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" +32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" +36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" +27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" +27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" +44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" +32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" +28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" +31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" diff --git a/hw4/.RData b/hw4/.RData new file mode 100644 index 0000000..416ffd5 Binary files /dev/null and b/hw4/.RData differ diff --git a/hw4/.Rhistory b/hw4/.Rhistory new file mode 100644 index 0000000..a9fedf3 --- /dev/null +++ b/hw4/.Rhistory @@ -0,0 +1,249 @@ +auto = read.table("auto.data",header=T,na.strings="?") +auto$mpg01=rep(0,397) +auto$mpg01[auto$mpg>median(auto$mpg)]=1 +library(ISLR) +library(MASS) +library(class) +train_bools <- (auto$year %% 2 == 0) +train_data = auto[train_bools,] +test_data = auto[!train_bools,] +help(knn) +help(knn) + train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3]) + test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3]) +train +test +?knn + cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) +cl +length(cl) +length(train) +nrows(train) +nrow(train) +train.X +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +train.X +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +test.X +train.X +train.mpg01 = auto$mpg01[train_bools] +train.mpg01 +length(train.mpg01) +nrow(train.X) +knn(train.X,train.Y,train.mpg01,K=1) +knn(train.X,train.Y,train.mpg01,k=1) +knn(train.X,test.X,train.mpg01,k=1) +train.X +na.omit(train.X) +?na.omit +na.omit(train.X) +na.omit(train.X) +knn(na.omit(train.X),test.X,train.mpg01,k=1) +knn(na.omit(train.X),test.X,na.omit(train.mpg01),k=1) +knn(na.omit(train.X),na.omit(test.X),na.omit(train.mpg01),k=1) +train.mpg012 = na.omit(auto$mpg01)[train_bools] +train.mpg012 +train.mpg01 +nrow(train) +na.omit(auto) +auto +na.omit(auto) +summary(auto) +summary(na.omit(auto)) +Auto = na.omit(auto) +auto = na.omit(auto) +ncol(auto) +nrow(auto) +auto <- na.omit(auto) +train_bools <- (auto$year %% 2 == 0) +train_data = auto[train_bools,] +test_data = auto[!train_bools,] +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,] +train.mpg01 = auto$mpg01[train_bools] +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +mean(knn.pred != auto$mpg01) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=2) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=3) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=4) +mean(knn.pred != test_data$mpg0) +knn.pred +length(knn.pred) +dim(knn.pred) +length(test_data) +ncol(test_data) +nrow(test_data) +q() +qda.fit +fit.qda +qda.fit +auto +qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) +import(MASS) +qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) +import(class) +library(MASS) +qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) +qda.fit +> mean(qda.pred$class!=test_data$mpg01,na.rm=T) +mean(qda.pred$class!=test_data$mpg01,na.rm=T) +qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data) +qda.pred=predict(qda.fit,test_data,na.rm=T) +mean(qda.pred$class!=test_data$mpg01,na.rm=T) +qda.fit=qda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data) +qda.pred=predict(qda.fit,test_data,na.rm=T) +mean(qda.pred$class!=test_data$mpg01,na.rm=T) +qda.fit +qda.pred=predict(qda.fit,test_data,na.rm=T) +mean(qda.pred$class!=test_data$mpg01,na.rm=T) +glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial) +glm.probs=predict(glm.fit,test_data,type="response") +glm.pred=rep(0,199) +glm.pred[glm.probs>.5]=1 +mean(glm.pred!=test_data$mpg01) +glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial) +glm.probs=predict(glm.fit,test_data,type="response") +glm.pred=rep(0,length(test_data) +glm.pred[glm.probs>.5]=1 +mean(glm.pred!=test_data$mpg01) +glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial) +glm.probs=predict(glm.fit,test_data,type="response") +glm.pred=rep(0,length(test_data)) +glm.pred[glm.probs>.5]=1 +mean(glm.pred!=test_data$mpg01) +glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial) +glm.probs=predict(glm.fit,test_data,type="response") +glm.pred=rep(0,length(test_data)) +glm.pred[glm.probs>.5]=1 +mean(glm.pred!=test_data$mpg01) +glm.pred +glm.pred=rep(0,length(test_data)) +glm.pred +test_data +glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial) +glm.probs=predict(glm.fit,test_data,type="response") +glm.pred=rep(0,nrow(test_data)) +glm.pred[glm.probs>.5]=1 +mean(glm.pred!=test_data$mpg01) +set.seed(1) +auto <- na.omit(auto) +train_bools <- (auto$year %% 2 == 0) +train_data = auto[train_bools,] +test_data = auto[!train_bools,] +train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[train_bools,] +test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[!train_bools,] +train.mpg01 = auto$mpg01[train_bools] +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=2) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=3) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=4) +mean(knn.pred != test_data$mpg0) +import(class) +library(class) +knn.pred = knn(train.X,test.X,train.mpg01,k=1) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=2) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=3) +mean(knn.pred != test_data$mpg01) +knn.pred = knn(train.X,test.X,train.mpg01,k=4) +mean(knn.pred != test_data$mpg0) +q() +library(boot) +library(MASS) +library(ISLr) +library(ISLR) +data(Default) +set.seed(45) +fit.glm = glm(default ~ income + balance,Default) +fit.glm = glm(default ~ income + balance,Default,family="binomial) +fit.glm = glm(default ~ income + balance,Default,family="binomial") +summary(glm) +summary(fit.glm) +fit.glm = glm(default ~ income + balance,Default) +fit.glm = glm(default ~ income + balance,data = Default) +fit.glm = glm(default ~ income + balance,Default,family="binomial") +length(Default) +length(Default$student) +train.default1 = default[1:6001,] +train.default1 = Default[1:6001,] +train.default2 = Default[1:5001,] +test.default1 = Default[!1:6001,] +test.default1 +head(test.default1) +test.default1$student +test.default1 = Default[6002:10000,] +head(test.default1) +test.default2 = Default[5002:10000,] +fit.glm.default1 = glm.fit(default ~ income + balance,data=Default,family="binomial") +fit.glm.default1 = glm.fit(default ~ income + balance,Default,family="binomial") +fit.glm = glm(default ~ income + balance,Default,family="binomial") +fit.glm.default1 = glm(default ~ income + balance,Default,family="binomial") +fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial") +summary(fit.glm.default1) +fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial") +fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial") +fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial") +fit.glm.default1.prob = predict(fit.glm.default1,test.default1,type="response") +fit.glm.default1.prob +fit.glm.default1.pred = rep("No",nrow(test.default1)) +fit.glm.default1.pred +fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes" +fit.glm.default1.pred +fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"fit.glm.default1.pred = rep("No",nrow(test.default1)) +table(fit.glm.default1.pred,test.default1) +fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes" +fit.glm.default1.pred +test.default1 +length(test.default1$student) +length(fit.glm.default1.pred) +table(fit.glm.default1.pred,test.default1$default) +1 - (3851+44)/(3851+90+14+44) +fit.glm.default2.prob = predict(fit.glm.default2,test.default2,type="response") +fit.glm.default2.pred = rep("No",nrow(test.default2)) +fit.glm.default2.pred[fit.glm.default2.prob > 0.5] = "Yes" +table(fit.glm.default.pred,test.default2$default) +table(fit.glm.default2.pred,test.default2$default) +1 - (4818+52)/(4818+106+23+52) +summary(fit.glm.default2) +summary(fit.glm.default1) +summary(fit.glm.default2) +coefficients(fit.glm.default1) +fit.glm.default1$coefficients +fit.glm.default1$coefficients[1,2] +fit.glm.default1$coefficients[1:2] +fit.glm.default1$coefficients[2:3] + boot.fn = function(Default,index){ + model = glm(default ~ income + balance,Default,family="binomial",subset=index) + fit.glm.default1$coefficients[2:3] + } +boot.fn(Default,c(14,5,79)) +boot.fn(Default,c(14,5,79,324,6435,234)) +boot.fn(Default,seq(15:7000)) + boot.fn = function(Default,index){ + model = glm(default ~ income + balance,Default,family="binomial",subset=index) + model$coefficients[2:3] + } + boot.fn = function(Default,index){ + boot.fn = function(Default,index){ + model = glm(default ~ income + balance,Default,family="binomial",subset=index) + model$coefficients[2:3] + } +boot.fn(Default,seq(15:7000)) +boot.fn(Default,seq(15:3000)) +boot.fn(Default,seq(15:3050)) +boot.fn(Default,seq(15:3500)) +set.seed(56) +?boot +boot(Default,boot.fn,c(1:1000)) +?boot +boot(Default,boot.fn,1000) +4.68/7.06 +2.32/3.232 +q() diff --git a/hw4/answers b/hw4/answers new file mode 100644 index 0000000..8219be0 --- /dev/null +++ b/hw4/answers @@ -0,0 +1,140 @@ +We now review k-fold cross-validation. + +────────────────────────────────────────────────────────────────────────── + (a) Explain how k-fold cross-validation is implemented. + + The data are split into k groups of about equal size. The + first group is set aside to be used as a validation set. The + model is trained on the remaining sets, and an MSE is + computed using the validation set. This process is repeated + using the next set as a validation set, producing a second + MSE. The process is performed on each of the k groups the + data were split into, producing k MSEs, one for each + validation set. Given a good value of k, this mean squared + error is a good approximation for the minimum point along + the flexibility axis that allows us to judge the appropriate + flexibility to model some data, i.e., to estimate the right + bias-variance tradeoff for a particular model. + +────────────────────────────────────────────────────────────────────────── + (b) What are the advantages and disadvantages of k-fold cross- + validation relative to: + + i. The validation set approach? + + The validation set approach is basically k-fold with k=2. + This gives a biased measure of the test error rate. However, + it's very quick to compute. Of course, the MSE here will + have no variance because we are only taking a single MSE + from a single test set: so this gives large bias of the MSE + but no variance. + + ii. LOOCV? + + LOOCV is on the other end of the spectrum from this. It + gives a very unbiased MSE for each validation set because + each validation set is a single datum and is being tested + against a nearly-full training set, compared to the overall + data set. However, the MSEs will be highly-correlated + because the training sets are almost completely the same, + and this guarantees a high variance. + + K-fold cross-validation occupies the space in-between, where + it has some bias and some variance of the MSE. Empirically, + we know that k=5 and k=10 are two values that often work + well to provide good estimates of these MSEs, because they + provide balanced bias and variance. + + + + +6. We continue to consider the use of a logistic regression model to + predict the probability of default using income and balance on + the Default data set. In particular, we will now compute + estimates for the standard errors of the income and balance + logistic regression co- efficients in two different ways: (1) + using the bootstrap, and (2) using the standard formula for + computing the standard errors in the glm() function. Do not + forget to set a random seed before beginning your analysis. + +────────────────────────────────────────────────────────────────────────── + (a) Using the summary() and glm() functions, determine the esti- + mated standard errors for the coefficients associated with + income and balance in a multiple logistic regression model that + uses both predictors. + + + 10,000 entries in the table, so testing 2 ways to separate the data, + + > train.default1 = Default[1:6001,] + > train.default2 = Default[1:5001,] + > test.default1 = Default[6002:10000,] + > test.default2 = Default[5002:10000,] + + > fit.glm.default1 = glm(default ~ income + balance,train.default1, family="binomial") + > fit.glm.default2 = glm(default ~ income + balance,train.default2, family="binomial") + + *** from summary(fit.glm.default1), we get std. error + + Coefficients: + Estimate Std. Error + (Intercept) -1.156e+01 5.622e-01 + income 2.237e-05 6.491e-06 + balance 5.649e-03 2.916e-04 + + + *** from summary(fit.glm.default2), we get std. error + + Coefficients: + Estimate Std. Error + (Intercept) -1.198e+01 6.279e-01 + income 2.885e-05 7.060e-06 + balance 5.822e-03 3.232e-04 + +────────────────────────────────────────────────────────────────────────── + (b) Write a function, boot.fn() , that takes as input the\ + Default data set as well as an index of the observations, and + that outputs the coefficient estimates for income and balance in + the multiple logistic regression model. + + boot.fn = function(Default,index){ + model = glm(default ~ income + balance,Default,family="binomial",subset=index) + model$coefficients[2:3] + } + +────────────────────────────────────────────────────────────────────────── + (c) Use the boot() function together with your boot.fn() + function to estimate the standard errors of the logistic + regression coefficients for income and balance . + + > set.seed(56) + > boot(Default,boot.fn,1000) + + Bootstrap Statistics : + original bias std. error + t1* 2.080898e-05 1.436086e-07 4.679106e-06 + t2* 5.647103e-03 1.876364e-05 2.320547e-04 + + *** + The std. error in t1 above is the std. error for the income + coefficient, and under t2, the std. error for the balance + coefficient. + +────────────────────────────────────────────────────────────────────────── + (d) Comment on the estimated standard errors obtained using the + glm() function and using your bootstrap function. + + + The bootstrap method uses random sampling to approximate + sampling a real response. This gives a stronger σ² + (variance) estimate than the formulaic approach used to + calculate the std err for a linear regression model. The std + err estimates from the bootstrap method are therefore more + reliable than those computed earlier in this exercise. + + The std err from bootstrap for the income coefficient is + about 66% of that computed from the linear regression model + directly. This is 72% for the coefficient of balance. These + smaller values should be considered reliable, that is, we + can believe the smaller errors compared to the standard + meothods, because of the reason I explained above. \ No newline at end of file diff --git a/hw4/answers.pdf b/hw4/answers.pdf new file mode 100644 index 0000000..78fc044 Binary files /dev/null and b/hw4/answers.pdf differ diff --git a/hw4/answers.ps b/hw4/answers.ps new file mode 100644 index 0000000..edfe59e --- /dev/null +++ b/hw4/answers.ps @@ -0,0 +1,2793 @@ +%!PS-Adobe-3.0 +%%Title: Otho Ulrich - HW #4 +%%Creator: paps version 0.6.7 by Dov Grobgeld +%%Pages: (atend) +%%BoundingBox: 0 0 595 841 +%%BeginProlog +%%Orientation: Portrait +/papsdict 1 dict def +papsdict begin + +/inch {72 mul} bind def +/mm {1 inch 25.4 div mul} bind def + +% override setpagedevice if it is not defined +/setpagedevice where { + pop % get rid of its dictionary + /setpagesize { + 3 dict begin + /pageheight exch def + /pagewidth exch def + /orientation 0 def + % Exchange pagewidth and pageheight so that pagewidth is bigger + pagewidth pageheight gt { + pagewidth + /pagewidth pageheight def + /pageheight exch def + /orientation 3 def + } if + 2 dict + dup /PageSize [pagewidth pageheight] put + dup /Orientation orientation put + setpagedevice + end + } def +} +{ + /setpagesize { pop pop } def +} ifelse +/duplex { + statusdict /setduplexmode known + { statusdict begin setduplexmode end } {pop} ifelse +} def +/tumble { + statusdict /settumble known + { statusdict begin settumble end } {pop} ifelse +} def +% Turn the page around +/turnpage { + 90 rotate + 0 pageheight neg translate +} def +% User settings +/pagewidth 595 def +/pageheight 841 def +pagewidth pageheight setpagesize +/column_width 523 def +/bodyheight 755 def +/lmarg 36 def +/ytop 791 def +/do_separation_line true def +/do_landscape false def +/do_tumble true def +/do_duplex true def +% Procedures to translate position to first and second column +/lw 20 def % whatever +/setnumcolumns { + /numcolumns exch def + /firstcolumn { /xpos lmarg def /ypos ytop def} def + /nextcolumn { + do_separation_line { + xpos column_width add gutter_width 2 div add % x start + ytop lw add moveto % y start + 0 bodyheight lw add neg rlineto 0 setlinewidth stroke + } if + /xpos xpos column_width add gutter_width add def + /ypos ytop def + } def +} def + +1 setnumcolumns +/showline { + /y exch def + /s exch def + xpos y moveto + column_width 0 rlineto stroke + xpos y moveto /Helvetica findfont 20 scalefont setfont s show +} def +/paps_bop { % Beginning of page definitions + papsdict begin + gsave + do_landscape {turnpage} if + % ps2pdf gets wrong orientation without this! + /Helvetica findfont setfont 100 100 moveto ( ) show + firstcolumn + end +} def + +/paps_eop { % End of page cleanups + grestore +} def +%%BeginProlog +/papsdict 1 dict def +papsdict begin + +/conicto { + /to_y exch def + /to_x exch def + /conic_cntrl_y exch def + /conic_cntrl_x exch def + currentpoint + /p0_y exch def + /p0_x exch def + /p1_x p0_x conic_cntrl_x p0_x sub 2 3 div mul add def + /p1_y p0_y conic_cntrl_y p0_y sub 2 3 div mul add def + /p2_x p1_x to_x p0_x sub 1 3 div mul add def + /p2_y p1_y to_y p0_y sub 1 3 div mul add def + p1_x p1_y p2_x p2_y to_x to_y curveto +} bind def +/start_ol { gsave } bind def +/end_ol { closepath fill grestore } bind def +/draw_char { fontdict begin gsave 0.001000 dup scale last_x last_y translate load exec end grestore} def +/goto_xy { fontdict begin /last_y exch def /last_x exch def end } def +/goto_x { fontdict begin /last_x exch def end } def +/fwd_x { fontdict begin /last_x exch last_x add def end } def +/c /curveto load def +/x /conicto load def +/l /lineto load def +/m /moveto load def +end +/paps_exec { + 1 dict begin + /ps exch def + /len ps length def + /pos 0 def + + % Loop over all the characters of the string + { + pos len eq {exit} if + + % Get character at pos + /ch ps pos 1 getinterval def + + % check for + + (+) ch eq { + /pos 1 pos add def + /xp ps pos 8 getinterval cvi def + /yp ps pos 8 add 8 getinterval cvi def + /pos 16 pos add def + papsdict begin xp yp goto_xy end + } { + (*) ch eq { + /pos 1 pos add def + /xp ps pos 8 getinterval cvi def + /pos 8 pos add def + papsdict begin xp goto_x end + } { (>) ch eq { + /pos 1 pos add def + /xp ps pos 4 getinterval cvi def + /pos 4 pos add def + papsdict begin xp 2 mul fwd_x end + } { (-) ch eq { + /pos 1 pos add def + /xp ps pos 4 getinterval cvi def + /pos 4 pos add def + papsdict begin xp neg 2 mul fwd_x end + } { + % Must be a 3 char sym. 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{ start_ol +5531 4033 m +4968 4822 4307 5184 3384 5184 c +1895 5184 756 4092 756 2669 c +756 1245 1895 144 3371 144 c +4307 144 4979 504 5531 1293 c +5531 -1740 l +4356 -1740 l +4140 -1740 4032 -1824 4032 -1980 c +4032 -2148 4140 -2232 4356 -2232 c +6671 -2232 l +6887 -2232 6995 -2148 6995 -1980 c +6995 -1824 6887 -1740 6671 -1740 c +6023 -1740 l +6023 4547 l +6671 4547 l +6887 4547 6995 4631 6995 4799 c +6995 4955 6887 5040 6671 5040 c +5531 5040 l +5531 4033 l +3384 4691 m +4595 4691 5531 3805 5531 2644 c +5531 1532 4572 635 3384 635 c +2183 635 1247 1532 1247 2669 c +1247 3805 2183 4691 3384 4691 c +7200 fwd_x +end_ol + } def +/JBA { start_ol +5688 5040 m +1487 5040 l +1487 3881 l +1487 3663 1571 3555 1739 3555 c +1895 3555 1980 3663 1980 3881 c +1980 4547 l +4991 4547 l +1379 432 l +1379 0 l +5868 0 l +5868 1170 l +5868 1387 5783 1496 5627 1496 c +5459 1496 5375 1387 5375 1170 c +5375 491 l +2052 491 l +5688 4606 l +5688 5040 l +7200 fwd_x +end_ol + } def +/KBA { start_ol +2484 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1512 -2232 1728 -2232 c +3276 -2232 l +4367 -2232 5135 -1451 5135 -328 c +5135 5040 l +1751 5040 l +1535 5040 1427 4955 1427 4799 c +1427 4631 1535 4547 1751 4547 c +4644 4547 l +4608 7511 m +3899 7511 l +3899 6264 l +4608 6264 l +4608 7511 l +7200 fwd_x +end_ol + } def +/QBA { start_ol +3539 1355 m +3071 1355 2711 1019 2711 576 c +2711 131 3071 -216 3539 -216 c +3659 -216 l +4127 -216 4487 119 4487 576 c +4487 1019 4127 1355 3659 1355 c +3539 1355 l +3539 5040 m +3071 5040 2711 4691 2711 4246 c +2711 3803 3071 3454 3539 3454 c +3659 3454 l +4127 3454 4487 3803 4487 4246 c +4487 4691 4127 5040 3659 5040 c +3539 5040 l +7200 fwd_x +end_ol + } def +/RBA { start_ol +1607 6334 m +1607 5509 l +1607 5290 1692 5181 1859 5181 c +2016 5181 2099 5290 2099 5509 c +2099 6006 l +2723 6322 3107 6419 3647 6419 c +4644 6419 5351 5848 5351 5047 c +5351 4356 4763 3834 3371 3240 c +3371 2423 l +3371 2207 3456 2099 3623 2099 c +3780 2099 3863 2207 3863 2423 c +3863 2914 l +5364 3628 5843 4150 5843 5047 c 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