Final Project 607: Naive Bayes
Rajwant Mishra
May 8, 2019
Prepare Data
Pacakge
Load data
# load data
workDir <- getwd()
filePath = paste0(workDir,"/Data")
g_max <- 1048576
train_Data<- read_tsv("https://raw.githubusercontent.com/Rajwantmishra/msds/master/607/Project5/607Project/data/drugLibTrain_raw.tsv")## Warning: Missing column names filled in: 'X1' [1]test_Data<- read_tsv("https://raw.githubusercontent.com/Rajwantmishra/msds/master/607/Project5/607Project/data/drugLibTest_raw.tsv")## Warning: Missing column names filled in: 'X1' [1]glimpse(train_Data)## Observations: 3,107
## Variables: 9
## $ X1 <dbl> 2202, 3117, 1146, 3947, 1951, 2372, 1043, 27...
## $ urlDrugName <chr> "enalapril", "ortho-tri-cyclen", "ponstel", ...
## $ rating <dbl> 4, 1, 10, 3, 2, 1, 9, 10, 10, 1, 7, 8, 8, 9,...
## $ effectiveness <chr> "Highly Effective", "Highly Effective", "Hig...
## $ sideEffects <chr> "Mild Side Effects", "Severe Side Effects", ...
## $ condition <chr> "management of congestive heart failure", "b...
## $ benefitsReview <chr> "slowed the progression of left ventricular ...
## $ sideEffectsReview <chr> "cough, hypotension , proteinuria, impotence...
## $ commentsReview <chr> "monitor blood pressure , weight and asses f...Working with One Row
NROW(unique(train_Data$X1)) # indicate X1 is unique ## [1] 3107t(train_Data[1,])## [,1]
## X1 "2202"
## urlDrugName "enalapril"
## rating "4"
## effectiveness "Highly Effective"
## sideEffects "Mild Side Effects"
## condition "management of congestive heart failure"
## benefitsReview "slowed the progression of left ventricular dysfunction into overt heart failure \r\r\nalone or with other agents in the managment of hypertension \r\r\nmangagement of congestive heart failur"
## sideEffectsReview "cough, hypotension , proteinuria, impotence , renal failure , angina pectoris , tachycardia , eosinophilic pneumonitis, tastes disturbances , anusease anorecia , weakness fatigue insominca weakness"
## commentsReview "monitor blood pressure , weight and asses for resolution of fluid"train_Data$condition[1]## [1] "management of congestive heart failure"train_Data$sideEffects[1]## [1] "Mild Side Effects"train_Data$sideEffectsReview[1]## [1] "cough, hypotension , proteinuria, impotence , renal failure , angina pectoris , tachycardia , eosinophilic pneumonitis, tastes disturbances , anusease anorecia , weakness fatigue insominca weakness"train_Data$commentsReview[1]## [1] "monitor blood pressure , weight and asses for resolution of fluid"#
#https://www.innerbody.com/diseases-conditions
# https://kidspicturedictionary.com/english-through-pictures/people-english-through-pictures/human-body/Using SVM suport vector meothd .
Since data points were too high it was not possible to run the model with full data on the working machine. Result of this Methods were not accurate . All the valuses were calssifed as one.
# library(e1071)
t(train_Data[2,])## [,1]
## X1 "3117"
## urlDrugName "ortho-tri-cyclen"
## rating "1"
## effectiveness "Highly Effective"
## sideEffects "Severe Side Effects"
## condition "birth prevention"
## benefitsReview "Although this type of birth control has more cons than pros, it did help with my cramps. It's also effective with the prevention of pregnancy. (Along with use of condoms as well)"
## sideEffectsReview "Heavy Cycle, Cramps, Hot Flashes, Fatigue, Long Lasting Cycles. It's only been 5 1/2 months, but i'm concidering changing to a different bc. This is my first time using any kind of bc, unfortunately due to the constant hassel, i'm not happy with the results."
## commentsReview "I Hate This Birth Control, I Would Not Suggest This To Anyone."# str_detect(train_Data$condition,"hirschsprungs")
# t(conditionBuffer[1,])
#
# str_detect(conditionBuffer$Dt.risknCause,"birth control")
# t(conditionBuffer[21,])
#---------------------------------------------------SVm Method
train_Data$urlDrugName <- as.factor(train_Data$urlDrugName)
train_Data$effectiveness <- as.factor(train_Data$effectiveness)
summary(train_Data)## X1 urlDrugName rating
## Min. : 0 lexapro : 63 Min. : 1.000
## 1st Qu.:1062 prozac : 46 1st Qu.: 5.000
## Median :2092 retin-a : 45 Median : 8.000
## Mean :2081 zoloft : 45 Mean : 7.006
## 3rd Qu.:3092 paxil : 38 3rd Qu.: 9.000
## Max. :4161 propecia: 38 Max. :10.000
## (Other) :2832
## effectiveness sideEffects condition
## Considerably Effective: 928 Length:3107 Length:3107
## Highly Effective :1330 Class :character Class :character
## Ineffective : 247 Mode :character Mode :character
## Marginally Effective : 187
## Moderately Effective : 415
##
##
## benefitsReview sideEffectsReview commentsReview
## Length:3107 Length:3107 Length:3107
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## unique(train_Data$X1)## [1] 2202 3117 1146 3947 1951 2372 1043 2715 1591 1686 458 3479 959
## [14] 928 2643 3692 338 3201 2566 723 305 1084 1246 1714 376 1571
## [27] 2436 3716 1959 1872 1038 2745 1268 797 2801 2360 630 422 330
## [40] 1899 1210 1762 1534 3060 3728 1804 1989 714 1290 2646 3100 3082
## [53] 1018 494 1239 1325 2542 2118 175 985 2120 830 3938 1351 2075
## [66] 4016 2285 768 3856 2412 3756 3077 1053 868 678 3995 1318 1441
## [79] 1483 3471 2461 427 2059 2319 927 240 1377 674 3763 3350 3193
## [92] 3435 2211 1029 732 133 1050 366 2859 3382 3144 259 4126 2555
## [105] 3541 3702 1938 1514 1987 385 2447 3364 2236 3469 705 2 272
## [118] 2839 277 1278 1588 2321 2006 3270 3582 2957 2088 3226 2518 1100
## [131] 3493 2503 1255 4145 2332 2031 2944 179 2097 2637 2145 3486 109
## [144] 691 3271 1321 1004 1906 489 4075 1261 3121 1340 591 3137 2395
## [157] 1164 2735 3439 1757 3535 1058 3394 1181 2473 1232 2585 2224 3649
## [170] 2598 2201 3718 1524 1128 884 2544 1691 3538 3867 2108 76 1470
## [183] 2226 3665 2956 1338 1743 3434 1368 3883 9 1861 1360 620 3723
## [196] 1880 69 29 2262 4013 850 2191 2345 300 3303 2268 3616 2320
## [209] 3789 1371 3940 158 878 1541 1770 1386 3118 3864 1789 2112 3941
## [222] 1536 349 1956 2724 549 1791 1498 228 37 1558 4125 2061 1284
## [235] 235 4065 2531 3885 1230 137 2445 3606 353 446 3542 2297 2823
## [248] 836 860 1843 2572 4037 1276 2945 1019 3446 1409 1273 3312 3052
## [261] 2644 2751 3386 3755 2810 1358 1564 4115 2626 53 4101 3779 3899
## [274] 795 3268 2635 1883 3814 661 3696 149 2099 2077 2667 332 856
## [287] 1042 505 320 3697 2051 1008 3708 1319 4047 1157 3456 603 3071
## [300] 98 1969 3053 40 1841 2301 113 938 454 3252 726 1114 1973
## [313] 1468 411 3847 3329 122 2243 3007 2028 3609 396 3815 1106 1570
## [326] 1665 518 706 4146 4007 406 3866 656 3064 2494 3586 1116 980
## [339] 2618 1983 1905 2350 531 161 1663 3114 2422 2733 1604 2522 1786
## [352] 1191 2124 2256 1382 3128 715 4147 2588 2687 566 1636 44 1460
## [365] 2025 1532 3683 3838 810 2232 3597 2084 1927 1143 3832 425 2001
## [378] 3407 4030 3098 3006 4004 602 2194 3289 2520 1044 787 1275 3843
## [391] 2677 1504 3502 782 988 3146 1582 3813 749 2790 1947 1923 2342
## [404] 1178 2239 1307 3529 851 3840 1654 3375 3162 82 1379 750 809
## [417] 2356 1000 2498 141 866 1485 1326 1643 4148 509 3129 3020 3338
## [430] 1774 1203 1206 121 2580 1772 3304 4121 2173 206 1660 1954 3046
## [443] 1332 3401 3051 3322 4023 269 4090 2923 1364 854 1472 135 2425
## [456] 1434 853 3158 824 890 3809 414 1592 2237 2442 747 476 1960
## [469] 456 1709 766 993 3179 793 2969 271 11 1402 1950 3276 2337
## [482] 3854 2090 676 1698 659 3970 2822 2812 2497 2877 704 3342 3571
## [495] 592 3254 2483 1966 314 119 200 3416 3559 2106 1406 3632 2828
## [508] 2294 1204 2486 946 3806 3010 1189 835 215 1846 392 27 3612
## [521] 756 2263 478 2060 1458 1898 1911 913 2138 2370 3284 4060 621
## [534] 1396 2820 2752 2832 2565 1391 132 283 2385 565 1677 925 3305
## [547] 973 1219 394 917 2882 3063 2136 3111 4161 727 2917 413 2491
## [560] 2980 1033 599 1346 3343 1067 1459 3022 1002 2313 3324 2852 3805
## [573] 2840 657 2437 1477 2276 3326 1723 2785 3277 2985 2547 671 3981
## [586] 882 241 2375 1262 340 4152 2690 1713 1985 3782 3084 3404 2567
## [599] 3285 1719 520 2788 2514 2086 770 2371 1175 474 3770 910 2548
## [612] 3143 3056 842 2283 268 1055 2306 3602 3008 107 3079 3526 2862
## [625] 1087 576 735 3989 3757 238 2267 2475 2573 2197 1436 2066 3595
## [638] 2409 1942 2323 984 2358 1487 1165 2907 1864 916 204 190 488
## [651] 3977 438 244 611 2540 3607 3150 1190 2336 3464 3748 2011 108
## [664] 1363 1085 3036 2670 3590 426 3963 3998 3807 2488 2340 1614 947
## [677] 2951 2122 1304 2804 4141 3035 816 459 254 937 303 3974 1144
## [690] 680 1561 3910 2205 144 1934 3032 1462 3422 41 4011 1560 1271
## [703] 3870 2922 3244 1674 3962 3917 2390 2257 2880 2861 3774 2665 2831
## [716] 4066 1623 2079 390 1863 1064 187 3645 251 537 357 278 1728
## [729] 3967 1473 3123 3775 3969 232 612 2746 3091 2206 328 941 2368
## [742] 785 2583 68 1711 998 1197 3902 1231 1802 2043 3892 1760 2780
## [755] 2736 3088 2278 1795 2069 2156 3176 3715 1809 638 2967 788 524
## [768] 2874 901 2325 3682 1217 1089 1023 1413 4044 3427 3351 1383 3134
## [781] 1251 2893 3484 145 8 393 2904 80 162 3120 203 1112 1454
## [794] 3259 359 1429 486 1194 13 1238 2213 2386 379 3264 294 1621
## [807] 2534 761 1069 2678 4099 3431 1061 2851 2479 345 970 3879 2845
## [820] 2899 59 3025 1584 2578 2689 967 3693 1080 3138 3213 3325 2454
## [833] 1684 2927 4130 3759 3093 440 3674 477 3638 3803 1123 1840 719
## [846] 3034 1925 2460 2730 3689 6 1830 3391 2532 1540 1381 125 2603
## [859] 3850 2652 712 2857 3087 391 2740 220 990 2630 1245 1047 58
## [872] 570 2659 817 4137 2414 2654 707 3592 261 2860 1908 1097 3787
## [885] 1301 2833 1180 3566 1142 1637 721 1759 1031 384 1565 590 2082
## [898] 1865 1488 3966 4039 3822 2196 1735 2958 2734 3109 859 2440 2741
## [911] 3239 1748 4096 3777 1796 2364 1062 823 3524 2433 2781 3796 2135
## [924] 1704 455 52 1667 1817 4035 20 1428 415 2045 194 1220 3353
## [937] 1092 3818 1224 1669 264 2504 3426 1464 2179 3669 1644 3705 3924
## [950] 3251 318 2012 2869 581 631 3808 2619 48 1850 1767 3698 807
## [963] 3722 3380 1746 2853 1897 223 2806 587 3511 3610 360 1242 1658
## [976] 3622 833 2992 3819 4153 693 3865 1610 968 468 1323 2359 1153
## [989] 1697 286 1229 3092 1725 3878 1892 4108 1493 2290 3554 1237 97
## [1002] 1105 72 3939 3701 799 1778 4045 3572 285 559 2593 4001 4133
## [1015] 315 1615 930 3041 604 1109 1603 3925 157 2017 2446 1970 1992
## [1028] 2978 4019 1659 3488 344 3992 3272 442 3853 2718 1647 3421 507
## [1041] 2636 18 2155 649 4050 776 47 2885 1940 2185 2722 1496 3374
## [1054] 2113 1784 3066 1648 2228 2837 1051 1218 1259 1015 3871 1638 1267
## [1067] 1896 128 3880 1060 2905 1264 1111 1936 3743 3912 3646 2067 1856
## [1080] 2987 1309 2887 2929 3172 2775 1270 2381 2328 3862 3258 1036 3929
## [1093] 1868 1523 1720 354 1554 880 3647 888 491 2298 3741 4105 1148
## [1106] 826 3186 669 2363 3793 3495 139 3846 2579 250 342 3243 796
## [1119] 1005 441 1310 3721 2930 920 43 2343 3387 2141 2178 270 2824
## [1132] 513 475 534 1331 897 267 233 3261 4036 2076 287 737 514
## [1145] 814 2704 2662 3393 1690 819 832 2449 279 3731 2640 3908 3876
## [1158] 2091 3935 2543 2252 3623 3205 2536 3455 3585 4009 2984 309 1838
## [1171] 2586 2470 1993 822 2315 1790 2247 3047 1550 1769 1549 1172 896
## [1184] 974 3157 3520 1195 3095 2443 3948 2723 77 1427 262 198 725
## [1197] 3292 3004 2759 2642 3174 431 3408 2288 3765 3742 2856 106 2146
## [1210] 1233 1045 515 1736 3245 1689 3834 2539 635 3057 1975 1347 4054
## [1223] 3868 301 3537 1828 2714 3574 310 2248 1330 2711 2633 4140 1599
## [1236] 1764 1102 908 645 3475 3214 2970 3385 595 2382 260 4034 2854
## [1249] 2419 3219 3026 2538 4132 1581 3410 1851 3569 2013 2996 991 2094
## [1262] 3104 101 2713 2055 1497 4156 950 1912 1265 1694 2767 2171 1465
## [1275] 508 1463 2787 1212 4058 2121 1182 3848 3760 3749 3372 2693 2937
## [1288] 147 2789 3580 2085 1797 3922 3611 2795 1151 2254 2818 2203 312
## [1301] 2807 3521 1199 3653 3102 3626 2026 3054 152 3187 1622 3736 3823
## [1314] 1650 2102 3661 2065 55 3528 1688 992 1073 3038 4002 3314 1343
## [1327] 3915 1676 255 1286 2160 1812 2849 919 3898 2892 335 553 2126
## [1340] 1727 1953 2489 2041 722 2600 140 1891 1103 1035 3873 585 1852
## [1353] 4107 934 3002 3180 1639 102 1877 2417 1895 3406 3560 4091 2339
## [1366] 2157 2246 208 1200 728 3923 19 4064 54 1870 2308 1094 3012
## [1379] 3159 3363 2326 1548 3452 2891 754 2995 1132 2143 2183 3266 3090
## [1392] 2049 3949 1579 3255 2526 1354 2888 2675 3750 1152 2275 3246 2641
## [1405] 38 3498 2875 1082 3024 3630 339 1539 2376 4033 2220 293 2379
## [1418] 1687 3553 767 2251 982 3033 2792 2181 1672 2738 2558 815 479
## [1431] 3211 978 1303 730 1431 3278 2707 3800 3762 1013 3672 1235 2793
## [1444] 2469 434 2797 2280 4052 2682 2333 3067 2125 2292 2329 2507 563
## [1457] 2562 205 1693 3395 1527 2878 2408 662 3957 2304 2519 89 647
## [1470] 764 1056 2238 827 1241 1904 3598 1531 3257 3287 143 789 2062
## [1483] 3310 1875 2169 2645 3039 171 412 2553 416 2606 1302 2952 3725
## [1496] 364 2411 2949 744 73 3302 3368 1272 2221 2373 2000 3761 2439
## [1509] 2639 1503 2215 1223 3153 2111 3891 2010 2743 2369 633 3229 3955
## [1522] 3650 1086 1415 634 1466 2047 2591 1862 3491 1813 2242 178 500
## [1535] 3827 484 3059 3331 2259 2721 3248 881 1173 1700 1348 273 3419
## [1548] 763 2231 1608 1365 3614 1609 997 2105 2528 2808 3227 1854 3070
## [1561] 1476 319 184 2939 2901 3501 3021 2040 843 3523 541 1160 805
## [1574] 2258 3744 1141 210 3065 2525 65 867 3262 2858 4098 1138 3175
## [1587] 1032 2341 341 3212 879 1517 372 2638 2272 2581 893 3634 3931
## [1600] 3676 1333 2415 3627 96 2770 2651 3045 3874 129 2574 2131 1646
## [1613] 1997 4074 2784 3345 3510 370 1799 2517 2128 1831 999 885 741
## [1626] 231 377 3751 3979 432 1370 3037 1387 1602 1149 2657 172 1887
## [1639] 2668 818 3223 2279 952 16 2038 2032 3194 367 1416 3772 3926
## [1652] 2776 2918 3561 1859 3215 3220 1946 3085 1635 3352 544 2467 1469
## [1665] 1679 1914 554 362 551 3983 81 1741 3944 355 2095 2462 2705
## [1678] 4038 556 697 3709 653 3573 3487 420 2655 3242 3619 1236 1095
## [1691] 3078 3766 2318 964 1171 371 3733 2908 1586 1495 2561 3771 1869
## [1704] 258 773 2073 2994 2030 3453 114 1827 1605 961 2302 3115 683
## [1717] 2338 56 4029 435 1063 155 3075 1021 60 2089 786 35 3539
## [1730] 1661 1066 358 1129 4122 3551 526 1207 3280 265 1596 1675 1979
## [1743] 1529 3587 3300 3000 2527 336 2610 3651 1163 891 495 582 3882
## [1756] 3089 3198 2492 1196 2666 3933 212 3200 1070 75 3594 1320 1037
## [1769] 3670 247 1606 3934 1511 1930 186 2964 2027 2731 2068 2906 1633
## [1782] 2974 1081 2925 3656 3642 1792 2435 1666 32 3601 1130 3029 445
## [1795] 3615 464 3769 4139 2192 1692 2327 1820 2816 1957 2873 2594 243
## [1808] 1715 401 2044 1996 3355 4131 3017 2710 450 1234 3295 3447 2346
## [1821] 471 3504 2835 3829 3636 1685 2884 3448 1798 2825 1844 613 1417
## [1834] 2487 4078 3240 2895 4158 2134 4070 2948 3234 2935 2742 2334 1225
## [1847] 1787 1509 3890 3700 2109 1740 542 110 1642 512 1624 2007 3362
## [1860] 2057 2104 3247 1730 1618 3301 78 2989 2330 2008 3478 2890 313
## [1873] 3152 1315 915 701 2399 466 1025 363 3149 1853 3932 1352 1369
## [1886] 3576 2081 3465 4118 1535 1040 2749 3904 3023 83 79 666 1449
## [1899] 4097 923 1450 3398 3210 2977 2266 2389 812 1521 3267 2589 660
## [1912] 1932 1452 3978 862 1814 3019 3424 3061 848 837 2943 1625 213
## [1925] 2533 3699 131 439 4151 3588 2434 2537 2968 1620 857 448 3753
## [1938] 3831 3849 3402 1738 103 3549 2848 525 1108 1832 1425 165 3600
## [1951] 911 2676 2410 3525 2432 1871 3139 2568 1752 2512 395 627 699
## [1964] 951 765 2755 2324 1392 2241 3620 2910 2847 1411 648 3900 3608
## [1977] 4109 1893 775 3788 51 2612 2554 2663 2838 3291 1384 1127 3839
## [1990] 1041 2464 2299 2158 4086 3599 111 183 2530 615 3188 3283 1717
## [2003] 1568 4059 1722 1185 2142 2545 397 1299 1641 3009 3624 3256 1754
## [2016] 2149 2098 3371 3141 573 4159 3817 3232 2998 2322 3825 1545 3914
## [2029] 2661 1885 356 610 3648 239 4076 3545 2180 3369 3166 1825 2815
## [2042] 3282 374 3965 1948 1329 1131 3739 1538 3112 3396 733 2471 2508
## [2055] 1400 3768 1418 3837 2931 2277 382 3311 2602 1513 3336 3706 1328
## [2068] 3555 1240 2249 3231 685 3218 1281 2805 2377 123 2648 2938 664
## [2081] 1807 2592 2316 3119 2725 1971 3685 230 905 3367 769 3414 3532
## [2094] 2441 3392 1756 2928 791 2162 3691 3719 4083 421 3459 2941 4129
## [2107] 1884 2628 3858 2151 802 1881 2019 2584 628 496 3457 1627 1793
## [2120] 2219 1901 794 2570 3720 2427 3945 2289 2976 3390 417 746 2139
## [2133] 1221 493 1920 1176 2622 3016 1350 828 3476 3347 1492 3994 3984
## [2146] 3745 3507 1030 2474 594 3901 3982 3804 2521 1808 3273 127 3570
## [2159] 3841 2450 403 150 4055 2407 1447 1640 3411 2455 62 2349 2269
## [2172] 2557 2413 95 3126 963 2204 3306 1587 3318 2166 236 222 3946
## [2185] 2406 1821 120 1696 2650 4160 2393 3151 2653 193 2296 181 3783
## [2198] 389 972 3506 1556 673 3536 4049 1374 1403 331 616 1293 3094
## [2211] 266 3127 1943 2212 2607 3207 1818 225 105 2426 1519 4123 632
## [2224] 2747 2080 874 2303 2110 1193 2979 2502 1902 1766 1937 711 3399
## [2237] 3557 2886 2362 2233 1699 284 1670 924 1443 3617 3405 738 1823
## [2250] 3299 2405 2367 4143 926 1412 3980 167 2482 2286 3909 12 1557
## [2263] 619 1098 90 614 3820 1414 861 3466 3881 781 1649 1998 2365
## [2276] 3269 3713 3988 28 1533 1072 3297 1922 2551 74 517 1317 3160
## [2289] 2623 2240 2014 2170 1595 1528 116 2354 3165 1078 2706 1810 2836
## [2302] 3830 3514 1611 624 3797 3263 409 2699 1634 115 288 4112 3811
## [2315] 1188 337 3658 2702 3076 46 751 3855 3224 757 462 1949 677
## [2328] 2727 1208 3786 424 325 668 350 447 713 3105 1935 1733 1228
## [2341] 3544 1155 387 1378 1345 758 2199 451 1201 703 3222 2260 1619
## [2354] 834 3167 954 3083 3732 2457 1745 1423 1419 4150 4111 3730 1184
## [2367] 3677 2018 1260 2490 21 1313 2830 2453 2311 3332 3631 3124 675
## [2380] 3952 1952 849 698 3473 1362 1139 873 606 838 3444 629 906
## [2393] 2920 1758 3260 3327 2764 2063 3379 2773 4144 321 3209 700 1186
## [2406] 2794 626 3907 2186 1016 1491 1096 852 3534 784 607 174 1209
## [2419] 2282 2476 100 3335 1250 745 334 2924 3835 2898 1716 2897 3309
## [2432] 3480 596 3003 1341 2227 4073 1562 3519 3334 1553 3581 327 1944
## [2445] 3877 643 2165 2054 2879 1478 1849 3062 3140 3443 4080 460 1052
## [2458] 1408 1573 1986 3307 2647 419 742 1890 1398 1448 3294 498 2766
## [2471] 375 979 1216 3714 404 2480 1435 3567 93 3530 3221 1780 2234
## [2484] 2754 1860 831 428 1900 3784 3204 3911 2782 282 2915 3185 343
## [2497] 2698 2802 2560 3163 1119 1585 695 3449 0 1593 1718 1426 2033
## [2510] 3238 2015 2005 2990 1137 3844 2317 1254 3810 2388 2163 3412 3564
## [2523] 2274 1742 1166 2916 246 2132 3785 2700 1682 933 3857 1471 3290
## [2536] 1982 365 2309 740 26 2900 3373 1451 4006 684 4068 1257 3
## [2549] 2195 841 2416 292 702 1054 3888 3217 2430 3953 4102 1678 1974
## [2562] 1256 2287 3458 1306 1879 2400 1453 2686 605 3533 1297 2515 63
## [2575] 1590 1894 995 1393 237 654 1978 112 3040 1357 808 1567 579
## [2588] 1845 126 3348 3072 4032 3903 3845 855 2758 2198 3073 3791 7
## [2601] 2563 858 3011 2694 4008 2993 3833 3500 407 4134 2418 3442 153
## [2614] 3655 1577 2404 2366 1009 2674 3737 280 3897 4127 3145 400 1607
## [2627] 1508 2529 1490 3298 1811 2021 506 2595 3080 1755 907 2052 3767
## [2640] 1683 552 1873 3973 1816 3389 3798 1136 949 3659 163 1671 1701
## [2653] 2397 2119 3183 4062 3142 1525 3415 449 2919 945 2850 3976 1543
## [2666] 1858 437 3652 1322 3462 790 877 743 2883 3308 3522 1011 1855
## [2679] 588 1314 3005 348 2484 1034 1170 2524 3296 3225 4067 1552 2716
## [2692] 575 651 597 2954 1258 1569 3413 3365 1316 470 3515 804 281
## [2705] 936 736 2779 2896 3015 3842 1150 1628 3403 3101 3773 2516 586
## [2718] 2208 160 1117 2814 1926 3182 863 956 3851 1339 1226 1222 2691
## [2731] 3625 608 164 2167 1046 3664 1990 2680 4031 3629 329 2023 2541
## [2744] 2559 4053 1439 3894 2154 813 433 1113 2103 864 274 971 2774
## [2757] 3747 3503 3752 3640 4136 3430 2152 3712 1355 2829 889 558 912
## [2770] 1390 2129 2575 511 2401 221 1305 146 3378 1788 1059 1833 3203
## [2783] 2867 510 323 1572 176 2868 1919 168 3436 1410 4113 1401 2270
## [2796] 2190 2250 136 1480 3169 1285 1984 898 197 2351 1494 1803 2096
## [2809] 771 2293 2708 2093 1118 4063 2866 3133 869 2402 1091 1457 199
## [2822] 429 3628 3483 2841 1834 1806 2940 1981 734 2983 297 2133 3482
## [2835] 811 2609 3668 1576 3013 1179 4010 4017 825 2726 1248 1574 4000
## [2848] 4120 625 3758 3727 1455 1729 1169 2273 1651 3099 4015 2617 2791
## [2861] 3799 2765 1028 1761 291 2034 1751 2295 3861 1012 3042 67 24
## [2874] 3470 216 2876 1921 2799 1159 3438 2172 166 4110 207 3527 3641
## [2887] 2335 2101 593 1183 1731 1874 527 2348 469 1359 2182 2827 1542
## [2900] 3317 932 1907 2737 1630 1479 1147 2950 1924 960 1499 25 1140
## [2913] 2210 929 3241 1563 2760 847 1656 2953 3550 2660 1026 2200 753
## [2926] 3349 3954 3872 3919 3068 2384 180 1279 2511 1444 3930 609 3107
## [2939] 1361 2357 1915 3358 1910 3315 94 3604 774 640 1312 2347 3678
## [2952] 2549 2505 1215 1405 1589 1205 275 2870 1158 1213 2092 1110 307
## [2965] 2834 2071 876 3132 86 2842 3125 2769 3147 3130 2846 1445 2624
## [2978] 209 3237 2803 1977 2656 1999 2184 2798 935 3663 2159 2438 130
## [2991] 1734 2083 3472 958 557 2431 1941 1547 2444 201 2981 2035 368
## [3004] 977 3643 1645 2448 3951 820 1965 1324 3780 1916 256 3695 3286
## [3017] 3184 1482 1710 2230 1616 1337 562 1888 373 2973 2783 2855 3018
## [3030] 3316 1695 1174 3936 2036 1211 2932 976 1161 2048 642 1077 3028
## [3043] 778 324 2264 84 4028 2696 2972 1600 2673 2459 1931 91 3171
## [3056] 430 2843 3208 3960 2352 2117 4027 4089 3131 1652 760 3577 3639
## [3069] 1967 4124 2189 806 1214 2946 3548 2739 151 2305 718 546 2761
## [3082] 3354 423 3236 663 801 759 2020 99 2509 1882 2058 2176 2909
## [3095] 539 1712 2913 2235 2148 2613 709 3485 1039 3281 1664 2621 2748fitdefaultRadial <- e1071::svm(urlDrugName~condition + effectiveness ,train_Data[which(train_Data$urlDrugName %in% c("biaxin","lamictal","depakene","sarafem")),])
fitdefaultRadial##
## Call:
## svm(formula = urlDrugName ~ condition + effectiveness, data = train_Data[which(train_Data$urlDrugName %in%
## c("biaxin", "lamictal", "depakene", "sarafem")), ])
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
## gamma: 0.02777778
##
## Number of Support Vectors: 46summary(fitdefaultRadial)##
## Call:
## svm(formula = urlDrugName ~ condition + effectiveness, data = train_Data[which(train_Data$urlDrugName %in%
## c("biaxin", "lamictal", "depakene", "sarafem")), ])
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
## gamma: 0.02777778
##
## Number of Support Vectors: 46
##
## ( 9 23 13 1 )
##
##
## Number of Classes: 4
##
## Levels:
## abilify accolate accupril accutane aciphex actiq actonel actos acyclovir aczone adcirca adderall adderall-xr adipex-p advair-diskus advair-hfa albuterol aldactone aldara alendronate alesse allegra allegra-d aloprim alprazolam ambien ambien-cr amerge amitiza amoxil amphetamine ampicillin anafranil androgel angeliq antivert aralen arava aricept arimidex armour-thyroid aromasin arthrotec asacol asmanex atacand atarax atenolol ativan atripla augmentin avage avapro avelox avita avodart axert aygestin azasan azelex azopt azor baciim baclofen bactrim-ds bactroban baraclude benadryl benicar benicar-hct benzaclin betamethasone betaseron biaxin bisoprolol boniva botox buprenorphine buspar byetta bystolic caduet carac cardura cardura-xl cataflam catapres ceclor cefzil celebrex celexa chantix cialis cipro citalopram clarinex claripel-cream clarithromycin claritin cleocin climara climara-pro clindamycin clindamycin-topical clobetasol clobex clomid clonazepam compazine concerta copaxone coreg corgard cosopt coumadin cozaar crestor cymbalta cyproheptadine cytomel danazol dapsone darvocet-n delestrogen deltasone depakene depakote depo-provera desonide desyrel detrol detrol-la dexamethasone dexedrine dextroamphetamine dextrostat diazepam diclofenac dicyclomine differin diflucan dilantin dilaudid diltiazem diovan dispermox ditropan divigel doryx dostinex dovonex doxepin doxycycline duac duragesic dysport ecotrin effexor effexor-xr efudex elavil elidel elmiron elocon emsam enablex enalapril enbrel erythra-derm erythromycin estrace estrasorb estratest estring estrostep-fe eulexin evista evoclin exelon femara femhrt femring fentanyl fexofenadine finacea fioricet fiorinal flagyl flexeril flomax flonase flovent fluconazole fluvoxamine follistim fortaz fosamax fosamax-plus-d fosinopril frova galzin geodon glucophage glucophage-xr gonal-f-rff grifulvin-v halcion haldol humira hydrochlorothiazide hydrocodone-and-acetaminophen hydrocortisone hyoscyamine hytrin hyzaar imitrex imodium imuran inderal innofem inspra isoniazid keflex kenalog keppra ketoconazole ketorolac klaron klonopin lac-hydrin lamictal lamisil lamotrigine lantus lasix latisse levaquin levetiracetam levitra levora levothroid levoxyl lexapro lidex lidocaine lipitor lithium-carbonate lo-ovral locoid-lipocream lodine lopressor lorazepam lortab lotrel lotronex lumigan lunesta lupron lybrel lyrica macrobid malarone maxalt medroxyprogesterone meperidine mercaptopurine meridia metformin metformin-extended-release methadone methimazole methotrexate metoclopramide metoprolol metrogel metrolotion metronidazole mevacor micardis minocin minocycline minoxidil miralax mirapex mirena mirtazapine mobic morphine motrin naltrexone naprosyn naproxen nardil nasacort nasacort-aq nasarel nasonex neoprofen neurontin nexium niacor niaspan nitrofurantoin nizoral nizoral-shampoo nolvadex noroxin norpramin nortriptyline norvasc nuvaring nuvigil nystatin omacor omnicef omnitrope oracea ortho-evra ortho-novum ortho-tri-cyclen ortho-tri-cyclen-lo oxazepam oxybutynin oxycodone oxycodone-and-acetaminophen oxycontin pamelor panixine-disperdose parlodel paxil paxil-cr penicillin-v penlac pentasa pepcid percocet periogard periostat permapen phendimetrazine phenergan pilocarpine plavix plendil polymyxin-b ponstel pravachol prednisolone prednisone premarin premarin-vaginal prempro prevacid prilosec prinivil pristiq proair-hfa prochlorperazine progesterone prograf proloprim prometrium propecia propranolol proquin-xr proscar protonix protopic provera provigil prozac quibron-t qvar ranitidine rebif reglan relafen relpax remeron remicade renova requip restasis restoril retin-a retin-a-micro rhinocort ribavirin rifadin risperdal risperdal-consta ritalin ritalin-la saizen sanctura-xr sarafem seasonale seasonique selegiline septra seroquel seroquel-xr serzone singulair skelaxin solodyn soltamox soma sonata sotret spiriva spironolactone strattera suboxone sular sulfasalazine sulindac sumatriptan symbicort symbyax synthroid tambocor tamiflu tapazole tarka tazorac tegretol tekturna temovate tenormin tequin tetracycline tirosint tobramycin tofranil-pm topamax topicort toprol-xl toradol tramadol trazodone trental tri-luma triamcinolone triaz trileptal trilipix trimethobenzamide trimethoprim triphasil tussionex tylenol ultracet ultram ultram-er vagifem valium valtrex vaniqa vasotec ventolin-hfa vesicare viagra vicodin vicoprofen vigamox vioxx vistaril vivelle vivelle-dot voltaren vytorin vyvanse warfarin wellbutrin wellbutrin-sr wellbutrin-xl xalatan xanax xanax-xr xenical xyrem xyzal yasmin zantac zegerid zestoretic ziac ziana zithromax zmax zocor zofran zoloft zomig zovirax zovirax-topical zyban zyprexa zyrtec zyrtec-d zyvoxfitdefaultpolynomial <- e1071::svm(urlDrugName~condition + effectiveness ,data= train_Data[1:5,] , kernel= "polynomial")
fitdefaultpolynomial##
## Call:
## svm(formula = urlDrugName ~ condition + effectiveness, data = train_Data[1:5,
## ], kernel = "polynomial")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: polynomial
## cost: 1
## degree: 3
## gamma: 0.1111111
## coef.0: 0
##
## Number of Support Vectors: 5summary(fitdefaultpolynomial)##
## Call:
## svm(formula = urlDrugName ~ condition + effectiveness, data = train_Data[1:5,
## ], kernel = "polynomial")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: polynomial
## cost: 1
## degree: 3
## gamma: 0.1111111
## coef.0: 0
##
## Number of Support Vectors: 5
##
## ( 1 1 1 1 1 )
##
##
## Number of Classes: 5
##
## Levels:
## abilify accolate accupril accutane aciphex actiq actonel actos acyclovir aczone adcirca adderall adderall-xr adipex-p advair-diskus advair-hfa albuterol aldactone aldara alendronate alesse allegra allegra-d aloprim alprazolam ambien ambien-cr amerge amitiza amoxil amphetamine ampicillin anafranil androgel angeliq antivert aralen arava aricept arimidex armour-thyroid aromasin arthrotec asacol asmanex atacand atarax atenolol ativan atripla augmentin avage avapro avelox avita avodart axert aygestin azasan azelex azopt azor baciim baclofen bactrim-ds bactroban baraclude benadryl benicar benicar-hct benzaclin betamethasone betaseron biaxin bisoprolol boniva botox buprenorphine buspar byetta bystolic caduet carac cardura cardura-xl cataflam catapres ceclor cefzil celebrex celexa chantix cialis cipro citalopram clarinex claripel-cream clarithromycin claritin cleocin climara climara-pro clindamycin clindamycin-topical clobetasol clobex clomid clonazepam compazine concerta copaxone coreg corgard cosopt coumadin cozaar crestor cymbalta cyproheptadine cytomel danazol dapsone darvocet-n delestrogen deltasone depakene depakote depo-provera desonide desyrel detrol detrol-la dexamethasone dexedrine dextroamphetamine dextrostat diazepam diclofenac dicyclomine differin diflucan dilantin dilaudid diltiazem diovan dispermox ditropan divigel doryx dostinex dovonex doxepin doxycycline duac duragesic dysport ecotrin effexor effexor-xr efudex elavil elidel elmiron elocon emsam enablex enalapril enbrel erythra-derm erythromycin estrace estrasorb estratest estring estrostep-fe eulexin evista evoclin exelon femara femhrt femring fentanyl fexofenadine finacea fioricet fiorinal flagyl flexeril flomax flonase flovent fluconazole fluvoxamine follistim fortaz fosamax fosamax-plus-d fosinopril frova galzin geodon glucophage glucophage-xr gonal-f-rff grifulvin-v halcion haldol humira hydrochlorothiazide hydrocodone-and-acetaminophen hydrocortisone hyoscyamine hytrin hyzaar imitrex imodium imuran inderal innofem inspra isoniazid keflex kenalog keppra ketoconazole ketorolac klaron klonopin lac-hydrin lamictal lamisil lamotrigine lantus lasix latisse levaquin levetiracetam levitra levora levothroid levoxyl lexapro lidex lidocaine lipitor lithium-carbonate lo-ovral locoid-lipocream lodine lopressor lorazepam lortab lotrel lotronex lumigan lunesta lupron lybrel lyrica macrobid malarone maxalt medroxyprogesterone meperidine mercaptopurine meridia metformin metformin-extended-release methadone methimazole methotrexate metoclopramide metoprolol metrogel metrolotion metronidazole mevacor micardis minocin minocycline minoxidil miralax mirapex mirena mirtazapine mobic morphine motrin naltrexone naprosyn naproxen nardil nasacort nasacort-aq nasarel nasonex neoprofen neurontin nexium niacor niaspan nitrofurantoin nizoral nizoral-shampoo nolvadex noroxin norpramin nortriptyline norvasc nuvaring nuvigil nystatin omacor omnicef omnitrope oracea ortho-evra ortho-novum ortho-tri-cyclen ortho-tri-cyclen-lo oxazepam oxybutynin oxycodone oxycodone-and-acetaminophen oxycontin pamelor panixine-disperdose parlodel paxil paxil-cr penicillin-v penlac pentasa pepcid percocet periogard periostat permapen phendimetrazine phenergan pilocarpine plavix plendil polymyxin-b ponstel pravachol prednisolone prednisone premarin premarin-vaginal prempro prevacid prilosec prinivil pristiq proair-hfa prochlorperazine progesterone prograf proloprim prometrium propecia propranolol proquin-xr proscar protonix protopic provera provigil prozac quibron-t qvar ranitidine rebif reglan relafen relpax remeron remicade renova requip restasis restoril retin-a retin-a-micro rhinocort ribavirin rifadin risperdal risperdal-consta ritalin ritalin-la saizen sanctura-xr sarafem seasonale seasonique selegiline septra seroquel seroquel-xr serzone singulair skelaxin solodyn soltamox soma sonata sotret spiriva spironolactone strattera suboxone sular sulfasalazine sulindac sumatriptan symbicort symbyax synthroid tambocor tamiflu tapazole tarka tazorac tegretol tekturna temovate tenormin tequin tetracycline tirosint tobramycin tofranil-pm topamax topicort toprol-xl toradol tramadol trazodone trental tri-luma triamcinolone triaz trileptal trilipix trimethobenzamide trimethoprim triphasil tussionex tylenol ultracet ultram ultram-er vagifem valium valtrex vaniqa vasotec ventolin-hfa vesicare viagra vicodin vicoprofen vigamox vioxx vistaril vivelle vivelle-dot voltaren vytorin vyvanse warfarin wellbutrin wellbutrin-sr wellbutrin-xl xalatan xanax xanax-xr xenical xyrem xyzal yasmin zantac zegerid zestoretic ziac ziana zithromax zmax zocor zofran zoloft zomig zovirax zovirax-topical zyban zyprexa zyrtec zyrtec-d zyvoxfitdefaultsigmoid <- e1071::svm(urlDrugName~condition + effectiveness ,data= train_Data[1:5,] , kernel= "sigmoid")
fitdefaultsigmoid##
## Call:
## svm(formula = urlDrugName ~ condition + effectiveness, data = train_Data[1:5,
## ], kernel = "sigmoid")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: sigmoid
## cost: 1
## gamma: 0.1111111
## coef.0: 0
##
## Number of Support Vectors: 5summary(fitdefaultsigmoid)##
## Call:
## svm(formula = urlDrugName ~ condition + effectiveness, data = train_Data[1:5,
## ], kernel = "sigmoid")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: sigmoid
## cost: 1
## gamma: 0.1111111
## coef.0: 0
##
## Number of Support Vectors: 5
##
## ( 1 1 1 1 1 )
##
##
## Number of Classes: 5
##
## Levels:
## abilify accolate accupril accutane aciphex actiq actonel actos acyclovir aczone adcirca adderall adderall-xr adipex-p advair-diskus advair-hfa albuterol aldactone aldara alendronate alesse allegra allegra-d aloprim alprazolam ambien ambien-cr amerge amitiza amoxil amphetamine ampicillin anafranil androgel angeliq antivert aralen arava aricept arimidex armour-thyroid aromasin arthrotec asacol asmanex atacand atarax atenolol ativan atripla augmentin avage avapro avelox avita avodart axert aygestin azasan azelex azopt azor baciim baclofen bactrim-ds bactroban baraclude benadryl benicar benicar-hct benzaclin betamethasone betaseron biaxin bisoprolol boniva botox buprenorphine buspar byetta bystolic caduet carac cardura cardura-xl cataflam catapres ceclor cefzil celebrex celexa chantix cialis cipro citalopram clarinex claripel-cream clarithromycin claritin cleocin climara climara-pro clindamycin clindamycin-topical clobetasol clobex clomid clonazepam compazine concerta copaxone coreg corgard cosopt coumadin cozaar crestor cymbalta cyproheptadine cytomel danazol dapsone darvocet-n delestrogen deltasone depakene depakote depo-provera desonide desyrel detrol detrol-la dexamethasone dexedrine dextroamphetamine dextrostat diazepam diclofenac dicyclomine differin diflucan dilantin dilaudid diltiazem diovan dispermox ditropan divigel doryx dostinex dovonex doxepin doxycycline duac duragesic dysport ecotrin effexor effexor-xr efudex elavil elidel elmiron elocon emsam enablex enalapril enbrel erythra-derm erythromycin estrace estrasorb estratest estring estrostep-fe eulexin evista evoclin exelon femara femhrt femring fentanyl fexofenadine finacea fioricet fiorinal flagyl flexeril flomax flonase flovent fluconazole fluvoxamine follistim fortaz fosamax fosamax-plus-d fosinopril frova galzin geodon glucophage glucophage-xr gonal-f-rff grifulvin-v halcion haldol humira hydrochlorothiazide hydrocodone-and-acetaminophen hydrocortisone hyoscyamine hytrin hyzaar imitrex imodium imuran inderal innofem inspra isoniazid keflex kenalog keppra ketoconazole ketorolac klaron klonopin lac-hydrin lamictal lamisil lamotrigine lantus lasix latisse levaquin levetiracetam levitra levora levothroid levoxyl lexapro lidex lidocaine lipitor lithium-carbonate lo-ovral locoid-lipocream lodine lopressor lorazepam lortab lotrel lotronex lumigan lunesta lupron lybrel lyrica macrobid malarone maxalt medroxyprogesterone meperidine mercaptopurine meridia metformin metformin-extended-release methadone methimazole methotrexate metoclopramide metoprolol metrogel metrolotion metronidazole mevacor micardis minocin minocycline minoxidil miralax mirapex mirena mirtazapine mobic morphine motrin naltrexone naprosyn naproxen nardil nasacort nasacort-aq nasarel nasonex neoprofen neurontin nexium niacor niaspan nitrofurantoin nizoral nizoral-shampoo nolvadex noroxin norpramin nortriptyline norvasc nuvaring nuvigil nystatin omacor omnicef omnitrope oracea ortho-evra ortho-novum ortho-tri-cyclen ortho-tri-cyclen-lo oxazepam oxybutynin oxycodone oxycodone-and-acetaminophen oxycontin pamelor panixine-disperdose parlodel paxil paxil-cr penicillin-v penlac pentasa pepcid percocet periogard periostat permapen phendimetrazine phenergan pilocarpine plavix plendil polymyxin-b ponstel pravachol prednisolone prednisone premarin premarin-vaginal prempro prevacid prilosec prinivil pristiq proair-hfa prochlorperazine progesterone prograf proloprim prometrium propecia propranolol proquin-xr proscar protonix protopic provera provigil prozac quibron-t qvar ranitidine rebif reglan relafen relpax remeron remicade renova requip restasis restoril retin-a retin-a-micro rhinocort ribavirin rifadin risperdal risperdal-consta ritalin ritalin-la saizen sanctura-xr sarafem seasonale seasonique selegiline septra seroquel seroquel-xr serzone singulair skelaxin solodyn soltamox soma sonata sotret spiriva spironolactone strattera suboxone sular sulfasalazine sulindac sumatriptan symbicort symbyax synthroid tambocor tamiflu tapazole tarka tazorac tegretol tekturna temovate tenormin tequin tetracycline tirosint tobramycin tofranil-pm topamax topicort toprol-xl toradol tramadol trazodone trental tri-luma triamcinolone triaz trileptal trilipix trimethobenzamide trimethoprim triphasil tussionex tylenol ultracet ultram ultram-er vagifem valium valtrex vaniqa vasotec ventolin-hfa vesicare viagra vicodin vicoprofen vigamox vioxx vistaril vivelle vivelle-dot voltaren vytorin vyvanse warfarin wellbutrin wellbutrin-sr wellbutrin-xl xalatan xanax xanax-xr xenical xyrem xyzal yasmin zantac zegerid zestoretic ziac ziana zithromax zmax zocor zofran zoloft zomig zovirax zovirax-topical zyban zyprexa zyrtec zyrtec-d zyvox#----------------------------------------------------------------
# Predict
predictdefaultRad <- predict(fitdefaultRadial,train_Data[which(train_Data$urlDrugName %in% c("biaxin","lamictal","depakene","sarafem")),])
predictdefaultPloy <- predict(fitdefaultpolynomial,train_Data[1:5,])
predictdefaultSig <- predict(fitdefaultsigmoid,train_Data[1:5,])
CrossTable(predictdefaultRad,train_Data[which(train_Data$urlDrugName %in% c("biaxin","lamictal","depakene","sarafem")),]$urlDrugName,prop.chisq = TRUE, prop.t = FALSE, dnn = c("Predicted","Actual"))##
##
## Cell Contents
## |-------------------------|
## | N |
## |-------------------------|
##
##
## Total Observations in Table: 52
##
##
## | train_Data[which(train_Data$urlDrugName %in% c("biaxin", "lamictal", "depakene", "sarafem")), ]$urlDrugName
## predictdefaultRad | biaxin | depakene | lamictal | sarafem | Row Total |
## ------------------|-----------|-----------|-----------|-----------|-----------|
## lamictal | 13 | 1 | 29 | 9 | 52 |
## ------------------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 13 | 1 | 29 | 9 | 52 |
## ------------------|-----------|-----------|-----------|-----------|-----------|
##
## Preare for Naive Bayes
TM Function
(funReplace <- content_transformer(function(x, pattern, rep="") str_replace(x,pattern,rep) ))## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x0000000012887d28>
## attr(,"class")
## [1] "content_transformer" "function"# (getPattern <- content_transformer( function(x,pattern)( str_extract_all(x, '<(.+?)>.+?</\\1>'))))
(getPattern <- content_transformer( function(x,pattern)( str_extract_all(x, pattern))))## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x000000000f77a8a8>
## attr(,"class")
## [1] "content_transformer" "function"# EMail
regEmail <-"(?<=(From|Delivered-To:|To:){1} )(<?[[:alnum:]]+@[[:alnum:].?-?]+>?)"
(getEmail<- content_transformer(function(x,pattern)(str_extract_all(x,regEmail)))) ## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x000000000fab04b8>
## attr(,"class")
## [1] "content_transformer" "function" # IP Address
regIP <- "\\b\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\b"
(getIP<- content_transformer(function(x,pattern)(str_extract_all(x,regIP)))) ## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x00000000151757b0>
## attr(,"class")
## [1] "content_transformer" "function" # Email Type
# regEmailType <- "(?<=(Content-Type:){1} )([[:graph:]]+)"
regEmailType <- "(?<=(Content-Type:){1} )([[:alpha:]]+/[[:alpha:]]+)"
(getEmailType <- content_transformer(function(x,pattern)(str_extract_all(x,regEmailType)))) ## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x00000000152fa128>
## attr(,"class")
## [1] "content_transformer" "function" #Subject
# Subject
regSub <- "(?<=(Subject:){1} )([[:graph:] [:graph:]]+)"
(getSubject <- content_transformer(function(x,pattern)(str_extract_all(x,regSub))))## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x00000000153cab38>
## attr(,"class")
## [1] "content_transformer" "function"(getBody<- content_transformer(function(x)(x[(str_which(x,regSub)):length(x)])))## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x000000001542f800>
## attr(,"class")
## [1] "content_transformer" "function"(getBodyLen <- content_transformer(function(x)( str_count(x))))## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x0000000015495b50>
## attr(,"class")
## [1] "content_transformer" "function"# Remove HTML TAG from
(cleanHTML <- content_transformer (function(x) {
return(gsub("<.*?>", "", x))
}))## function (x, ...)
## {
## content(x) <- FUN(content(x), ...)
## x
## }
## <bytecode: 0x0000000012888508>
## <environment: 0x0000000015512448>
## attr(,"class")
## [1] "content_transformer" "function"Create Test/Train Data
Using Term matrix and naiveBayes algorithm
#-------------------------------------------
#--------------------------------TEST With 4 drug
#-------------------------------------------
# Creat Test and Train data
train <- train_Data[which(train_Data$urlDrugName %in% c("biaxin","lamictal","depakene","sarafem")),]
test <- test_Data[which(test_Data$urlDrugName %in% c("biaxin","lamictal","depakene","sarafem")),]
# Chack Data
head(train)head(test)train$urlDrugName <- train$urlDrugName
test$urlDrugName <- test$urlDrugName
# prop.table(table(train$urlDrugName))
forcats::fct_infreq(train$urlDrugName, ordered = TRUE)## [1] sarafem lamictal lamictal biaxin lamictal biaxin lamictal
## [8] biaxin biaxin biaxin lamictal lamictal biaxin sarafem
## [15] sarafem biaxin lamictal sarafem lamictal lamictal lamictal
## [22] lamictal lamictal lamictal lamictal biaxin lamictal biaxin
## [29] lamictal sarafem lamictal sarafem biaxin biaxin depakene
## [36] lamictal lamictal lamictal lamictal sarafem biaxin lamictal
## [43] biaxin lamictal lamictal lamictal lamictal lamictal lamictal
## [50] lamictal sarafem sarafem
## 502 Levels: lamictal < biaxin < sarafem < depakene < ... < zyvoxhead(forcats::fct_count(train$urlDrugName, sort = TRUE))(forcats::fct_count(test$urlDrugName, sort = TRUE))# reate Datframe good for TERM MATRIX
## TRAIN...........................................
tm_dt_comment <- data.frame(doc_id = train$urlDrugName,text = train$commentsReview)
tm_dt_condition <- data.frame(doc_id = train$urlDrugName,text = train$condition)
## Test...........................................
tm_dte_comment <- data.frame(doc_id = test$urlDrugName,text = test$commentsReview)
tm_dte_condition <- data.frame(doc_id = test$urlDrugName,text = test$condition)
## ..........................................
#Create VCorpus
## TRAIN...........................................
corpus_commentsReview <- VCorpus(DataframeSource(tm_dt_comment))
# PCorpus(DataframeSource(tm_dt_comment),dbControl=list(useDb = TRUE,dbName = "texts.db",dbType = "DB1"))
corpus_condition <-VCorpus(DataframeSource(tm_dt_condition))
## TRAIN...........................................
corpus_commentsReview_test <- VCorpus(DataframeSource(tm_dte_comment))
corpus_condition_test <-VCorpus(DataframeSource(tm_dte_condition))
## ..........................................
#Cheack Corpus
print(corpus_commentsReview)## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 52summary(corpus_commentsReview)## Length Class Mode
## sarafem 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## depakene 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## sarafem 2 PlainTextDocument listprint(corpus_condition)## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 52summary(corpus_condition)## Length Class Mode
## sarafem 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## depakene 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## biaxin 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## lamictal 2 PlainTextDocument list
## sarafem 2 PlainTextDocument list
## sarafem 2 PlainTextDocument listmeta(corpus_condition[[1]])## author : character(0)
## datetimestamp: 2019-05-12 03:47:19
## description : character(0)
## heading : character(0)
## id : sarafem
## language : en
## origin : character(0)# Inspect cosrpus
inspect(corpus_commentsReview[2][[1]])## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 526
##
## My doctor started me on a minimal dose (25 mg) of Lamotrigine, and this will be prior to starting Interferon/Ribavarin treatment for the Hep-C I've got. (An antidepressant may be added in the near future as well.) Lamotrigine was prescribed to put me in a calmer state of mind, specifically because of the high anxiety effect I receive from antidepressants, which resulted in 2 visits to an emergency room - as well as a one month hospital stay due to extreme anxiety and paranoia.
## So far/so good is this layman's analysis.inspect(corpus_condition[1][[1]])## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 10
##
## depressioninspect(corpus_condition[1][[1]])## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 10
##
## depressioninspect(corpus_condition[2][[1]])## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 31
##
## bipolar disorder and depressionmeta(corpus_condition[[1]])## author : character(0)
## datetimestamp: 2019-05-12 03:47:19
## description : character(0)
## heading : character(0)
## id : sarafem
## language : en
## origin : character(0)meta(corpus_condition[[2]])## author : character(0)
## datetimestamp: 2019-05-12 03:47:19
## description : character(0)
## heading : character(0)
## id : lamictal
## language : en
## origin : character(0)# meta(corpus_condition[[1]], "comment", type = c("indexed", "corpus", "local")) <- "TEST "
# Clean Data
# corpus_condition_clean <- tm_map(corpus_condition,tolower)
# corpus_condition_clean <- tm_map(corpus_condition,removePunctuation)
# corpus_condition_clean <- tm_map(corpus_condition,removeNumbers)
# corpus_condition_clean <- tm_map(corpus_condition,stripWhitespace)
# corpus_condition_clean <- tm_map(corpus_condition,removeWords,stopwords())
# Create Document Term Matrix
## TRAIN...........................................
corpus_condition_clean_dtm <- DocumentTermMatrix(corpus_condition, control =
list(removePunctuation = TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
corpus_commentsReview_clean_dtm <- DocumentTermMatrix(corpus_commentsReview, control =
list(removePunctuation = TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
## Test...........................................
corpus_condition_test_dtm <- DocumentTermMatrix(corpus_condition_test, control =
list(removePunctuation = TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
corpus_commentsReview_test_dtm <- DocumentTermMatrix(corpus_commentsReview_test, control =
list(removePunctuation = TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
## .....................................................................................
#condition Coprpus DTM
corpus_condition_clean_dtm## <<DocumentTermMatrix (documents: 52, terms: 40)>>
## Non-/sparse entries: 101/1979
## Sparsity : 95%
## Maximal term length: 14
## Weighting : term frequency (tf)#Comments Corpus DTM
corpus_commentsReview_clean_dtm## <<DocumentTermMatrix (documents: 52, terms: 634)>>
## Non-/sparse entries: 1092/31876
## Sparsity : 97%
## Maximal term length: 19
## Weighting : term frequency (tf)inspect(corpus_condition_clean_dtm[1:2,])## <<DocumentTermMatrix (documents: 2, terms: 40)>>
## Non-/sparse entries: 4/76
## Sparsity : 95%
## Maximal term length: 14
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs anxiety asthmatic behavior bipolar bipolarmixed bipoler
## lamictal 0 0 0 1 0 0
## sarafem 0 0 0 0 0 0
## Terms
## Docs bronchitis clonic depression disorder
## lamictal 0 0 1 1
## sarafem 0 0 1 0library(wordcloud)
# wordcloud
suppressWarnings(wordcloud(corpus_condition, min.freq=20, scale=c(5, .1), colors=brewer.pal(6, "Dark2")) ) 
# suppressWarnings(wordcloud(corpus_condition_test, min.freq=2, scale=c(3, .5), colors=brewer.pal(6, "Dark2")) )
suppressWarnings(wordcloud(corpus_condition_test, min.freq=10,scale=c(5, .1), colors=brewer.pal(6, "Dark2")) ) 
# Organize terms by their frequency:
corpus_condition_clean_dtm_freq <- colSums(as.matrix(corpus_condition_clean_dtm))
length(corpus_condition_clean_dtm_freq) ## [1] 40# Find Frequent term
dim(corpus_condition_clean_dtm[])## [1] 52 40inspect(corpus_condition_clean_dtm[1,])## <<DocumentTermMatrix (documents: 1, terms: 40)>>
## Non-/sparse entries: 1/39
## Sparsity : 98%
## Maximal term length: 14
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs anxiety asthmatic behavior bipolar bipolarmixed bipoler
## sarafem 0 0 0 0 0 0
## Terms
## Docs bronchitis clonic depresion depression
## sarafem 0 0 0 1# We could create datapoint with most frequest terms used and then apply to the model for taining..
# In this case Iwould be usingi n my data as it is . as its not a big data set .
# but to avoid missing vlaue in test dat aset wwe would create a vector of most frequest words and drop every thing that is not in the vacotr. # from the test data for testin purpose to avoaid error at test time.
corpus_condition_clean_dtm_freq_term <- findMostFreqTerms(corpus_condition_clean_dtm,20)
length(corpus_condition_clean_dtm_freq_term[])## [1] 52 corpus_condition_clean_dtm_freq_term[1:3]## $sarafem
## depression
## 1
##
## $lamictal
## bipolar depression disorder
## 1 1 1
##
## $lamictal
## disorder seizure
## 1 1 # Organize terms by their frequency:
corpus_condition_clean_dtm_freq <- colSums(as.matrix(corpus_condition_clean_dtm))
length(corpus_condition_clean_dtm_freq) ## [1] 40corpus_condition_test_dtm_freq <- colSums(as.matrix(corpus_condition_test_dtm))
length(corpus_condition_test_dtm_freq) ## [1] 19# $ Matrix to data frame
reshape2::melt(sort(corpus_condition_clean_dtm_freq,decreasing = TRUE)[1:20])data.frame(word=names(corpus_condition_clean_dtm_freq), freq=corpus_condition_clean_dtm_freq) dim(corpus_condition_clean_dtm)## [1] 52 40# Find terms with frequecy at least 5
freq_words <- findFreqTerms(corpus_condition_clean_dtm,5)
Mongo_freq_words <- findFreqTerms(corpus_condition_clean_dtm,1)
freq_words## [1] "bipolar" "depression" "disorder" "infection" "sinus" # corpus_condition_test_dtm_freqword <-
# https://www.youtube.com/watch?v=sujx3MjEH_0
corpus_condition_clean_dtm_freqword <- corpus_condition_clean_dtm[,freq_words]
corpus_condition_test_dtm_freqword <- corpus_condition_test_dtm[,freq_words]
#Build Trainng data set for Mongo
Mongo_corpus_condition_clean_dtm_freqword <- corpus_condition_clean_dtm[,Mongo_freq_words]
options(error=recover)
# corpus_condition_test_dtm_Testready <- corpus_condition_test[[1]]
freq_y_n <- function(x){
y <- ifelse(x>0,1,0)
y <- factor(y, level=c(0,1),labels = c("No","Yes"))
y
}
new_train <- apply(corpus_condition_clean_dtm_freqword,2,freq_y_n)
new_test <- apply(corpus_condition_test_dtm_freqword,2,freq_y_n)
# Converting Train Data for trainig into Yes and No
Mongo_Train_For_Model <- apply(Mongo_corpus_condition_clean_dtm_freqword,2,freq_y_n)
new_test2 <- apply(corpus_condition_test_dtm_freqword,2,freq_y_n)
corpus_condition_test_dtm_freqword[1:4,]## <<DocumentTermMatrix (documents: 4, terms: 5)>>
## Non-/sparse entries: 7/13
## Sparsity : 65%
## Maximal term length: 10
## Weighting : term frequency (tf)Apply Naive Bayes
#-- Start usng librarat e1071
train_classifier <- naiveBayes(new_train,train$urlDrugName)
# Will save Model Mong_train_classifier to Mongo
Mong_train_classifier <- naiveBayes(Mongo_Train_For_Model,train$urlDrugName)
class(train_classifier)## [1] "naiveBayes" # Test
test_predict <- predict(train_classifier, new_test)
table (test_predict,test$urlDrugName)##
## test_predict biaxin depakene lamictal sarafem
## abilify 0 0 0 0
## accolate 0 0 0 0
## accupril 0 0 0 0
## accutane 0 0 0 0
## aciphex 0 0 0 0
## actiq 0 0 0 0
## actonel 0 0 0 0
## actos 0 0 0 0
## acyclovir 0 0 0 0
## aczone 0 0 0 0
## adcirca 0 0 0 0
## adderall 0 0 0 0
## adderall-xr 0 0 0 0
## adipex-p 0 0 0 0
## advair-diskus 0 0 0 0
## advair-hfa 0 0 0 0
## albuterol 0 0 0 0
## aldactone 0 0 0 0
## aldara 0 0 0 0
## alendronate 0 0 0 0
## alesse 0 0 0 0
## allegra 0 0 0 0
## allegra-d 0 0 0 0
## aloprim 0 0 0 0
## alprazolam 0 0 0 0
## ambien 0 0 0 0
## ambien-cr 0 0 0 0
## amerge 0 0 0 0
## amitiza 0 0 0 0
## amoxil 0 0 0 0
## amphetamine 0 0 0 0
## ampicillin 0 0 0 0
## anafranil 0 0 0 0
## androgel 0 0 0 0
## angeliq 0 0 0 0
## antivert 0 0 0 0
## aralen 0 0 0 0
## arava 0 0 0 0
## aricept 0 0 0 0
## arimidex 0 0 0 0
## armour-thyroid 0 0 0 0
## aromasin 0 0 0 0
## arthrotec 0 0 0 0
## asacol 0 0 0 0
## asmanex 0 0 0 0
## atacand 0 0 0 0
## atarax 0 0 0 0
## atenolol 0 0 0 0
## ativan 0 0 0 0
## atripla 0 0 0 0
## augmentin 0 0 0 0
## avage 0 0 0 0
## avapro 0 0 0 0
## avelox 0 0 0 0
## avita 0 0 0 0
## avodart 0 0 0 0
## axert 0 0 0 0
## aygestin 0 0 0 0
## azasan 0 0 0 0
## azelex 0 0 0 0
## azopt 0 0 0 0
## azor 0 0 0 0
## baciim 0 0 0 0
## baclofen 0 0 0 0
## bactrim-ds 0 0 0 0
## bactroban 0 0 0 0
## baraclude 0 0 0 0
## benadryl 0 0 0 0
## benicar 0 0 0 0
## benicar-hct 0 0 0 0
## benzaclin 0 0 0 0
## betamethasone 0 0 0 0
## betaseron 0 0 0 0
## biaxin 5 0 0 0
## bisoprolol 0 0 0 0
## boniva 0 0 0 0
## botox 0 0 0 0
## buprenorphine 0 0 0 0
## buspar 0 0 0 0
## byetta 0 0 0 0
## bystolic 0 0 0 0
## caduet 0 0 0 0
## carac 0 0 0 0
## cardura 0 0 0 0
## cardura-xl 0 0 0 0
## cataflam 0 0 0 0
## catapres 0 0 0 0
## ceclor 0 0 0 0
## cefzil 0 0 0 0
## celebrex 0 0 0 0
## celexa 0 0 0 0
## chantix 0 0 0 0
## cialis 0 0 0 0
## cipro 0 0 0 0
## citalopram 0 0 0 0
## clarinex 0 0 0 0
## claripel-cream 0 0 0 0
## clarithromycin 0 0 0 0
## claritin 0 0 0 0
## cleocin 0 0 0 0
## climara 0 0 0 0
## climara-pro 0 0 0 0
## clindamycin 0 0 0 0
## clindamycin-topical 0 0 0 0
## clobetasol 0 0 0 0
## clobex 0 0 0 0
## clomid 0 0 0 0
## clonazepam 0 0 0 0
## compazine 0 0 0 0
## concerta 0 0 0 0
## copaxone 0 0 0 0
## coreg 0 0 0 0
## corgard 0 0 0 0
## cosopt 0 0 0 0
## coumadin 0 0 0 0
## cozaar 0 0 0 0
## crestor 0 0 0 0
## cymbalta 0 0 0 0
## cyproheptadine 0 0 0 0
## cytomel 0 0 0 0
## danazol 0 0 0 0
## dapsone 0 0 0 0
## darvocet-n 0 0 0 0
## delestrogen 0 0 0 0
## deltasone 0 0 0 0
## depakene 0 0 0 0
## depakote 0 0 0 0
## depo-provera 0 0 0 0
## desonide 0 0 0 0
## desyrel 0 0 0 0
## detrol 0 0 0 0
## detrol-la 0 0 0 0
## dexamethasone 0 0 0 0
## dexedrine 0 0 0 0
## dextroamphetamine 0 0 0 0
## dextrostat 0 0 0 0
## diazepam 0 0 0 0
## diclofenac 0 0 0 0
## dicyclomine 0 0 0 0
## differin 0 0 0 0
## diflucan 0 0 0 0
## dilantin 0 0 0 0
## dilaudid 0 0 0 0
## diltiazem 0 0 0 0
## diovan 0 0 0 0
## dispermox 0 0 0 0
## ditropan 0 0 0 0
## divigel 0 0 0 0
## doryx 0 0 0 0
## dostinex 0 0 0 0
## dovonex 0 0 0 0
## doxepin 0 0 0 0
## doxycycline 0 0 0 0
## duac 0 0 0 0
## duragesic 0 0 0 0
## dysport 0 0 0 0
## ecotrin 0 0 0 0
## effexor 0 0 0 0
## effexor-xr 0 0 0 0
## efudex 0 0 0 0
## elavil 0 0 0 0
## elidel 0 0 0 0
## elmiron 0 0 0 0
## elocon 0 0 0 0
## emsam 0 0 0 0
## enablex 0 0 0 0
## enalapril 0 0 0 0
## enbrel 0 0 0 0
## erythra-derm 0 0 0 0
## erythromycin 0 0 0 0
## estrace 0 0 0 0
## estrasorb 0 0 0 0
## estratest 0 0 0 0
## estring 0 0 0 0
## estrostep-fe 0 0 0 0
## eulexin 0 0 0 0
## evista 0 0 0 0
## evoclin 0 0 0 0
## exelon 0 0 0 0
## femara 0 0 0 0
## femhrt 0 0 0 0
## femring 0 0 0 0
## fentanyl 0 0 0 0
## fexofenadine 0 0 0 0
## finacea 0 0 0 0
## fioricet 0 0 0 0
## fiorinal 0 0 0 0
## flagyl 0 0 0 0
## flexeril 0 0 0 0
## flomax 0 0 0 0
## flonase 0 0 0 0
## flovent 0 0 0 0
## fluconazole 0 0 0 0
## fluvoxamine 0 0 0 0
## follistim 0 0 0 0
## fortaz 0 0 0 0
## fosamax 0 0 0 0
## fosamax-plus-d 0 0 0 0
## fosinopril 0 0 0 0
## frova 0 0 0 0
## galzin 0 0 0 0
## geodon 0 0 0 0
## glucophage 0 0 0 0
## glucophage-xr 0 0 0 0
## gonal-f-rff 0 0 0 0
## grifulvin-v 0 0 0 0
## halcion 0 0 0 0
## haldol 0 0 0 0
## humira 0 0 0 0
## hydrochlorothiazide 0 0 0 0
## hydrocodone-and-acetaminophen 0 0 0 0
## hydrocortisone 0 0 0 0
## hyoscyamine 0 0 0 0
## hytrin 0 0 0 0
## hyzaar 0 0 0 0
## imitrex 0 0 0 0
## imodium 0 0 0 0
## imuran 0 0 0 0
## inderal 0 0 0 0
## innofem 0 0 0 0
## inspra 0 0 0 0
## isoniazid 0 0 0 0
## keflex 0 0 0 0
## kenalog 0 0 0 0
## keppra 0 0 0 0
## ketoconazole 0 0 0 0
## ketorolac 0 0 0 0
## klaron 0 0 0 0
## klonopin 0 0 0 0
## lac-hydrin 0 0 0 0
## lamictal 3 1 10 3
## lamisil 0 0 0 0
## lamotrigine 0 0 0 0
## lantus 0 0 0 0
## lasix 0 0 0 0
## latisse 0 0 0 0
## levaquin 0 0 0 0
## levetiracetam 0 0 0 0
## levitra 0 0 0 0
## levora 0 0 0 0
## levothroid 0 0 0 0
## levoxyl 0 0 0 0
## lexapro 0 0 0 0
## lidex 0 0 0 0
## lidocaine 0 0 0 0
## lipitor 0 0 0 0
## lithium-carbonate 0 0 0 0
## lo-ovral 0 0 0 0
## locoid-lipocream 0 0 0 0
## lodine 0 0 0 0
## lopressor 0 0 0 0
## lorazepam 0 0 0 0
## lortab 0 0 0 0
## lotrel 0 0 0 0
## lotronex 0 0 0 0
## lumigan 0 0 0 0
## lunesta 0 0 0 0
## lupron 0 0 0 0
## lybrel 0 0 0 0
## lyrica 0 0 0 0
## macrobid 0 0 0 0
## malarone 0 0 0 0
## maxalt 0 0 0 0
## medroxyprogesterone 0 0 0 0
## meperidine 0 0 0 0
## mercaptopurine 0 0 0 0
## meridia 0 0 0 0
## metformin 0 0 0 0
## metformin-extended-release 0 0 0 0
## methadone 0 0 0 0
## methimazole 0 0 0 0
## methotrexate 0 0 0 0
## metoclopramide 0 0 0 0
## metoprolol 0 0 0 0
## metrogel 0 0 0 0
## metrolotion 0 0 0 0
## metronidazole 0 0 0 0
## mevacor 0 0 0 0
## micardis 0 0 0 0
## minocin 0 0 0 0
## minocycline 0 0 0 0
## minoxidil 0 0 0 0
## miralax 0 0 0 0
## mirapex 0 0 0 0
## mirena 0 0 0 0
## mirtazapine 0 0 0 0
## mobic 0 0 0 0
## morphine 0 0 0 0
## motrin 0 0 0 0
## naltrexone 0 0 0 0
## naprosyn 0 0 0 0
## naproxen 0 0 0 0
## nardil 0 0 0 0
## nasacort 0 0 0 0
## nasacort-aq 0 0 0 0
## nasarel 0 0 0 0
## nasonex 0 0 0 0
## neoprofen 0 0 0 0
## neurontin 0 0 0 0
## nexium 0 0 0 0
## niacor 0 0 0 0
## niaspan 0 0 0 0
## nitrofurantoin 0 0 0 0
## nizoral 0 0 0 0
## nizoral-shampoo 0 0 0 0
## nolvadex 0 0 0 0
## noroxin 0 0 0 0
## norpramin 0 0 0 0
## nortriptyline 0 0 0 0
## norvasc 0 0 0 0
## nuvaring 0 0 0 0
## nuvigil 0 0 0 0
## nystatin 0 0 0 0
## omacor 0 0 0 0
## omnicef 0 0 0 0
## omnitrope 0 0 0 0
## oracea 0 0 0 0
## ortho-evra 0 0 0 0
## ortho-novum 0 0 0 0
## ortho-tri-cyclen 0 0 0 0
## ortho-tri-cyclen-lo 0 0 0 0
## oxazepam 0 0 0 0
## oxybutynin 0 0 0 0
## oxycodone 0 0 0 0
## oxycodone-and-acetaminophen 0 0 0 0
## oxycontin 0 0 0 0
## pamelor 0 0 0 0
## panixine-disperdose 0 0 0 0
## parlodel 0 0 0 0
## paxil 0 0 0 0
## paxil-cr 0 0 0 0
## penicillin-v 0 0 0 0
## penlac 0 0 0 0
## pentasa 0 0 0 0
## pepcid 0 0 0 0
## percocet 0 0 0 0
## periogard 0 0 0 0
## periostat 0 0 0 0
## permapen 0 0 0 0
## phendimetrazine 0 0 0 0
## phenergan 0 0 0 0
## pilocarpine 0 0 0 0
## plavix 0 0 0 0
## plendil 0 0 0 0
## polymyxin-b 0 0 0 0
## ponstel 0 0 0 0
## pravachol 0 0 0 0
## prednisolone 0 0 0 0
## prednisone 0 0 0 0
## premarin 0 0 0 0
## premarin-vaginal 0 0 0 0
## prempro 0 0 0 0
## prevacid 0 0 0 0
## prilosec 0 0 0 0
## prinivil 0 0 0 0
## pristiq 0 0 0 0
## proair-hfa 0 0 0 0
## prochlorperazine 0 0 0 0
## progesterone 0 0 0 0
## prograf 0 0 0 0
## proloprim 0 0 0 0
## prometrium 0 0 0 0
## propecia 0 0 0 0
## propranolol 0 0 0 0
## proquin-xr 0 0 0 0
## proscar 0 0 0 0
## protonix 0 0 0 0
## protopic 0 0 0 0
## provera 0 0 0 0
## provigil 0 0 0 0
## prozac 0 0 0 0
## quibron-t 0 0 0 0
## qvar 0 0 0 0
## ranitidine 0 0 0 0
## rebif 0 0 0 0
## reglan 0 0 0 0
## relafen 0 0 0 0
## relpax 0 0 0 0
## remeron 0 0 0 0
## remicade 0 0 0 0
## renova 0 0 0 0
## requip 0 0 0 0
## restasis 0 0 0 0
## restoril 0 0 0 0
## retin-a 0 0 0 0
## retin-a-micro 0 0 0 0
## rhinocort 0 0 0 0
## ribavirin 0 0 0 0
## rifadin 0 0 0 0
## risperdal 0 0 0 0
## risperdal-consta 0 0 0 0
## ritalin 0 0 0 0
## ritalin-la 0 0 0 0
## saizen 0 0 0 0
## sanctura-xr 0 0 0 0
## sarafem 0 0 0 1
## seasonale 0 0 0 0
## seasonique 0 0 0 0
## selegiline 0 0 0 0
## septra 0 0 0 0
## seroquel 0 0 0 0
## seroquel-xr 0 0 0 0
## serzone 0 0 0 0
## singulair 0 0 0 0
## skelaxin 0 0 0 0
## solodyn 0 0 0 0
## soltamox 0 0 0 0
## soma 0 0 0 0
## sonata 0 0 0 0
## sotret 0 0 0 0
## spiriva 0 0 0 0
## spironolactone 0 0 0 0
## strattera 0 0 0 0
## suboxone 0 0 0 0
## sular 0 0 0 0
## sulfasalazine 0 0 0 0
## sulindac 0 0 0 0
## sumatriptan 0 0 0 0
## symbicort 0 0 0 0
## symbyax 0 0 0 0
## synthroid 0 0 0 0
## tambocor 0 0 0 0
## tamiflu 0 0 0 0
## tapazole 0 0 0 0
## tarka 0 0 0 0
## tazorac 0 0 0 0
## tegretol 0 0 0 0
## tekturna 0 0 0 0
## temovate 0 0 0 0
## tenormin 0 0 0 0
## tequin 0 0 0 0
## tetracycline 0 0 0 0
## tirosint 0 0 0 0
## tobramycin 0 0 0 0
## tofranil-pm 0 0 0 0
## topamax 0 0 0 0
## topicort 0 0 0 0
## toprol-xl 0 0 0 0
## toradol 0 0 0 0
## tramadol 0 0 0 0
## trazodone 0 0 0 0
## trental 0 0 0 0
## tri-luma 0 0 0 0
## triamcinolone 0 0 0 0
## triaz 0 0 0 0
## trileptal 0 0 0 0
## trilipix 0 0 0 0
## trimethobenzamide 0 0 0 0
## trimethoprim 0 0 0 0
## triphasil 0 0 0 0
## tussionex 0 0 0 0
## tylenol 0 0 0 0
## ultracet 0 0 0 0
## ultram 0 0 0 0
## ultram-er 0 0 0 0
## vagifem 0 0 0 0
## valium 0 0 0 0
## valtrex 0 0 0 0
## vaniqa 0 0 0 0
## vasotec 0 0 0 0
## ventolin-hfa 0 0 0 0
## vesicare 0 0 0 0
## viagra 0 0 0 0
## vicodin 0 0 0 0
## vicoprofen 0 0 0 0
## vigamox 0 0 0 0
## vioxx 0 0 0 0
## vistaril 0 0 0 0
## vivelle 0 0 0 0
## vivelle-dot 0 0 0 0
## voltaren 0 0 0 0
## vytorin 0 0 0 0
## vyvanse 0 0 0 0
## warfarin 0 0 0 0
## wellbutrin 0 0 0 0
## wellbutrin-sr 0 0 0 0
## wellbutrin-xl 0 0 0 0
## xalatan 0 0 0 0
## xanax 0 0 0 0
## xanax-xr 0 0 0 0
## xenical 0 0 0 0
## xyrem 0 0 0 0
## xyzal 0 0 0 0
## yasmin 0 0 0 0
## zantac 0 0 0 0
## zegerid 0 0 0 0
## zestoretic 0 0 0 0
## ziac 0 0 0 0
## ziana 0 0 0 0
## zithromax 0 0 0 0
## zmax 0 0 0 0
## zocor 0 0 0 0
## zofran 0 0 0 0
## zoloft 0 0 0 0
## zomig 0 0 0 0
## zovirax 0 0 0 0
## zovirax-topical 0 0 0 0
## zyban 0 0 0 0
## zyprexa 0 0 0 0
## zyrtec 0 0 0 0
## zyrtec-d 0 0 0 0
## zyvox 0 0 0 0 forcats::fct_count(test$urlDrugName) forcats::fct_count(test_predict,sort = TRUE) library(gmodels)
CrossTable(test_predict,test$urlDrugName,prop.chisq = TRUE, prop.t = FALSE, dnn = c("Predicted","Actual"))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | Actual
## Predicted | biaxin | depakene | lamictal | sarafem | Row Total |
## -------------|-----------|-----------|-----------|-----------|-----------|
## biaxin | 5 | 0 | 0 | 0 | 5 |
## | 6.114 | 0.217 | 2.174 | 0.870 | |
## | 1.000 | 0.000 | 0.000 | 0.000 | 0.217 |
## | 0.625 | 0.000 | 0.000 | 0.000 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## lamictal | 3 | 1 | 10 | 3 | 17 |
## | 1.435 | 0.092 | 0.921 | 0.001 | |
## | 0.176 | 0.059 | 0.588 | 0.176 | 0.739 |
## | 0.375 | 1.000 | 1.000 | 0.750 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## sarafem | 0 | 0 | 0 | 1 | 1 |
## | 0.348 | 0.043 | 0.435 | 3.924 | |
## | 0.000 | 0.000 | 0.000 | 1.000 | 0.043 |
## | 0.000 | 0.000 | 0.000 | 0.250 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 8 | 1 | 10 | 4 | 23 |
## | 0.348 | 0.043 | 0.435 | 0.174 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
##
## User Input Function
#--------------------------------------------------------------------------------------
#----------------------------------User Input Functio ---------------------------------
#--------------------------------------------------------------------------------------
usr_ask <- name <- function(dtquestion ) {
# tm_dte_condition <- data.frame(doc_id = test$urlDrugName,text = test$condition)
# tm_dte_condition_User <- data.frame(doc_id = c("NoDRUG", "NoNEWDRUG",""),text =
# c("I am in depression",
# "I have sinus",
# "I have throt infection"))
tm_dte_condition_User <- data.frame(doc_id = c(rep("noDrug",1,length(dtquestion[,1]))),
text = dtquestion$text )
corpus_condition_test_user <-VCorpus(DataframeSource(tm_dte_condition_User))
# Use only 5 most frequent words (fivefreq) to build the DTM
# Find terms with frequecy at least 5
freq_words_user <- findFreqTerms(corpus_condition_clean_dtm,1)
freq_words_user
dtm.train.nb <- DocumentTermMatrix(corpus_condition, control=list(dictionary = freq_words,
removePunctuation = TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
dim(dtm.train.nb)
dtm.test.nb <- DocumentTermMatrix(corpus_condition_test_user, control=list(dictionary = freq_words,
removePunctuation = TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
dim(dtm.test.nb)
# Function to convert the word frequencies to yes (presence) and no (absence) labels
convert_count <- function(x) {
y <- ifelse(x > 0, 1,0)
y <- factor(y, levels=c(0,1), labels=c("No", "Yes"))
y
}
# Apply the convert_count function to get final training and testing DTMs
trainNB <- apply(dtm.train.nb, 2, convert_count)
testNB <- apply(dtm.test.nb, 2, convert_count)
nav_lap <- naiveBayes(trainNB, train$urlDrugName, laplace = 1)
red_lap <- predict(nav_lap,testNB)
fct_count(red_lap,sort = TRUE)
CrossTable(tm_dte_condition_User$text,red_lap,prop.chisq = FALSE, prop.t = FALSE, dnn = c("Problem","Drug"))
red_lap
}User Input test
#--------------------------------------------------------------------------------------
#----------------------------------User Input test ------------------------------------
#--------------------------------------------------------------------------------------
user_question <- data.frame(doc_id = c("NoDRUG", "NoNEWDRUG",""),text =
c("I am in depression",
"I have sinus",
"I have throt infection"))
user_question <- data.frame(doc_id = c("NoDRUG"),text =
c("I am in depression","sinus"
))
usr_ask(user_question)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 2
##
##
## | Drug
## Problem | biaxin | sarafem | Row Total |
## -------------------|-----------|-----------|-----------|
## I am in depression | 0 | 1 | 1 |
## | 0.000 | 1.000 | 0.500 |
## | 0.000 | 1.000 | |
## -------------------|-----------|-----------|-----------|
## sinus | 1 | 0 | 1 |
## | 1.000 | 0.000 | 0.500 |
## | 1.000 | 0.000 | |
## -------------------|-----------|-----------|-----------|
## Column Total | 1 | 1 | 2 |
## | 0.500 | 0.500 | |
## -------------------|-----------|-----------|-----------|
##
## ## [1] sarafem biaxin
## 502 Levels: abilify accolate accupril accutane aciphex actiq ... zyvox #----------Test REgualr
user_question2 <- test[,c(1,6)]
names(user_question2) <- c("doc_id","text")
res <- usr_ask(user_question2)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | Drug
## Problem | biaxin | lamictal | sarafem | Row Total |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bi-polar / anxiety | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar | 0 | 2 | 0 | 2 |
## | 0.000 | 1.000 | 0.000 | 0.087 |
## | 0.000 | 0.118 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar disorder | 0 | 3 | 0 | 3 |
## | 0.000 | 1.000 | 0.000 | 0.130 |
## | 0.000 | 0.176 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar disorder, ocd behaviors | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar ii | 0 | 3 | 0 | 3 |
## | 0.000 | 1.000 | 0.000 | 0.130 |
## | 0.000 | 0.176 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## borreliosis | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bronchitis | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## depression | 0 | 0 | 1 | 1 |
## | 0.000 | 0.000 | 1.000 | 0.043 |
## | 0.000 | 0.000 | 1.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## epilepsy | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## mood disorder | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## obsessive compulsive disorder | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## pmdd | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## sinus infection | 3 | 0 | 0 | 3 |
## | 1.000 | 0.000 | 0.000 | 0.130 |
## | 0.600 | 0.000 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## sinus infection, chest infection | 1 | 0 | 0 | 1 |
## | 1.000 | 0.000 | 0.000 | 0.043 |
## | 0.200 | 0.000 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## ulcer | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## upper respiratory infection | 1 | 0 | 0 | 1 |
## | 1.000 | 0.000 | 0.000 | 0.043 |
## | 0.200 | 0.000 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## Column Total | 5 | 17 | 1 | 23 |
## | 0.217 | 0.739 | 0.043 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
##
## CrossTable(res,test$urlDrugName,prop.chisq = TRUE, prop.t = FALSE, dnn = c("Predicted","Actual")) ##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | Actual
## Predicted | biaxin | depakene | lamictal | sarafem | Row Total |
## -------------|-----------|-----------|-----------|-----------|-----------|
## biaxin | 5 | 0 | 0 | 0 | 5 |
## | 6.114 | 0.217 | 2.174 | 0.870 | |
## | 1.000 | 0.000 | 0.000 | 0.000 | 0.217 |
## | 0.625 | 0.000 | 0.000 | 0.000 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## lamictal | 3 | 1 | 10 | 3 | 17 |
## | 1.435 | 0.092 | 0.921 | 0.001 | |
## | 0.176 | 0.059 | 0.588 | 0.176 | 0.739 |
## | 0.375 | 1.000 | 1.000 | 0.750 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## sarafem | 0 | 0 | 0 | 1 | 1 |
## | 0.348 | 0.043 | 0.435 | 3.924 | |
## | 0.000 | 0.000 | 0.000 | 1.000 | 0.043 |
## | 0.000 | 0.000 | 0.000 | 0.250 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 8 | 1 | 10 | 4 | 23 |
## | 0.348 | 0.043 | 0.435 | 0.174 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
##
## usr_ask(user_question2)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | Drug
## Problem | biaxin | lamictal | sarafem | Row Total |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bi-polar / anxiety | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar | 0 | 2 | 0 | 2 |
## | 0.000 | 1.000 | 0.000 | 0.087 |
## | 0.000 | 0.118 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar disorder | 0 | 3 | 0 | 3 |
## | 0.000 | 1.000 | 0.000 | 0.130 |
## | 0.000 | 0.176 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar disorder, ocd behaviors | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bipolar ii | 0 | 3 | 0 | 3 |
## | 0.000 | 1.000 | 0.000 | 0.130 |
## | 0.000 | 0.176 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## borreliosis | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## bronchitis | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## depression | 0 | 0 | 1 | 1 |
## | 0.000 | 0.000 | 1.000 | 0.043 |
## | 0.000 | 0.000 | 1.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## epilepsy | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## mood disorder | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## obsessive compulsive disorder | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## pmdd | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## sinus infection | 3 | 0 | 0 | 3 |
## | 1.000 | 0.000 | 0.000 | 0.130 |
## | 0.600 | 0.000 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## sinus infection, chest infection | 1 | 0 | 0 | 1 |
## | 1.000 | 0.000 | 0.000 | 0.043 |
## | 0.200 | 0.000 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## ulcer | 0 | 1 | 0 | 1 |
## | 0.000 | 1.000 | 0.000 | 0.043 |
## | 0.000 | 0.059 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## upper respiratory infection | 1 | 0 | 0 | 1 |
## | 1.000 | 0.000 | 0.000 | 0.043 |
## | 0.200 | 0.000 | 0.000 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
## Column Total | 5 | 17 | 1 | 23 |
## | 0.217 | 0.739 | 0.043 | |
## ---------------------------------|-----------|-----------|-----------|-----------|
##
## ## [1] biaxin lamictal lamictal lamictal biaxin lamictal lamictal
## [8] biaxin lamictal lamictal lamictal lamictal biaxin lamictal
## [15] lamictal biaxin lamictal lamictal sarafem lamictal lamictal
## [22] lamictal lamictal
## 502 Levels: abilify accolate accupril accutane aciphex actiq ... zyvoxMongo Data for Model
STORE NAIVE MODEL to Mongo
# install.packages("gridExtra")
# Mongo_Train_Cond_Corpus$drop()
Mongo_Train_Cond_Corpus$count()
#upload corpus_condition_clean_dtm
Mongo_Train_Cond_Corpus$insert(as.data.frame(Mongo_freq_words))
Mongo_Train_Cond_Corpus$index()
# Read Freq words from Mongo
Mongo_Read_Freq <- Mongo_Train_Cond_Corpus$find()
# Prepare for Test
Nav_model <- Mong_train_classifier
# Upload the Nav_Model to Mongo
saveRDS(Nav_model,"model/Nav_model.rds")
fs_model$remove('Nav_model.rds')
fs_model$upload("model/Nav_model.rds")
# Downlaod Model fromMOngo
fs_model$download("Nav_model.rds","mongo/MongoNav.rds")
#------------------------------------------------------------Mongo Test
# Mongo_tm_dte_condition <- data.frame(doc_id = test$urlDrugName,text = test$condition)
Mongo_tm_dte_condition_User <- data.frame(doc_id = c("NoDRUG", "NoNEWDRUG",""),text =
c("I am in depression",
"I have sinus",
"I have throt infection"))
dtquestion <- Mongo_tm_dte_condition_User
Mongo_tm_dte_condition_User <- data.frame(doc_id = c(rep("noDrug",1,length(dtquestion[,1]))),
text = dtquestion$text )
Mongo_corpus_condition_test_user <-VCorpus(DataframeSource(Mongo_tm_dte_condition_User))
# Create Document Term Matrix with only Freuest word as per train data using Mongo_freq_words
as.list(Mongo_Read_Freq)
Mongo_dtm.test.nb <- DocumentTermMatrix(Mongo_corpus_condition_test_user, control=list(dictionary =
Mongo_Read_Freq$Mongo_freq_words,
removePunctuation =
TRUE,
tolower = TRUE,
stripWhitespace= TRUE,
removeNumbers = TRUE,
stopwords = TRUE))
dim(Mongo_dtm.test.nb)
# Function to convert the word frequencies to yes (presence) and no (absence) labels
convert_mongo <- function(x) {
y <- ifelse(x > 0, 1,0)
y <- factor(y, levels=c(0,1), labels=c("No", "Yes"))
y
}
Mongo_testNB <- apply(Mongo_dtm.test.nb, 2, convert_mongo)
# Read Model from Mongo
# Do Predict
#------------------------------------------------------------
# load the model
saved_model <- readRDS("mongo/MongoNav.rds")
MOngo_red_lap <- predict(Mong_train_classifier,Mongo_testNB)
Mongo_Pred_lap <- predict(saved_model,Mongo_testNB)
fct_count(MOngo_red_lap,sort = TRUE)
CrossTable(Mongo_tm_dte_condition_User$text,MOngo_red_lap,prop.chisq = FALSE, prop.t = FALSE, dnn = c("Problem","Drug"))Using laplace smoothing
No change in result noted , mostly due to samll sample size.
#--------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------
# ------------- Using laplace
train_classifier_lp <- naiveBayes(new_train,train$urlDrugName,laplace = 1)
class(train_classifier)## [1] "naiveBayes" # Test
test_predict_lp <- predict(train_classifier_lp, new_test)
table (test_predict,test$urlDrugName)##
## test_predict biaxin depakene lamictal sarafem
## abilify 0 0 0 0
## accolate 0 0 0 0
## accupril 0 0 0 0
## accutane 0 0 0 0
## aciphex 0 0 0 0
## actiq 0 0 0 0
## actonel 0 0 0 0
## actos 0 0 0 0
## acyclovir 0 0 0 0
## aczone 0 0 0 0
## adcirca 0 0 0 0
## adderall 0 0 0 0
## adderall-xr 0 0 0 0
## adipex-p 0 0 0 0
## advair-diskus 0 0 0 0
## advair-hfa 0 0 0 0
## albuterol 0 0 0 0
## aldactone 0 0 0 0
## aldara 0 0 0 0
## alendronate 0 0 0 0
## alesse 0 0 0 0
## allegra 0 0 0 0
## allegra-d 0 0 0 0
## aloprim 0 0 0 0
## alprazolam 0 0 0 0
## ambien 0 0 0 0
## ambien-cr 0 0 0 0
## amerge 0 0 0 0
## amitiza 0 0 0 0
## amoxil 0 0 0 0
## amphetamine 0 0 0 0
## ampicillin 0 0 0 0
## anafranil 0 0 0 0
## androgel 0 0 0 0
## angeliq 0 0 0 0
## antivert 0 0 0 0
## aralen 0 0 0 0
## arava 0 0 0 0
## aricept 0 0 0 0
## arimidex 0 0 0 0
## armour-thyroid 0 0 0 0
## aromasin 0 0 0 0
## arthrotec 0 0 0 0
## asacol 0 0 0 0
## asmanex 0 0 0 0
## atacand 0 0 0 0
## atarax 0 0 0 0
## atenolol 0 0 0 0
## ativan 0 0 0 0
## atripla 0 0 0 0
## augmentin 0 0 0 0
## avage 0 0 0 0
## avapro 0 0 0 0
## avelox 0 0 0 0
## avita 0 0 0 0
## avodart 0 0 0 0
## axert 0 0 0 0
## aygestin 0 0 0 0
## azasan 0 0 0 0
## azelex 0 0 0 0
## azopt 0 0 0 0
## azor 0 0 0 0
## baciim 0 0 0 0
## baclofen 0 0 0 0
## bactrim-ds 0 0 0 0
## bactroban 0 0 0 0
## baraclude 0 0 0 0
## benadryl 0 0 0 0
## benicar 0 0 0 0
## benicar-hct 0 0 0 0
## benzaclin 0 0 0 0
## betamethasone 0 0 0 0
## betaseron 0 0 0 0
## biaxin 5 0 0 0
## bisoprolol 0 0 0 0
## boniva 0 0 0 0
## botox 0 0 0 0
## buprenorphine 0 0 0 0
## buspar 0 0 0 0
## byetta 0 0 0 0
## bystolic 0 0 0 0
## caduet 0 0 0 0
## carac 0 0 0 0
## cardura 0 0 0 0
## cardura-xl 0 0 0 0
## cataflam 0 0 0 0
## catapres 0 0 0 0
## ceclor 0 0 0 0
## cefzil 0 0 0 0
## celebrex 0 0 0 0
## celexa 0 0 0 0
## chantix 0 0 0 0
## cialis 0 0 0 0
## cipro 0 0 0 0
## citalopram 0 0 0 0
## clarinex 0 0 0 0
## claripel-cream 0 0 0 0
## clarithromycin 0 0 0 0
## claritin 0 0 0 0
## cleocin 0 0 0 0
## climara 0 0 0 0
## climara-pro 0 0 0 0
## clindamycin 0 0 0 0
## clindamycin-topical 0 0 0 0
## clobetasol 0 0 0 0
## clobex 0 0 0 0
## clomid 0 0 0 0
## clonazepam 0 0 0 0
## compazine 0 0 0 0
## concerta 0 0 0 0
## copaxone 0 0 0 0
## coreg 0 0 0 0
## corgard 0 0 0 0
## cosopt 0 0 0 0
## coumadin 0 0 0 0
## cozaar 0 0 0 0
## crestor 0 0 0 0
## cymbalta 0 0 0 0
## cyproheptadine 0 0 0 0
## cytomel 0 0 0 0
## danazol 0 0 0 0
## dapsone 0 0 0 0
## darvocet-n 0 0 0 0
## delestrogen 0 0 0 0
## deltasone 0 0 0 0
## depakene 0 0 0 0
## depakote 0 0 0 0
## depo-provera 0 0 0 0
## desonide 0 0 0 0
## desyrel 0 0 0 0
## detrol 0 0 0 0
## detrol-la 0 0 0 0
## dexamethasone 0 0 0 0
## dexedrine 0 0 0 0
## dextroamphetamine 0 0 0 0
## dextrostat 0 0 0 0
## diazepam 0 0 0 0
## diclofenac 0 0 0 0
## dicyclomine 0 0 0 0
## differin 0 0 0 0
## diflucan 0 0 0 0
## dilantin 0 0 0 0
## dilaudid 0 0 0 0
## diltiazem 0 0 0 0
## diovan 0 0 0 0
## dispermox 0 0 0 0
## ditropan 0 0 0 0
## divigel 0 0 0 0
## doryx 0 0 0 0
## dostinex 0 0 0 0
## dovonex 0 0 0 0
## doxepin 0 0 0 0
## doxycycline 0 0 0 0
## duac 0 0 0 0
## duragesic 0 0 0 0
## dysport 0 0 0 0
## ecotrin 0 0 0 0
## effexor 0 0 0 0
## effexor-xr 0 0 0 0
## efudex 0 0 0 0
## elavil 0 0 0 0
## elidel 0 0 0 0
## elmiron 0 0 0 0
## elocon 0 0 0 0
## emsam 0 0 0 0
## enablex 0 0 0 0
## enalapril 0 0 0 0
## enbrel 0 0 0 0
## erythra-derm 0 0 0 0
## erythromycin 0 0 0 0
## estrace 0 0 0 0
## estrasorb 0 0 0 0
## estratest 0 0 0 0
## estring 0 0 0 0
## estrostep-fe 0 0 0 0
## eulexin 0 0 0 0
## evista 0 0 0 0
## evoclin 0 0 0 0
## exelon 0 0 0 0
## femara 0 0 0 0
## femhrt 0 0 0 0
## femring 0 0 0 0
## fentanyl 0 0 0 0
## fexofenadine 0 0 0 0
## finacea 0 0 0 0
## fioricet 0 0 0 0
## fiorinal 0 0 0 0
## flagyl 0 0 0 0
## flexeril 0 0 0 0
## flomax 0 0 0 0
## flonase 0 0 0 0
## flovent 0 0 0 0
## fluconazole 0 0 0 0
## fluvoxamine 0 0 0 0
## follistim 0 0 0 0
## fortaz 0 0 0 0
## fosamax 0 0 0 0
## fosamax-plus-d 0 0 0 0
## fosinopril 0 0 0 0
## frova 0 0 0 0
## galzin 0 0 0 0
## geodon 0 0 0 0
## glucophage 0 0 0 0
## glucophage-xr 0 0 0 0
## gonal-f-rff 0 0 0 0
## grifulvin-v 0 0 0 0
## halcion 0 0 0 0
## haldol 0 0 0 0
## humira 0 0 0 0
## hydrochlorothiazide 0 0 0 0
## hydrocodone-and-acetaminophen 0 0 0 0
## hydrocortisone 0 0 0 0
## hyoscyamine 0 0 0 0
## hytrin 0 0 0 0
## hyzaar 0 0 0 0
## imitrex 0 0 0 0
## imodium 0 0 0 0
## imuran 0 0 0 0
## inderal 0 0 0 0
## innofem 0 0 0 0
## inspra 0 0 0 0
## isoniazid 0 0 0 0
## keflex 0 0 0 0
## kenalog 0 0 0 0
## keppra 0 0 0 0
## ketoconazole 0 0 0 0
## ketorolac 0 0 0 0
## klaron 0 0 0 0
## klonopin 0 0 0 0
## lac-hydrin 0 0 0 0
## lamictal 3 1 10 3
## lamisil 0 0 0 0
## lamotrigine 0 0 0 0
## lantus 0 0 0 0
## lasix 0 0 0 0
## latisse 0 0 0 0
## levaquin 0 0 0 0
## levetiracetam 0 0 0 0
## levitra 0 0 0 0
## levora 0 0 0 0
## levothroid 0 0 0 0
## levoxyl 0 0 0 0
## lexapro 0 0 0 0
## lidex 0 0 0 0
## lidocaine 0 0 0 0
## lipitor 0 0 0 0
## lithium-carbonate 0 0 0 0
## lo-ovral 0 0 0 0
## locoid-lipocream 0 0 0 0
## lodine 0 0 0 0
## lopressor 0 0 0 0
## lorazepam 0 0 0 0
## lortab 0 0 0 0
## lotrel 0 0 0 0
## lotronex 0 0 0 0
## lumigan 0 0 0 0
## lunesta 0 0 0 0
## lupron 0 0 0 0
## lybrel 0 0 0 0
## lyrica 0 0 0 0
## macrobid 0 0 0 0
## malarone 0 0 0 0
## maxalt 0 0 0 0
## medroxyprogesterone 0 0 0 0
## meperidine 0 0 0 0
## mercaptopurine 0 0 0 0
## meridia 0 0 0 0
## metformin 0 0 0 0
## metformin-extended-release 0 0 0 0
## methadone 0 0 0 0
## methimazole 0 0 0 0
## methotrexate 0 0 0 0
## metoclopramide 0 0 0 0
## metoprolol 0 0 0 0
## metrogel 0 0 0 0
## metrolotion 0 0 0 0
## metronidazole 0 0 0 0
## mevacor 0 0 0 0
## micardis 0 0 0 0
## minocin 0 0 0 0
## minocycline 0 0 0 0
## minoxidil 0 0 0 0
## miralax 0 0 0 0
## mirapex 0 0 0 0
## mirena 0 0 0 0
## mirtazapine 0 0 0 0
## mobic 0 0 0 0
## morphine 0 0 0 0
## motrin 0 0 0 0
## naltrexone 0 0 0 0
## naprosyn 0 0 0 0
## naproxen 0 0 0 0
## nardil 0 0 0 0
## nasacort 0 0 0 0
## nasacort-aq 0 0 0 0
## nasarel 0 0 0 0
## nasonex 0 0 0 0
## neoprofen 0 0 0 0
## neurontin 0 0 0 0
## nexium 0 0 0 0
## niacor 0 0 0 0
## niaspan 0 0 0 0
## nitrofurantoin 0 0 0 0
## nizoral 0 0 0 0
## nizoral-shampoo 0 0 0 0
## nolvadex 0 0 0 0
## noroxin 0 0 0 0
## norpramin 0 0 0 0
## nortriptyline 0 0 0 0
## norvasc 0 0 0 0
## nuvaring 0 0 0 0
## nuvigil 0 0 0 0
## nystatin 0 0 0 0
## omacor 0 0 0 0
## omnicef 0 0 0 0
## omnitrope 0 0 0 0
## oracea 0 0 0 0
## ortho-evra 0 0 0 0
## ortho-novum 0 0 0 0
## ortho-tri-cyclen 0 0 0 0
## ortho-tri-cyclen-lo 0 0 0 0
## oxazepam 0 0 0 0
## oxybutynin 0 0 0 0
## oxycodone 0 0 0 0
## oxycodone-and-acetaminophen 0 0 0 0
## oxycontin 0 0 0 0
## pamelor 0 0 0 0
## panixine-disperdose 0 0 0 0
## parlodel 0 0 0 0
## paxil 0 0 0 0
## paxil-cr 0 0 0 0
## penicillin-v 0 0 0 0
## penlac 0 0 0 0
## pentasa 0 0 0 0
## pepcid 0 0 0 0
## percocet 0 0 0 0
## periogard 0 0 0 0
## periostat 0 0 0 0
## permapen 0 0 0 0
## phendimetrazine 0 0 0 0
## phenergan 0 0 0 0
## pilocarpine 0 0 0 0
## plavix 0 0 0 0
## plendil 0 0 0 0
## polymyxin-b 0 0 0 0
## ponstel 0 0 0 0
## pravachol 0 0 0 0
## prednisolone 0 0 0 0
## prednisone 0 0 0 0
## premarin 0 0 0 0
## premarin-vaginal 0 0 0 0
## prempro 0 0 0 0
## prevacid 0 0 0 0
## prilosec 0 0 0 0
## prinivil 0 0 0 0
## pristiq 0 0 0 0
## proair-hfa 0 0 0 0
## prochlorperazine 0 0 0 0
## progesterone 0 0 0 0
## prograf 0 0 0 0
## proloprim 0 0 0 0
## prometrium 0 0 0 0
## propecia 0 0 0 0
## propranolol 0 0 0 0
## proquin-xr 0 0 0 0
## proscar 0 0 0 0
## protonix 0 0 0 0
## protopic 0 0 0 0
## provera 0 0 0 0
## provigil 0 0 0 0
## prozac 0 0 0 0
## quibron-t 0 0 0 0
## qvar 0 0 0 0
## ranitidine 0 0 0 0
## rebif 0 0 0 0
## reglan 0 0 0 0
## relafen 0 0 0 0
## relpax 0 0 0 0
## remeron 0 0 0 0
## remicade 0 0 0 0
## renova 0 0 0 0
## requip 0 0 0 0
## restasis 0 0 0 0
## restoril 0 0 0 0
## retin-a 0 0 0 0
## retin-a-micro 0 0 0 0
## rhinocort 0 0 0 0
## ribavirin 0 0 0 0
## rifadin 0 0 0 0
## risperdal 0 0 0 0
## risperdal-consta 0 0 0 0
## ritalin 0 0 0 0
## ritalin-la 0 0 0 0
## saizen 0 0 0 0
## sanctura-xr 0 0 0 0
## sarafem 0 0 0 1
## seasonale 0 0 0 0
## seasonique 0 0 0 0
## selegiline 0 0 0 0
## septra 0 0 0 0
## seroquel 0 0 0 0
## seroquel-xr 0 0 0 0
## serzone 0 0 0 0
## singulair 0 0 0 0
## skelaxin 0 0 0 0
## solodyn 0 0 0 0
## soltamox 0 0 0 0
## soma 0 0 0 0
## sonata 0 0 0 0
## sotret 0 0 0 0
## spiriva 0 0 0 0
## spironolactone 0 0 0 0
## strattera 0 0 0 0
## suboxone 0 0 0 0
## sular 0 0 0 0
## sulfasalazine 0 0 0 0
## sulindac 0 0 0 0
## sumatriptan 0 0 0 0
## symbicort 0 0 0 0
## symbyax 0 0 0 0
## synthroid 0 0 0 0
## tambocor 0 0 0 0
## tamiflu 0 0 0 0
## tapazole 0 0 0 0
## tarka 0 0 0 0
## tazorac 0 0 0 0
## tegretol 0 0 0 0
## tekturna 0 0 0 0
## temovate 0 0 0 0
## tenormin 0 0 0 0
## tequin 0 0 0 0
## tetracycline 0 0 0 0
## tirosint 0 0 0 0
## tobramycin 0 0 0 0
## tofranil-pm 0 0 0 0
## topamax 0 0 0 0
## topicort 0 0 0 0
## toprol-xl 0 0 0 0
## toradol 0 0 0 0
## tramadol 0 0 0 0
## trazodone 0 0 0 0
## trental 0 0 0 0
## tri-luma 0 0 0 0
## triamcinolone 0 0 0 0
## triaz 0 0 0 0
## trileptal 0 0 0 0
## trilipix 0 0 0 0
## trimethobenzamide 0 0 0 0
## trimethoprim 0 0 0 0
## triphasil 0 0 0 0
## tussionex 0 0 0 0
## tylenol 0 0 0 0
## ultracet 0 0 0 0
## ultram 0 0 0 0
## ultram-er 0 0 0 0
## vagifem 0 0 0 0
## valium 0 0 0 0
## valtrex 0 0 0 0
## vaniqa 0 0 0 0
## vasotec 0 0 0 0
## ventolin-hfa 0 0 0 0
## vesicare 0 0 0 0
## viagra 0 0 0 0
## vicodin 0 0 0 0
## vicoprofen 0 0 0 0
## vigamox 0 0 0 0
## vioxx 0 0 0 0
## vistaril 0 0 0 0
## vivelle 0 0 0 0
## vivelle-dot 0 0 0 0
## voltaren 0 0 0 0
## vytorin 0 0 0 0
## vyvanse 0 0 0 0
## warfarin 0 0 0 0
## wellbutrin 0 0 0 0
## wellbutrin-sr 0 0 0 0
## wellbutrin-xl 0 0 0 0
## xalatan 0 0 0 0
## xanax 0 0 0 0
## xanax-xr 0 0 0 0
## xenical 0 0 0 0
## xyrem 0 0 0 0
## xyzal 0 0 0 0
## yasmin 0 0 0 0
## zantac 0 0 0 0
## zegerid 0 0 0 0
## zestoretic 0 0 0 0
## ziac 0 0 0 0
## ziana 0 0 0 0
## zithromax 0 0 0 0
## zmax 0 0 0 0
## zocor 0 0 0 0
## zofran 0 0 0 0
## zoloft 0 0 0 0
## zomig 0 0 0 0
## zovirax 0 0 0 0
## zovirax-topical 0 0 0 0
## zyban 0 0 0 0
## zyprexa 0 0 0 0
## zyrtec 0 0 0 0
## zyrtec-d 0 0 0 0
## zyvox 0 0 0 0 forcats::fct_count(test$urlDrugName) forcats::fct_count(test_predict,sort = TRUE) CrossTable(test_predict,test$urlDrugName,prop.chisq = TRUE, prop.t = FALSE, dnn = c("Predicted","Actual"))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | Actual
## Predicted | biaxin | depakene | lamictal | sarafem | Row Total |
## -------------|-----------|-----------|-----------|-----------|-----------|
## biaxin | 5 | 0 | 0 | 0 | 5 |
## | 6.114 | 0.217 | 2.174 | 0.870 | |
## | 1.000 | 0.000 | 0.000 | 0.000 | 0.217 |
## | 0.625 | 0.000 | 0.000 | 0.000 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## lamictal | 3 | 1 | 10 | 3 | 17 |
## | 1.435 | 0.092 | 0.921 | 0.001 | |
## | 0.176 | 0.059 | 0.588 | 0.176 | 0.739 |
## | 0.375 | 1.000 | 1.000 | 0.750 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## sarafem | 0 | 0 | 0 | 1 | 1 |
## | 0.348 | 0.043 | 0.435 | 3.924 | |
## | 0.000 | 0.000 | 0.000 | 1.000 | 0.043 |
## | 0.000 | 0.000 | 0.000 | 0.250 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 8 | 1 | 10 | 4 | 23 |
## | 0.348 | 0.043 | 0.435 | 0.174 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
##
## CrossTable(test_predict_lp,test$urlDrugName,prop.chisq = TRUE, prop.t = FALSE, dnn = c("Predicted","Actual"))##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | Actual
## Predicted | biaxin | depakene | lamictal | sarafem | Row Total |
## -------------|-----------|-----------|-----------|-----------|-----------|
## biaxin | 5 | 0 | 0 | 0 | 5 |
## | 6.114 | 0.217 | 2.174 | 0.870 | |
## | 1.000 | 0.000 | 0.000 | 0.000 | 0.217 |
## | 0.625 | 0.000 | 0.000 | 0.000 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## lamictal | 3 | 1 | 10 | 3 | 17 |
## | 1.435 | 0.092 | 0.921 | 0.001 | |
## | 0.176 | 0.059 | 0.588 | 0.176 | 0.739 |
## | 0.375 | 1.000 | 1.000 | 0.750 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## sarafem | 0 | 0 | 0 | 1 | 1 |
## | 0.348 | 0.043 | 0.435 | 3.924 | |
## | 0.000 | 0.000 | 0.000 | 1.000 | 0.043 |
## | 0.000 | 0.000 | 0.000 | 0.250 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 8 | 1 | 10 | 4 | 23 |
## | 0.348 | 0.043 | 0.435 | 0.174 | |
## -------------|-----------|-----------|-----------|-----------|-----------|
##
##