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Data X:
1 1 1 13 0 3 0 0 1 1 14 1 4 2 1 5 2 6 1 4 4 1 2 0 1 1 3 1 1 2 1 9 1 2 2 0 1 1 10 0 2 0 1 1 2 12 1 2 4 1 5 2 14 1 3 2 1 2 1 13 2 1 2 0 1 0 98 2 1 1 1 1 3 8 1 2 8 1 3 3 10 0 3 0 0 5 1 10 1 2 2 1 3 4 12 0 2 0 1 3 3 15 0 4 0 1 2 3 15 0 3 0 1 5 4 15 1 4 2 1 4 3 15 1 2 4 0 4 4 15 1 4 3 1 2 4 12 1 2 3 0 5 1 7 0 2 0 1 3 0 98 2 1 4 1 3 4 10 1 2 4 0 5 4 5 1 1 1 1 2 0 98 0 2 0 1 1 4 13 1 1 2 1 3 3 12 0 1 0 0 5 2 98 2 1 1 1 2 3 15 1 4 3 0 5 1 15 1 3 1 1 4 1 11 1 2 2 0 2 1 5 1 1 5 1 1 4 14 1 2 3 1 4 4 98 0 4 0 0 4 1 3 1 4 3 1 3 4 13 1 1 3 1 3 1 4 0 2 0 1 1 3 15 0 2 0 1 1 1 1 0 3 0 0 3 4 12 0 4 0 1 5 1 98 0 1 0 0 2 3 14 0 3 0 1 5 3 15 1 4 3 1 4 4 12 0 3 0 0 4 2 4 1 2 4 1 3 1 5 0 2 0 1 2 2 11 1 2 1 1 3 4 15 1 2 4 1 2 3 10 0 2 0 1 4 0 5 2 3 3 0 4 1 9 0 2 0 1 2 1 15 1 4 1 1 5 1 8 0 4 0 1 4 0 3 0 2 0 1 4 3 15 1 4 2 1 2 2 3 0 3 0 1 2 1 98 2 2 5 1 3 0 2 1 2 2 0 3 1 4 1 2 4 1 5 3 11 0 2 0 0 1 1 11 2 4 1 1 5 4 12 0 4 0 1 1 3 1 1 2 8 1 4 3 11 1 2 5 1 5 0 8 0 3 0 1 4 1 13 0 4 0 1 2 3 13 1 1 2 1 2 3 15 1 4 2 1 1 3 15 0 4 0 1 2 1 98 1 2 2 1 3 1 12 1 4 2 1 2 1 8 1 3 1 0 4 1 4 1 2 4 1 5 1 14 1 3 4 1 5 0 15 0 2 0 1 5 3 8 0 3 0 1 5 2 7 1 2 2 0 3 0 12 1 3 2 0 1 2 5 8 1 4 1 5 1 7 0 1 0 0 4 2 13 0 2 0 0 1 4 15 1 3 2 1 5 4 11 0 4 0 0 4 1 8 2 4 4 1 2 1 98 0 2 0 1 5 0 4 1 2 2 1 3 0 4 0 2 0 1 4 0 12 1 3 3 1 4 1 10 1 1 2 1 3 1 8 1 3 3 1 4 4 11 1 4 8 1 4 4 98 0 3 0 0 4 1 8 1 1 2 1 3 1 13 1 3 2 1 3 3 1 1 2 2 0 1 1 11 1 2 2 1 3 0 2 1 3 4 1 1 1 8 0 2 0 1 2 1 4 1 2 2 1 3 1 10 1 3 2 0 1 4 98 1 2 2 1 1 1 98 0 4 0 1 5 0 98 2 2 2 1 5 0 1 0 1 0 1 3 1 8 0 3 0 1 2 3 10 1 2 2 1 5 3 11 0 2 0 1 4 1 6 1 3 2 1 5 3 15 0 2 0 1 3 3 15 1 2 3 1 4 3 12 0 2 0 1 5 3 15 1 1 4 1 3 1 6 1 2 3 1 1 3 14 1 2 3 0 4 2 11 1 2 8 0 3 3 6 0 1 0 1 5 4 15 1 4 2 0 4 1 12 1 3 2 0 5 2 15 1 2 4 1 5 3 5 0 1 0 1 2 2 14 0 2 0 1 5 0 7 1 3 2 1 5 4 10 0 1 0 1 1 4 12 1 4 4 1 5 4 10 0 3 0 0 5 1 13 0 3 0 0 2 1 7 1 2 4 1 2 1 98 0 2 0 1 5 3 14 0 2 0 0 4 4 15 1 4 2 0 2 3 15 1 2 2 1 2 0 7 1 1 2 1 3 4 15 0 4 0 1 2 4 15 0 2 0 1 1 4 15 1 2 2 1 1 0 8 8 2 2 1 3 1 9 0 1 0 1 4 1 6 1 2 2 1 2 0 10 1 4 4 1 5 1 10 0 3 0 0 1 3 12 0 2 0 0 1 3 12 0 2 0 1 2 2 12 1 2 4 1 2 3 98 1 3 3 1 1 4 15 1 3 2 1 1 3 9 0 2 0 1 1 4 15 1 2 2 1 1 2 14 0 1 0 0 2 4 15 1 4 1 1 3 3 11 1 3 2 1 3 0 1 1 3 2 1 2 1 8 1 4 5 1 4 3 11 1 1 2 1 2 1 4 0 3 0 1 1 0 3 1 2 4 1 5 1 2 2 2 4 1 2 1 13 1 2 2 1 5 3 12 1 4 2 0 3 1 9 0 1 0 0 5 1 98 0 2 0 1 1 1 12 1 4 2 1 4 4 14 1 4 2 1 3 1 15 1 4 4 1 2 3 15 0 4 0 0 5 1 2 1 4 2 1 3 4 13 1 3 2 1 3 3 98 1 4 2 1 1 2 15 1 4 4 1 3 1 11 1 2 4 1 3 1 6 1 2 2 1 5 3 15 1 1 5 1 3 1 9 1 3 2 1 4 3 14 0 1 0 1 5 1 9 1 2 2 1 1 1 98 1 2 4 1 3 3 14 0 2 0 1 2 4 13 1 2 2 1 1 3 98 0 4 0 0 2 3 10 1 4 2 1 1 3 8 0 2 0 1 4 3 13 1 1 4 1 5 1 10 1 1 5 1 4 1 8 1 2 2 1 1 1 10 1 2 2 1 5 3 14 1 2 2 0 5 1 15 0 4 0 1 2 1 98 1 1 4 0 5 3 98 1 2 2 1 2 4 15 1 2 2 1 5 3 15 1 3 1 0 5 0 98 1 2 2 1 5 3 11 0 1 0 0 1 1 11 0 2 0 1 1 1 11 1 1 4 0 2 0 12 0 2 0 1 4 1 13 1 2 4 1 5 1 6 1 2 1 1 3 4 98 1 4 3 0 3 4 11 1 4 2 1 2 1 98 0 2 0 0 4 1 12 1 1 1 1 5 3 12 0 2 0 1 4 3 15 0 4 0 1 5 3 15 1 4 2 1 5 4 15 0 2 0 0 2 4 9 1 2 3 1 1 1 11 2 3 2 1 4 2 9 0 2 0 1 5 0 12 2 2 1 1 1 0 98 2 1 4 0 5 1 98 1 3 4 1 1 1 10 0 1 0 0 1 1 11 1 3 2 1 5 4 11 1 2 4 0 2 3 10 1 2 4 1 4 2 13 1 2 2 1 2 0 10 1 3 4 1 5 1 5 0 2 0 1 4 4 5 1 1 5 1 4 4 13 0 4 0 1 3 2 11 0 2 0 1 5 0 9 1 2 4 0 2 1 12 1 2 2 1 5 4 15 0 4 0 1 4 0 1 8 9 5 1 5 1 8 1 3 3 0 4 3 10 0 1 0 0 5 1 14 1 2 3 1 5 3 98 0 2 0 0 1 0 11 1 1 4 1 2 0 6 0 2 0 1 1 0 10 1 1 5 1 1 0 98 0 2 0 1 4 0 98 1 3 8 1 1 4 11 1 2 8 0 5 1 9 1 2 4 0 4 1 9 1 2 2 1 2 1 15 1 2 3 1 3 1 12 1 1 3 1 5 2 10 0 2 0 0 1 4 13 1 3 2 1 2 1 9 0 2 0 1 3 0 5 2 1 4 1 3 1 5 1 4 4 0 3 0 2 1 2 1 1 1 0 6 1 4 4 1 4 0 98 2 1 2 1 5 0 6 2 2 2 1 1 0 1 2 2 2 1 1 1 1 1 3 1 0 5 0 3 0 1 0 1 1 1 9 0 1 0 1 1 0 2 1 2 2 1 1 4 15 2 1 4 1 5 2 98 0 3 0 1 2 3 15 0 2 0 1 1 0 2 0 1 0 0 5 1 1 1 2 1 1 2 1 1 1 2 2 1 5 4 15 0 3 0 1 2 4 15 1 2 5 1 4 4 14 0 4 0 0 5 1 10 0 2 0 1 3 1 4 1 3 4 1 3 1 10 0 4 0 1 2 4 15 1 2 3 1 2 3 15 1 2 3 1 5 4 13 1 2 4 1 1 3 10 0 3 0 1 2 4 13 1 2 5 1 1 1 11 1 3 1 1 3 1 1 2 2 5 0 2 4 98 1 2 2 1 5 1 8 1 3 2 1 5 1 10 1 1 4 1 5 3 13 1 3 5 1 5 1 1 1 2 5 1 2 1 2 1 4 2 1 3 1 10 8 1 4 1 5 2 15 1 3 4 1 4 4 10 1 3 4 1 1 3 10 1 3 8 0 2 1 9 1 2 4 1 5 1 11 1 1 4 1 1 1 11 0 2 0 1 4 1 8 1 1 4 0 2 1 9 0 2 0 0 2 1 3 1 2 4 0 2 1 11 1 2 4 0 2 1 11 1 3 2 1 2 1 11 0 3 0 0 5 1 11 1 4 4 1 3 2 10 0 4 0 1 1 3 98 0 2 0 1 5 0 7 1 2 4 1 1 3 12 1 3 2 1 1 1 6 1 3 3 0 1 2 11 1 2 2 1 4 1 7 0 2 0 1 4 0 9 0 3 0 0 1 3 12 0 2 0 1 2 0 10 2 3 3 1 1 1 15 1 2 3 1 3 1 12 1 1 3 1 3 1 4 1 2 2 1 1 1 9 1 2 2 1 5 4 14 0 4 0 1 1 1 10 1 2 2 1 2 3 12 0 2 0 1 5 1 15 2 2 5 1 1 3 12 1 3 2 1 3 2 5 0 3 0 1 4 3 98 0 2 0 1 3 1 2 0 3 0 1 2 1 10 1 3 4 1 2 3 11 2 2 2 1 1 0 4 1 1 2 0 5 1 11 0 3 0 0 5 1 3 1 2 2 1 3 1 9 1 3 2 1 1 4 15 1 4 2 1 5 1 98 1 1 3 1 1 0 1 0 2 0 1 4 1 10 1 2 9 1 5 0 4 8 3 2 1 5 1 9 0 1 0 0 3 1 4 1 4 2 1 2 1 9 0 3 0 1 3 1 8 1 2 2 1 5 3 10 1 2 8 1 4 1 11 1 2 1 1 1 3 11 1 2 4 1 3 3 7 0 2 0 0 5 1 6 1 4 3 1 3 2 9 0 3 0 1 3 1 5 1 2 2 1 4 3 9 1 1 3 1 3 1 10 1 2 5 1 1 1 12 0 4 0 0 3 0 98 1 2 2 0 4 2 3 0 4 0 1 4 1 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3 8 0 2 0 1 5 3 15 1 3 2 1 5 1 8 0 2 0 0 4 1 10 1 2 4 0 4 1 9 1 1 2 1 4 4 15 1 2 4 1 3 1 98 0 2 0 1 3 1 8 1 3 4 0 4 1 10 0 1 0 1 4 4 15 0 1 0 1 2 3 13 1 2 4 1 2 1 98 1 4 4 1 4 3 12 0 1 0 1 5 3 98 1 2 4 1 1 1 4 1 1 5 1 2 1 8 1 2 8 0 2 4 8 2 1 5 1 1 3 13 2 1 4 1 1 4 15 2 1 4 1 1 3 15 1 4 1 1 3 1 11 0 1 0 1 5 3 6 1 2 4 1 3 1 9 1 3 5 0 1 1 11 0 1 0 0 5 1 10 1 2 5 1 5 1 6 0 2 0 1 4 1 5 1 1 5 1 5 0 6 1 1 4 1 4 0 2 0 1 0 1 4 4 4 0 1 0 1 3 1 11 1 3 5 0 5 1 12 0 2 0 1 2 3 14 1 2 2 1 2 1 9 1 4 2 1 2 1 2 0 1 0 1 5 1 12 0 2 0 1 5 0 2 1 2 2 0 4 1 3 1 2 2 1 5 0 98 0 2 0 1 4 1 3 1 3 2 1 3 0 12 0 4 0 1 4 1 10 1 4 2 1 2 3 12 1 2 2 1 4 3 10 1 1 4 1 4 4 13 0 1 0 1 3 3 98 0 3 0 1 2 3 9 1 3 3 1 2 0 98 0 3 0 1 3 1 8 1 4 2 0 5 1 1 1 4 2 1 1 4 9 0 1 0 1 4 2 6 1 2 2 1 1 1 15 1 2 3 1 3 0 11 1 4 2 1 4 1 98 1 1 4 1 1 3 98 1 1 4 1 1 4 11 0 2 0 1 4 1 11 1 4 2 1 1 2 10 0 2 0 1 5 1 11 0 3 0 1 4 1 10 0 2 0 1 3 1 11 1 2 2 1 4 1 10 1 4 1 1 5 1 98 0 2 0 1 5 0 1 1 3 4 1 2 1 3 1 1 3 0 4 1 8 1 2 3 1 4 1 6 0 1 0 1 2 0 98 8 1 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7 1 1 5 0 5 1 9 0 2 0 1 5 1 2 1 2 4 1 4 1 11 1 1 2 0 2 3 12 0 4 0 1 5 3 8 1 2 2 1 5 1 7 1 2 3 1 3 0 10 1 3 2 1 4 1 2 1 2 2 0 5 2 3 0 1 0 0 3 4 2 1 2 1 1 4 1 8 0 2 0 0 4 0 3 8 8 2 1 4 0 2 1 2 2 0 4 3 15 1 3 4 1 2 4 98 1 2 2 0 4 1 12 1 3 2 1 4 4 15 0 2 0 1 1 4 15 0 2 0 1 2 0 7 1 1 8 0 5 1 4 8 2 3 1 5 4 98 0 1 0 1 2 1 5 1 1 3 0 5 1 12 0 3 0 1 1 4 7 8 2 3 1 3 1 2 0 1 0 0 5 3 10 0 2 0 1 5 3 14 2 1 5 0 3 3 15 0 2 0 1 3 3 15 1 2 4 1 2 3 13 0 2 0 1 1 1 10 0 2 0 1 2 1 10 0 1 0 0 4 1 10 0 3 0 1 3 3 98 2 2 2 1 2 3 11 1 2 2 1 3 3 15 1 2 4 1 1 4 13 1 2 4 0 1 0 1 1 3 2 1 5 1 98 0 1 0 1 2 1 6 8 2 4 0 1 1 10 2 1 2 1 3 3 98 0 2 0 1 1 3 3 1 1 4 1 4 0 3 2 2 2 1 2 1 5 0 3 0 1 1 1 5 1 2 2 1 4 0 98 0 1 0 1 2 1 2 0 2 0 1 3 1 5 2 1 8 1 4 3 98 0 3 0 1 1 1 98 1 1 2 1 5 1 98 0 1 0 1 3 3 11 1 1 2 0 2 4 12 0 2 0 1 1 3 12 2 9 5 1 4 3 15 1 2 3 1 4 3 13 0 2 0 0 1 4 15 1 2 3 1 2 3 10 1 1 3 1 4 1 10 1 1 2 1 4 3 12 1 1 4 1 4 4 15 0 4 0 0 5 4 6 2 2 2 0 2 0 3 0 4 0 1 2 1 7 2 2 4 1 2 1 10 0 3 0 1 5 0 4 0 2 0 1 4 1 11 1 2 8 1 5 1 8 1 4 2 1 5 0 8 0 2 0 1 3 1 11 1 2 4 0 4 1 5 1 4 4 0 5 1 8 0 3 0 1 3 4 5 0 2 0 1 4 1 10 8 1 5 1 3 1 9 1 4 4 1 4 1 10 1 4 4 0 2 4 11 1 4 2 0 2 3 15 1 3 2 1 2 0 2 2 2 2 1 5 4 8 1 4 2 0 3 1 11 1 2 2 1 1 1 7 1 2 4 1 1 3 12 1 2 3 0 2 1 10 0 2 0 1 4 4 10 1 2 3 0 1 1 12 0 4 0 0 3 1 10 1 2 4 1 5 1 10 0 2 0 0 1 3 14 0 2 0 1 1 4 8 1 2 3 0 2 0 2 0 3 0 1 5 1 5 2 1 4 1 3 1 4 2 2 5 1 4 0 98 2 2 9 0 4 3 13 0 2 0 1 1 2 9 1 3 5 1 2 1 12 1 2 5 1 4 1 12 1 2 3 1 4 1 10 1 2 3 1 4 0 2 0 1 0 0 5 1 5 2 2 2 1 1 1 12 1 3 3 1 1 3 13 0 2 0 1 2 3 13 0 3 0 1 1 0 4 8 1 4 1 4 3 10 1 1 3 0 2 3 11 1 1 5 0 2 1 11 0 4 0 1 1 1 13 0 2 0 1 2 3 9 1 3 5 1 1 1 8 1 3 2 1 1 1 11 0 2 0 1 3 1 8 1 2 4 1 4 3 6 1 1 3 1 3 1 6 1 1 4 1 5 1 5 1 2 1 1 4 1 10 0 4 0 1 2 1 9 1 2 2 0 5 1 10 1 3 2 1 1 3 10 0 3 0 0 2 3 12 1 2 3 1 4 3 9 0 2 0 1 3 1 4 0 4 0 1 4 3 11 0 3 0 1 2 3 15 1 2 4 0 3 1 14 0 2 0 0 1 1 10 1 2 4 1 2 4 12 1 2 2 0 4 3 98 0 1 0 1 4 4 13 0 2 0 0 2 1 10 1 1 5 1 4 1 4 0 2 0 1 3 0 8 2 4 2 0 1 1 98 1 1 3 1 4 1 3 1 2 2 1 4 1 6 0 1 0 0 1 1 11 1 1 8 1 3 1 9 1 2 1 1 1 2 2 2 1 4 1 4 0 1 0 1 0 1 3 2 98 0 2 0 1 1 1 11 1 2 4 1 4 1 9 1 3 4 1 4 0 98 2 2 5 1 5 3 15 0 1 0 1 2 4 15 1 2 3 1 4 4 12 0 1 0 0 3 4 7 2 3 2 1 3 3 8 0 2 0 1 2 4 13 0 2 0 0 2 1 11 1 2 3 1 3 1 2 1 2 2 1 3 3 7 1 3 2 1 2 1 4 0 1 0 0 3 4 8 1 1 5 1 1 1 12 1 3 1 1 5 1 1 0 1 0 1 2 3 15 1 1 4 1 5 3 12 1 1 4 1 5 4 12 1 3 2 1 2 0 11 1 1 4 1 4 0 4 1 2 4 1 5 1 4 8 4 5 1 2 1 7 1 2 5 1 2 1 10 0 4 0 1 4 0 98 1 3 1 0 2 0 7 2 3 2 1 4 0 98 2 2 3 1 5 1 9 1 3 4 1 1 1 3 1 3 5 1 2 1 2 1 1 4 1 1 1 3 0 3 0 0 1 1 1 1 2 2 1 5 3 12 1 4 2 1 2 0 9 1 4 2 0 4 1 2 1 2 4 1 4 0 12 0 3 0 0 2 1 3 0 2 0 0 3 1 2 0 3 0 1 1 3 12 0 1 0 1 3 1 8 2 2 2 1 4 3 8 1 3 3 1 2 1 98 2 3 4 0 1 1 15 0 3 0 1 2 3 11 1 4 3 1 4 0 2 1 4 2 1 1 1 6 0 3 0 1 1 1 6 1 3 3 0 3 4 12 1 2 3 0 4 1 7 1 3 2 0 2 0 2 0 2 0 0 3 1 7 1 4 1 1 2 1 11 2 2 5 1 2 1 98 0 2 0 1 4 2 98 1 4 4 0 5 3 8 1 4 8 1 5 2 10 1 4 3 0 4 0 1 1 8 4 1 5 1 2 0 2 0 0 2 2 13 0 4 0 1 2 0 9 1 2 2 1 3 1 7 0 2 0 1 5 0 8 1 2 2 1 5 0 7 0 2 0 1 4 1 5 0 2 0 0 2 1 1 0 2 0 1 1 1 1 0 3 0 1 5 1 9 0 4 0 1 4 1 5 1 3 4 1 3 4 15 2 1 4 0 2 2 98 0 3 0 0 4 1 10 0 2 0 1 1 0 98 1 9 9 0 1 2 7 2 1 2 1 1 1 5 0 1 0 1 2 1 14 1 1 4 1 3 1 12 0 2 0 1 4 4 15 1 4 5 1 4 4 6 1 3 5 1 2 3 11 0 2 0 1 2 3 13 0 1 0 1 3 4 11 0 3 0 1 3 4 13 2 2 4 1 1 3 13 0 3 0 1 3 1 11 1 1 5 0 4 0 8 1 3 2 1 1 1 5 1 1 4 1 4 1 2 0 2 0 0 2 1 3 1 3 1 0 3 0 98 1 2 1 1 5 1 6 0 2 0 0 1 1 2 1 1 2 0 4 1 4 1 2 2 0 4 0 98 0 1 0 0 4 1 9 1 1 2 1 1 1 4 1 2 3 0 1 1 11 0 2 0 1 4 0 2 1 3 2 0 3 0 7 1 3 2 1 5 1 6 1 1 2 0 3 1 12 1 1 3 0 1 0 1 0 4 0 0 3 1 13 1 1 5 1 2 2 11 0 2 0 0 2 1 9 1 2 2 1 4 1 98 0 1 0 1 4 1 4 8 8 3 0 4 1 11 1 2 4 0 1 1 15 1 2 2 1 5 1 6 2 3 2 1 3 3 10 2 2 5 1 4 1 7 1 2 2 1 5 3 7 2 2 5 1 4 0 5 2 1 4 0 2 1 10 0 2 0 1 3 0 98 2 2 2 1 1 1 6 1 4 2 0 4 4 11 1 2 3 1 3 3 2 0 1 0 1 1 1 5 1 3 4 1 2 1 10 0 1 0 1 3 1 2 1 2 2 0 3 0 98 0 1 0 1 5 1 12 1 3 2 1 1 0 2 0 2 0 0 2 1 13 0 2 0 1 3 0 8 1 3 4 1 1 1 98 0 2 0 1 3 1 10 1 2 4 1 3 1 4 0 3 0 0 4 4 11 0 1 0 1 3 1 9 1 2 4 1 1 4 10 1 2 2 1 1 1 5 1 2 2 1 1 1 10 0 4 0 1 1 3 13 1 2 4 0 3 1 11 0 1 0 1 1 4 10 1 3 2 0 2 1 6 1 2 2 1 1 3 8 1 2 5 1 3 3 4 2 2 2 1 5 1 98 2 1 5 1 2 1 4 1 2 1 1 4 4 98 1 3 3 1 4 1 98 1 2 2 1 2 2 98 0 1 0 1 2 0 1 2 2 4 0 1 0 5 0 1 0 1 4 4 9 2 2 2 1 3 0 4 1 2 2 1 2 2 13 0 2 0 1 2 1 14 1 2 2 0 4 2 14 1 2 4 1 4 1 98 1 2 2 1 4 1 12 0 3 0 0 4 4 9 1 1 2 1 3 1 13 0 3 0 1 3 3 8 1 2 4 1 1 0 4 0 1 0 1 5 1 98 1 2 2 1 5 0 1 0 2 0 0 4 1 5 1 2 3 0 1 1 5 0 1 0 1 2 0 98 0 1 0 0 2 1 3 1 2 2 0 4 1 8 1 2 1 1 3 3 98 1 8 3 1 4 0 1 0 1 0 0 3 0 3 8 8 8 0 4 1 12 1 1 2 1 4 3 98 1 9 3 1 2 0 2 0 2 0 0 1 1 4 1 2 2 1 1 0 4 1 3 2 1 3 0 1 1 3 2 1 4 4 10 1 4 4 1 1 1 9 1 2 4 1 4 3 12 0 2 0 1 4 0 6 0 1 0 1 5 1 98 1 4 2 1 3 3 11 0 2 0 1 2 3 10 1 4 2 1 3 2 98 1 3 2 1 5 4 15 0 4 0 1 3 4 11 1 2 3 1 3 3 10 0 4 0 0 2 1 1 1 2 4 1 2 4 11 1 4 2 1 1 0 2 0 4 0 0 5 0 3 1 3 2 0 3 1 12 1 2 1
Names of X columns:
DIVORCE Calc_age_new DEGREE INCOME06_new SEXEDUC RELPERSN DIVBEST
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R Code
library(brglm) roc.plot <- function (sd, sdc, newplot = TRUE, ...) { sall <- sort(c(sd, sdc)) sens <- 0 specc <- 0 for (i in length(sall):1) { sens <- c(sens, mean(sd >= sall[i], na.rm = T)) specc <- c(specc, mean(sdc >= sall[i], na.rm = T)) } if (newplot) { plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = 'l', xlab = '1-specificity', ylab = 'sensitivity', main = 'ROC plot', ...) abline(0, 1) } else lines(specc, sens, ...) npoints <- length(sens) area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] - specc[-npoints])) lift <- (sens - specc)[-1] cutoff <- sall[lift == max(lift)][1] sensopt <- sens[-1][lift == max(lift)][1] specopt <- 1 - specc[-1][lift == max(lift)][1] list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt) } roc.analysis <- function (object, newdata = NULL, newplot = TRUE, ...) { if (is.null(newdata)) { sd <- object$fitted[object$y == 1] sdc <- object$fitted[object$y == 0] } else { sd <- predict(object, newdata, type = 'response')[newdata$y == 1] sdc <- predict(object, newdata, type = 'response')[newdata$y == 0] } roc.plot(sd, sdc, newplot, ...) } hosmerlem <- function (y, yhat, g = 10) { cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0, 1, 1/g)), include.lowest = T) obs <- xtabs(cbind(1 - y, y) ~ cutyhat) expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat) chisq <- sum((obs - expect)^2/expect) P <- 1 - pchisq(chisq, g - 2) c('X^2' = chisq, Df = g - 2, 'P(>Chi)' = P) } x <- as.data.frame(t(y)) r <- brglm(x) summary(r) rc <- summary(r)$coeff try(hm <- hosmerlem(y[1,],r$fitted.values),silent=T) try(hm,silent=T) bitmap(file='test0.png') ra <- roc.analysis(r) dev.off() te <- array(0,dim=c(2,99)) for (i in 1:99) { threshold <- i / 100 numcorr1 <- 0 numfaul1 <- 0 numcorr0 <- 0 numfaul0 <- 0 for (j in 1:length(r$fitted.values)) { if (y[1,j] > 0.99) { if (r$fitted.values[j] >= threshold) numcorr1 = numcorr1 + 1 else numfaul1 = numfaul1 + 1 } else { if (r$fitted.values[j] < threshold) numcorr0 = numcorr0 + 1 else numfaul0 = numfaul0 + 1 } } te[1,i] <- numfaul1 / (numfaul1 + numcorr1) te[2,i] <- numfaul0 / (numfaul0 + numcorr0) } bitmap(file='test1.png') op <- par(mfrow=c(2,2)) plot((1:99)/100,te[1,],xlab='Threshold',ylab='Type I error', main='1 - Specificity') plot((1:99)/100,te[2,],xlab='Threshold',ylab='Type II error', main='1 - Sensitivity') plot(te[1,],te[2,],xlab='Type I error',ylab='Type II error', main='(1-Sens.) vs (1-Spec.)') plot((1:99)/100,te[1,]+te[2,],xlab='Threshold',ylab='Sum of Type I & II error', main='(1-Sens.) + (1-Spec.)') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Coefficients of Bias-Reduced Logistic Regression',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,'2-sided p-value',header=TRUE) a<-table.row.end(a) for (i in 1:length(rc[,1])) { a<-table.row.start(a) a<-table.element(a,labels(rc)[[1]][i],header=TRUE) a<-table.element(a,rc[i,1]) a<-table.element(a,rc[i,2]) a<-table.element(a,rc[i,3]) a<-table.element(a,2*(1-pt(abs(rc[i,3]),r$df.residual))) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Summary of Bias-Reduced Logistic Regression',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Deviance',1,TRUE) a<-table.element(a,r$deviance) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Penalized deviance',1,TRUE) a<-table.element(a,r$penalized.deviance) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual Degrees of Freedom',1,TRUE) a<-table.element(a,r$df.residual) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'ROC Area',1,TRUE) a<-table.element(a,ra$area) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Hosmer–Lemeshow test',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Chi-square',1,TRUE) phm <- array('NA',dim=3) for (i in 1:3) { try(phm[i] <- hm[i],silent=T) } a<-table.element(a,phm[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Degrees of Freedom',1,TRUE) a<-table.element(a,phm[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'P(>Chi)',1,TRUE) a<-table.element(a,phm[3]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Fit of Logistic Regression',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Index',1,TRUE) a<-table.element(a,'Actual',1,TRUE) a<-table.element(a,'Fitted',1,TRUE) a<-table.element(a,'Error',1,TRUE) a<-table.row.end(a) for (i in 1:length(r$fitted.values)) { a<-table.row.start(a) a<-table.element(a,i,1,TRUE) a<-table.element(a,y[1,i]) a<-table.element(a,r$fitted.values[i]) a<-table.element(a,y[1,i]-r$fitted.values[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Type I & II errors for various threshold values',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Threshold',1,TRUE) a<-table.element(a,'Type I',1,TRUE) a<-table.element(a,'Type II',1,TRUE) a<-table.row.end(a) for (i in 1:99) { a<-table.row.start(a) a<-table.element(a,i/100,1,TRUE) a<-table.element(a,te[1,i]) a<-table.element(a,te[2,i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable3.tab')
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1 seconds
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Big Analytics Cloud Computing Center
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