III Prova de Biometria - Parte 3
Victor Viana
19 de junho de 2018
III Avaliação - Parte 3
Nessa parte do terceiro exercício proposto, temos o intuito de analisar o banco de dados “Pima Indians Diabetes Database”, utilizando Testes de Diagnósticos.Esse banco de dados é composto por 9 variáveis, onde iremos dar preferência à variável “plas”
Fazendo uma breve análise descritiva da variável “plas” que significa “Plasma glucose tolerance test”, temos que o seu valor mínimo é 0 e seu valor máximo é 199.
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0,165,76,43,255,47.9,0.259,26,0,
0,117,0,0,0,33.8,0.932,44,0,
5,115,76,0,0,31.2,0.343,44,1,
9,152,78,34,171,34.2,0.893,33,1,
7,178,84,0,0,39.9,0.331,41,1,
1,130,70,13,105,25.9,0.472,22,0,
1,95,74,21,73,25.9,0.673,36,0,
1,0,68,35,0,32,0.389,22,0,
5,122,86,0,0,34.7,0.29,33,0,
8,95,72,0,0,36.8,0.485,57,0,
8,126,88,36,108,38.5,0.349,49,0,
1,139,46,19,83,28.7,0.654,22,0,
3,116,0,0,0,23.5,0.187,23,0,
3,99,62,19,74,21.8,0.279,26,0,
5,0,80,32,0,41,0.346,37,1,
4,92,80,0,0,42.2,0.237,29,0,
4,137,84,0,0,31.2,0.252,30,0,
3,61,82,28,0,34.4,0.243,46,0,
1,90,62,12,43,27.2,0.58,24,0,
3,90,78,0,0,42.7,0.559,21,0,
9,165,88,0,0,30.4,0.302,49,1,
1,125,50,40,167,33.3,0.962,28,1,
13,129,0,30,0,39.9,0.569,44,1,
12,88,74,40,54,35.3,0.378,48,0,
1,196,76,36,249,36.5,0.875,29,1,
5,189,64,33,325,31.2,0.583,29,1,
5,158,70,0,0,29.8,0.207,63,0,
5,103,108,37,0,39.2,0.305,65,0,
4,146,78,0,0,38.5,0.52,67,1,
4,147,74,25,293,34.9,0.385,30,0,
5,99,54,28,83,34,0.499,30,0,
6,124,72,0,0,27.6,0.368,29,1,
0,101,64,17,0,21,0.252,21,0,
3,81,86,16,66,27.5,0.306,22,0,
1,133,102,28,140,32.8,0.234,45,1,
3,173,82,48,465,38.4,2.137,25,1,
0,118,64,23,89,0,1.731,21,0,
0,84,64,22,66,35.8,0.545,21,0,
2,105,58,40,94,34.9,0.225,25,0,
2,122,52,43,158,36.2,0.816,28,0,
12,140,82,43,325,39.2,0.528,58,1,
0,98,82,15,84,25.2,0.299,22,0,
1,87,60,37,75,37.2,0.509,22,0,
4,156,75,0,0,48.3,0.238,32,1,
0,93,100,39,72,43.4,1.021,35,0,
1,107,72,30,82,30.8,0.821,24,0,
0,105,68,22,0,20,0.236,22,0,
1,109,60,8,182,25.4,0.947,21,0,
1,90,62,18,59,25.1,1.268,25,0,
1,125,70,24,110,24.3,0.221,25,0,
1,119,54,13,50,22.3,0.205,24,0,
5,116,74,29,0,32.3,0.66,35,1,
8,105,100,36,0,43.3,0.239,45,1,
5,144,82,26,285,32,0.452,58,1,
3,100,68,23,81,31.6,0.949,28,0,
1,100,66,29,196,32,0.444,42,0,
5,166,76,0,0,45.7,0.34,27,1,
1,131,64,14,415,23.7,0.389,21,0,
4,116,72,12,87,22.1,0.463,37,0,
4,158,78,0,0,32.9,0.803,31,1,
2,127,58,24,275,27.7,1.6,25,0,
3,96,56,34,115,24.7,0.944,39,0,
0,131,66,40,0,34.3,0.196,22,1,
3,82,70,0,0,21.1,0.389,25,0,
3,193,70,31,0,34.9,0.241,25,1,
4,95,64,0,0,32,0.161,31,1,
6,137,61,0,0,24.2,0.151,55,0,
5,136,84,41,88,35,0.286,35,1,
9,72,78,25,0,31.6,0.28,38,0,
5,168,64,0,0,32.9,0.135,41,1,
2,123,48,32,165,42.1,0.52,26,0,
4,115,72,0,0,28.9,0.376,46,1,
0,101,62,0,0,21.9,0.336,25,0,
8,197,74,0,0,25.9,1.191,39,1,
1,172,68,49,579,42.4,0.702,28,1,
6,102,90,39,0,35.7,0.674,28,0,
1,112,72,30,176,34.4,0.528,25,0,
1,143,84,23,310,42.4,1.076,22,0,
1,143,74,22,61,26.2,0.256,21,0,
0,138,60,35,167,34.6,0.534,21,1,
3,173,84,33,474,35.7,0.258,22,1,
1,97,68,21,0,27.2,1.095,22,0,
4,144,82,32,0,38.5,0.554,37,1,
1,83,68,0,0,18.2,0.624,27,0,
3,129,64,29,115,26.4,0.219,28,1,
1,119,88,41,170,45.3,0.507,26,0,
2,94,68,18,76,26,0.561,21,0,
0,102,64,46,78,40.6,0.496,21,0,
2,115,64,22,0,30.8,0.421,21,0,
8,151,78,32,210,42.9,0.516,36,1,
4,184,78,39,277,37,0.264,31,1,
0,94,0,0,0,0,0.256,25,0,
1,181,64,30,180,34.1,0.328,38,1,
0,135,94,46,145,40.6,0.284,26,0,
1,95,82,25,180,35,0.233,43,1,
2,99,0,0,0,22.2,0.108,23,0,
3,89,74,16,85,30.4,0.551,38,0,
1,80,74,11,60,30,0.527,22,0,
2,139,75,0,0,25.6,0.167,29,0,
1,90,68,8,0,24.5,1.138,36,0,
0,141,0,0,0,42.4,0.205,29,1,
12,140,85,33,0,37.4,0.244,41,0,
5,147,75,0,0,29.9,0.434,28,0,
1,97,70,15,0,18.2,0.147,21,0,
6,107,88,0,0,36.8,0.727,31,0,
0,189,104,25,0,34.3,0.435,41,1,
2,83,66,23,50,32.2,0.497,22,0,
4,117,64,27,120,33.2,0.23,24,0,
8,108,70,0,0,30.5,0.955,33,1,
4,117,62,12,0,29.7,0.38,30,1,
0,180,78,63,14,59.4,2.42,25,1,
1,100,72,12,70,25.3,0.658,28,0,
0,95,80,45,92,36.5,0.33,26,0,
0,104,64,37,64,33.6,0.51,22,1,
0,120,74,18,63,30.5,0.285,26,0,
1,82,64,13,95,21.2,0.415,23,0,
2,134,70,0,0,28.9,0.542,23,1,
0,91,68,32,210,39.9,0.381,25,0,
2,119,0,0,0,19.6,0.832,72,0,
2,100,54,28,105,37.8,0.498,24,0,
14,175,62,30,0,33.6,0.212,38,1,
1,135,54,0,0,26.7,0.687,62,0,
5,86,68,28,71,30.2,0.364,24,0,
10,148,84,48,237,37.6,1.001,51,1,
9,134,74,33,60,25.9,0.46,81,0,
9,120,72,22,56,20.8,0.733,48,0,
1,71,62,0,0,21.8,0.416,26,0,
8,74,70,40,49,35.3,0.705,39,0,
5,88,78,30,0,27.6,0.258,37,0,
10,115,98,0,0,24,1.022,34,0,
0,124,56,13,105,21.8,0.452,21,0,
0,74,52,10,36,27.8,0.269,22,0,
0,97,64,36,100,36.8,0.6,25,0,
8,120,0,0,0,30,0.183,38,1,
6,154,78,41,140,46.1,0.571,27,0,
1,144,82,40,0,41.3,0.607,28,0,
0,137,70,38,0,33.2,0.17,22,0,
0,119,66,27,0,38.8,0.259,22,0,
7,136,90,0,0,29.9,0.21,50,0,
4,114,64,0,0,28.9,0.126,24,0,
0,137,84,27,0,27.3,0.231,59,0,
2,105,80,45,191,33.7,0.711,29,1,
7,114,76,17,110,23.8,0.466,31,0,
8,126,74,38,75,25.9,0.162,39,0,
4,132,86,31,0,28,0.419,63,0,
3,158,70,30,328,35.5,0.344,35,1,
0,123,88,37,0,35.2,0.197,29,0,
4,85,58,22,49,27.8,0.306,28,0,
0,84,82,31,125,38.2,0.233,23,0,
0,145,0,0,0,44.2,0.63,31,1,
0,135,68,42,250,42.3,0.365,24,1,
1,139,62,41,480,40.7,0.536,21,0,
0,173,78,32,265,46.5,1.159,58,0,
4,99,72,17,0,25.6,0.294,28,0,
8,194,80,0,0,26.1,0.551,67,0,
2,83,65,28,66,36.8,0.629,24,0,
2,89,90,30,0,33.5,0.292,42,0,
4,99,68,38,0,32.8,0.145,33,0,
4,125,70,18,122,28.9,1.144,45,1,
3,80,0,0,0,0,0.174,22,0,
6,166,74,0,0,26.6,0.304,66,0,
5,110,68,0,0,26,0.292,30,0,
2,81,72,15,76,30.1,0.547,25,0,
7,195,70,33,145,25.1,0.163,55,1,
6,154,74,32,193,29.3,0.839,39,0,
2,117,90,19,71,25.2,0.313,21,0,
3,84,72,32,0,37.2,0.267,28,0,
6,0,68,41,0,39,0.727,41,1,
7,94,64,25,79,33.3,0.738,41,0,
3,96,78,39,0,37.3,0.238,40,0,
10,75,82,0,0,33.3,0.263,38,0,
0,180,90,26,90,36.5,0.314,35,1,
1,130,60,23,170,28.6,0.692,21,0,
2,84,50,23,76,30.4,0.968,21,0,
8,120,78,0,0,25,0.409,64,0,
12,84,72,31,0,29.7,0.297,46,1,
0,139,62,17,210,22.1,0.207,21,0,
9,91,68,0,0,24.2,0.2,58,0,
2,91,62,0,0,27.3,0.525,22,0,
3,99,54,19,86,25.6,0.154,24,0,
3,163,70,18,105,31.6,0.268,28,1,
9,145,88,34,165,30.3,0.771,53,1,
7,125,86,0,0,37.6,0.304,51,0,
13,76,60,0,0,32.8,0.18,41,0,
6,129,90,7,326,19.6,0.582,60,0,
2,68,70,32,66,25,0.187,25,0,
3,124,80,33,130,33.2,0.305,26,0,
6,114,0,0,0,0,0.189,26,0,
9,130,70,0,0,34.2,0.652,45,1,
3,125,58,0,0,31.6,0.151,24,0,
3,87,60,18,0,21.8,0.444,21,0,
1,97,64,19,82,18.2,0.299,21,0,
3,116,74,15,105,26.3,0.107,24,0,
0,117,66,31,188,30.8,0.493,22,0,
0,111,65,0,0,24.6,0.66,31,0,
2,122,60,18,106,29.8,0.717,22,0,
0,107,76,0,0,45.3,0.686,24,0,
1,86,66,52,65,41.3,0.917,29,0,
6,91,0,0,0,29.8,0.501,31,0,
1,77,56,30,56,33.3,1.251,24,0,
4,132,0,0,0,32.9,0.302,23,1,
0,105,90,0,0,29.6,0.197,46,0,
0,57,60,0,0,21.7,0.735,67,0,
0,127,80,37,210,36.3,0.804,23,0,
3,129,92,49,155,36.4,0.968,32,1,
8,100,74,40,215,39.4,0.661,43,1,
3,128,72,25,190,32.4,0.549,27,1,
10,90,85,32,0,34.9,0.825,56,1,
4,84,90,23,56,39.5,0.159,25,0,
1,88,78,29,76,32,0.365,29,0,
8,186,90,35,225,34.5,0.423,37,1,
5,187,76,27,207,43.6,1.034,53,1,
4,131,68,21,166,33.1,0.16,28,0,
1,164,82,43,67,32.8,0.341,50,0,
4,189,110,31,0,28.5,0.68,37,0,
1,116,70,28,0,27.4,0.204,21,0,
3,84,68,30,106,31.9,0.591,25,0,
6,114,88,0,0,27.8,0.247,66,0,
1,88,62,24,44,29.9,0.422,23,0,
1,84,64,23,115,36.9,0.471,28,0,
7,124,70,33,215,25.5,0.161,37,0,
1,97,70,40,0,38.1,0.218,30,0,
8,110,76,0,0,27.8,0.237,58,0,
11,103,68,40,0,46.2,0.126,42,0,
11,85,74,0,0,30.1,0.3,35,0,
6,125,76,0,0,33.8,0.121,54,1,
0,198,66,32,274,41.3,0.502,28,1,
1,87,68,34,77,37.6,0.401,24,0,
6,99,60,19,54,26.9,0.497,32,0,
0,91,80,0,0,32.4,0.601,27,0,
2,95,54,14,88,26.1,0.748,22,0,
1,99,72,30,18,38.6,0.412,21,0,
6,92,62,32,126,32,0.085,46,0,
4,154,72,29,126,31.3,0.338,37,0,
0,121,66,30,165,34.3,0.203,33,1,
3,78,70,0,0,32.5,0.27,39,0,
2,130,96,0,0,22.6,0.268,21,0,
3,111,58,31,44,29.5,0.43,22,0,
2,98,60,17,120,34.7,0.198,22,0,
1,143,86,30,330,30.1,0.892,23,0,
1,119,44,47,63,35.5,0.28,25,0,
6,108,44,20,130,24,0.813,35,0,
2,118,80,0,0,42.9,0.693,21,1,
10,133,68,0,0,27,0.245,36,0,
2,197,70,99,0,34.7,0.575,62,1,
0,151,90,46,0,42.1,0.371,21,1,
6,109,60,27,0,25,0.206,27,0,
12,121,78,17,0,26.5,0.259,62,0,
8,100,76,0,0,38.7,0.19,42,0,
8,124,76,24,600,28.7,0.687,52,1,
1,93,56,11,0,22.5,0.417,22,0,
8,143,66,0,0,34.9,0.129,41,1,
6,103,66,0,0,24.3,0.249,29,0,
3,176,86,27,156,33.3,1.154,52,1,
0,73,0,0,0,21.1,0.342,25,0,
11,111,84,40,0,46.8,0.925,45,1,
2,112,78,50,140,39.4,0.175,24,0,
3,132,80,0,0,34.4,0.402,44,1,
2,82,52,22,115,28.5,1.699,25,0,
6,123,72,45,230,33.6,0.733,34,0,
0,188,82,14,185,32,0.682,22,1,
0,67,76,0,0,45.3,0.194,46,0,
1,89,24,19,25,27.8,0.559,21,0,
1,173,74,0,0,36.8,0.088,38,1,
1,109,38,18,120,23.1,0.407,26,0,
1,108,88,19,0,27.1,0.4,24,0,
6,96,0,0,0,23.7,0.19,28,0,
1,124,74,36,0,27.8,0.1,30,0,
7,150,78,29,126,35.2,0.692,54,1,
4,183,0,0,0,28.4,0.212,36,1,
1,124,60,32,0,35.8,0.514,21,0,
1,181,78,42,293,40,1.258,22,1,
1,92,62,25,41,19.5,0.482,25,0,
0,152,82,39,272,41.5,0.27,27,0,
1,111,62,13,182,24,0.138,23,0,
3,106,54,21,158,30.9,0.292,24,0,
3,174,58,22,194,32.9,0.593,36,1,
7,168,88,42,321,38.2,0.787,40,1,
6,105,80,28,0,32.5,0.878,26,0,
11,138,74,26,144,36.1,0.557,50,1,
3,106,72,0,0,25.8,0.207,27,0,
6,117,96,0,0,28.7,0.157,30,0,
2,68,62,13,15,20.1,0.257,23,0,
9,112,82,24,0,28.2,1.282,50,1,
0,119,0,0,0,32.4,0.141,24,1,
2,112,86,42,160,38.4,0.246,28,0,
2,92,76,20,0,24.2,1.698,28,0,
6,183,94,0,0,40.8,1.461,45,0,
0,94,70,27,115,43.5,0.347,21,0,
2,108,64,0,0,30.8,0.158,21,0,
4,90,88,47,54,37.7,0.362,29,0,
0,125,68,0,0,24.7,0.206,21,0,
0,132,78,0,0,32.4,0.393,21,0,
5,128,80,0,0,34.6,0.144,45,0,
4,94,65,22,0,24.7,0.148,21,0,
7,114,64,0,0,27.4,0.732,34,1,
0,102,78,40,90,34.5,0.238,24,0,
2,111,60,0,0,26.2,0.343,23,0,
1,128,82,17,183,27.5,0.115,22,0,
10,92,62,0,0,25.9,0.167,31,0,
13,104,72,0,0,31.2,0.465,38,1,
5,104,74,0,0,28.8,0.153,48,0,
2,94,76,18,66,31.6,0.649,23,0,
7,97,76,32,91,40.9,0.871,32,1,
1,100,74,12,46,19.5,0.149,28,0,
0,102,86,17,105,29.3,0.695,27,0,
4,128,70,0,0,34.3,0.303,24,0,
6,147,80,0,0,29.5,0.178,50,1,
4,90,0,0,0,28,0.61,31,0,
3,103,72,30,152,27.6,0.73,27,0,
2,157,74,35,440,39.4,0.134,30,0,
1,167,74,17,144,23.4,0.447,33,1,
0,179,50,36,159,37.8,0.455,22,1,
11,136,84,35,130,28.3,0.26,42,1,
0,107,60,25,0,26.4,0.133,23,0,
1,91,54,25,100,25.2,0.234,23,0,
1,117,60,23,106,33.8,0.466,27,0,
5,123,74,40,77,34.1,0.269,28,0,
2,120,54,0,0,26.8,0.455,27,0,
1,106,70,28,135,34.2,0.142,22,0,
2,155,52,27,540,38.7,0.24,25,1,
2,101,58,35,90,21.8,0.155,22,0,
1,120,80,48,200,38.9,1.162,41,0,
11,127,106,0,0,39,0.19,51,0,
3,80,82,31,70,34.2,1.292,27,1,
10,162,84,0,0,27.7,0.182,54,0,
1,199,76,43,0,42.9,1.394,22,1,
8,167,106,46,231,37.6,0.165,43,1,
9,145,80,46,130,37.9,0.637,40,1,
6,115,60,39,0,33.7,0.245,40,1,
1,112,80,45,132,34.8,0.217,24,0,
4,145,82,18,0,32.5,0.235,70,1,
10,111,70,27,0,27.5,0.141,40,1,
6,98,58,33,190,34,0.43,43,0,
9,154,78,30,100,30.9,0.164,45,0,
6,165,68,26,168,33.6,0.631,49,0,
1,99,58,10,0,25.4,0.551,21,0,
10,68,106,23,49,35.5,0.285,47,0,
3,123,100,35,240,57.3,0.88,22,0,
8,91,82,0,0,35.6,0.587,68,0,
6,195,70,0,0,30.9,0.328,31,1,
9,156,86,0,0,24.8,0.23,53,1,
0,93,60,0,0,35.3,0.263,25,0,
3,121,52,0,0,36,0.127,25,1,
2,101,58,17,265,24.2,0.614,23,0,
2,56,56,28,45,24.2,0.332,22,0,
0,162,76,36,0,49.6,0.364,26,1,
0,95,64,39,105,44.6,0.366,22,0,
4,125,80,0,0,32.3,0.536,27,1,
5,136,82,0,0,0,0.64,69,0,
2,129,74,26,205,33.2,0.591,25,0,
3,130,64,0,0,23.1,0.314,22,0,
1,107,50,19,0,28.3,0.181,29,0,
1,140,74,26,180,24.1,0.828,23,0,
1,144,82,46,180,46.1,0.335,46,1,
8,107,80,0,0,24.6,0.856,34,0,
13,158,114,0,0,42.3,0.257,44,1,
2,121,70,32,95,39.1,0.886,23,0,
7,129,68,49,125,38.5,0.439,43,1,
2,90,60,0,0,23.5,0.191,25,0,
7,142,90,24,480,30.4,0.128,43,1,
3,169,74,19,125,29.9,0.268,31,1,
0,99,0,0,0,25,0.253,22,0,
4,127,88,11,155,34.5,0.598,28,0,
4,118,70,0,0,44.5,0.904,26,0,
2,122,76,27,200,35.9,0.483,26,0,
6,125,78,31,0,27.6,0.565,49,1,
1,168,88,29,0,35,0.905,52,1,
2,129,0,0,0,38.5,0.304,41,0,
4,110,76,20,100,28.4,0.118,27,0,
6,80,80,36,0,39.8,0.177,28,0,
10,115,0,0,0,0,0.261,30,1,
2,127,46,21,335,34.4,0.176,22,0,
9,164,78,0,0,32.8,0.148,45,1,
2,93,64,32,160,38,0.674,23,1,
3,158,64,13,387,31.2,0.295,24,0,
5,126,78,27,22,29.6,0.439,40,0,
10,129,62,36,0,41.2,0.441,38,1,
0,134,58,20,291,26.4,0.352,21,0,
3,102,74,0,0,29.5,0.121,32,0,
7,187,50,33,392,33.9,0.826,34,1,
3,173,78,39,185,33.8,0.97,31,1,
10,94,72,18,0,23.1,0.595,56,0,
1,108,60,46,178,35.5,0.415,24,0,
5,97,76,27,0,35.6,0.378,52,1,
4,83,86,19,0,29.3,0.317,34,0,
1,114,66,36,200,38.1,0.289,21,0,
1,149,68,29,127,29.3,0.349,42,1,
5,117,86,30,105,39.1,0.251,42,0,
1,111,94,0,0,32.8,0.265,45,0,
4,112,78,40,0,39.4,0.236,38,0,
1,116,78,29,180,36.1,0.496,25,0,
0,141,84,26,0,32.4,0.433,22,0,
2,175,88,0,0,22.9,0.326,22,0,
2,92,52,0,0,30.1,0.141,22,0,
3,130,78,23,79,28.4,0.323,34,1,
8,120,86,0,0,28.4,0.259,22,1,
2,174,88,37,120,44.5,0.646,24,1,
2,106,56,27,165,29,0.426,22,0,
2,105,75,0,0,23.3,0.56,53,0,
4,95,60,32,0,35.4,0.284,28,0,
0,126,86,27,120,27.4,0.515,21,0,
8,65,72,23,0,32,0.6,42,0,
2,99,60,17,160,36.6,0.453,21,0,
1,102,74,0,0,39.5,0.293,42,1,
11,120,80,37,150,42.3,0.785,48,1,
3,102,44,20,94,30.8,0.4,26,0,
1,109,58,18,116,28.5,0.219,22,0,
9,140,94,0,0,32.7,0.734,45,1,
13,153,88,37,140,40.6,1.174,39,0,
12,100,84,33,105,30,0.488,46,0,
1,147,94,41,0,49.3,0.358,27,1,
1,81,74,41,57,46.3,1.096,32,0,
3,187,70,22,200,36.4,0.408,36,1,
6,162,62,0,0,24.3,0.178,50,1,
4,136,70,0,0,31.2,1.182,22,1,
1,121,78,39,74,39,0.261,28,0,
3,108,62,24,0,26,0.223,25,0,
0,181,88,44,510,43.3,0.222,26,1,
8,154,78,32,0,32.4,0.443,45,1,
1,128,88,39,110,36.5,1.057,37,1,
7,137,90,41,0,32,0.391,39,0,
0,123,72,0,0,36.3,0.258,52,1,
1,106,76,0,0,37.5,0.197,26,0,
6,190,92,0,0,35.5,0.278,66,1,
2,88,58,26,16,28.4,0.766,22,0,
9,170,74,31,0,44,0.403,43,1,
9,89,62,0,0,22.5,0.142,33,0,
10,101,76,48,180,32.9,0.171,63,0,
2,122,70,27,0,36.8,0.34,27,0,
5,121,72,23,112,26.2,0.245,30,0,
1,126,60,0,0,30.1,0.349,47,1,
1,93,70,31,0,30.4,0.315,23,0),nrow=768, ncol = 9, byrow=T))
colnames(dat) <- c("preg", "plas","pres","skin","insu","mass","pedi","age","teste")
attach(dat)
names(dat)## [1] "preg" "plas" "pres" "skin" "insu" "mass" "pedi" "age" "teste"var <- dat$plas
summary(var)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 99.0 117.0 120.9 140.2 199.0length(var)## [1] 768table(var)## var
## 0 44 56 57 61 62 65 67 68 71 72 73 74 75 76 77 78 79
## 5 1 1 2 1 1 1 1 3 4 1 3 4 2 2 2 4 3
## 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
## 6 6 3 6 10 7 3 7 9 6 11 9 9 7 7 13 8 9
## 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115
## 3 17 17 9 13 9 6 13 14 11 13 12 6 14 13 5 11 10
## 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133
## 7 11 6 11 11 6 12 9 11 14 9 5 11 14 7 5 5 5
## 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151
## 6 4 8 8 5 8 5 5 5 6 7 5 9 7 4 1 3 6
## 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169
## 4 2 6 5 3 2 8 2 1 3 6 3 3 4 3 3 4 1
## 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 186 187 188
## 2 3 1 6 2 2 2 1 1 5 5 5 1 3 3 1 4 2
## 189 190 191 193 194 195 196 197 198 199
## 4 1 1 2 3 2 3 4 1 1Agora dividimos os valores que a variável “plas”" pode assumir em faixas,cruzando com a variável “teste” que exprime se o paciente possui diabetes ou não, da seguinte forma:
intervalo <- cut(var,breaks=c(0,20,40,60,80,100,120,140,160,180,max(var)))
intervalo ## [1] (140,160] (80,100] (180,199] (80,100] (120,140] (100,120] (60,80]
## [8] (100,120] (180,199] (120,140] (100,120] (160,180] (120,140] (180,199]
## [15] (160,180] (80,100] (100,120] (100,120] (100,120] (100,120] (120,140]
## [22] (80,100] (180,199] (100,120] (140,160] (120,140] (140,160] (80,100]
## [29] (140,160] (100,120] (100,120] (140,160] (80,100] (80,100] (120,140]
## [36] (100,120] (120,140] (100,120] (80,100] (100,120] (160,180] (120,140]
## [43] (100,120] (160,180] (140,160] (160,180] (140,160] (60,80] (100,120]
## [50] (100,120] (100,120] (100,120] (80,100] (160,180] (140,160] (60,80]
## [57] (180,199] (80,100] (140,160] (100,120] (80,100] (120,140] (40,60]
## [64] (140,160] (100,120] (80,100] (100,120] (100,120] (80,100] (140,160]
## [71] (80,100] (120,140] (120,140] (120,140] (60,80] <NA> (60,80]
## [78] (80,100] (120,140] (100,120] (100,120] (60,80] (80,100] (100,120]
## [85] (120,140] (100,120] (100,120] (80,100] (120,140] (100,120] (60,80]
## [92] (120,140] (80,100] (120,140] (140,160] (140,160] (80,100] (60,80]
## [99] (80,100] (120,140] (160,180] (140,160] (120,140] (80,100] (80,100]
## [106] (120,140] (80,100] (140,160] (80,100] (80,100] (160,180] (140,160]
## [113] (80,100] (60,80] (140,160] (140,160] (120,140] (60,80] (80,100]
## [120] (80,100] (160,180] (100,120] (100,120] (120,140] (100,120] (80,100]
## [127] (100,120] (100,120] (100,120] (100,120] (160,180] (120,140] (160,180]
## [134] (80,100] (80,100] (120,140] (80,100] (80,100] (120,140] (100,120]
## [141] (120,140] (100,120] (100,120] (100,120] (140,160] (100,120] (40,60]
## [148] (100,120] (140,160] (80,100] (120,140] (100,120] (140,160] (140,160]
## [155] (180,199] (140,160] (80,100] (100,120] (80,100] (160,180] (140,160]
## [162] (100,120] (100,120] (80,100] (120,140] (100,120] (140,160] (100,120]
## [169] (100,120] (100,120] (100,120] (120,140] (80,100] (60,80] (60,80]
## [176] (160,180] (80,100] (120,140] (140,160] (120,140] (80,100] (100,120]
## [183] <NA> (60,80] (140,160] (180,199] (180,199] (120,140] (100,120]
## [190] (120,140] (100,120] (120,140] (140,160] (120,140] (80,100] (140,160]
## [197] (100,120] (100,120] (100,120] (140,160] (100,120] (120,140] (100,120]
## [204] (80,100] (100,120] (100,120] (180,199] (160,180] (80,100] (180,199]
## [211] (80,100] (140,160] (160,180] (120,140] (100,120] (140,160] (100,120]
## [218] (120,140] (80,100] (100,120] (160,180] (140,160] (100,120] (140,160]
## [225] (80,100] (80,100] (100,120] (160,180] (180,199] (100,120] (140,160]
## [232] (120,140] (60,80] (120,140] (60,80] (160,180] (180,199] (160,180]
## [239] (160,180] (100,120] (80,100] (80,100] (120,140] (100,120] (140,160]
## [246] (180,199] (120,140] (160,180] (120,140] (100,120] (100,120] (120,140]
## [253] (80,100] (80,100] (80,100] (100,120] (100,120] (100,120] (180,199]
## [260] (140,160] (180,199] (140,160] (80,100] (140,160] (120,140] (80,100]
## [267] (120,140] (120,140] (100,120] (140,160] (100,120] (100,120] (120,140]
## [274] (60,80] (100,120] (80,100] (100,120] (100,120] (100,120] (100,120]
## [281] (140,160] (120,140] (120,140] (160,180] (100,120] (120,140] (140,160]
## [288] (100,120] (80,100] (100,120] (60,80] (100,120] (120,140] (120,140]
## [295] (160,180] (140,160] (140,160] (120,140] (80,100] (100,120] (160,180]
## [302] (140,160] (60,80] (100,120] (140,160] (100,120] (160,180] (120,140]
## [309] (120,140] (120,140] (60,80] (100,120] (140,160] (100,120] (100,120]
## [316] (100,120] (80,100] (180,199] (100,120] (180,199] (120,140] (100,120]
## [323] (120,140] (140,160] (100,120] (140,160] (120,140] (160,180] (100,120]
## [330] (100,120] (100,120] (80,100] (160,180] (100,120] (80,100] (160,180]
## [337] (100,120] (100,120] (140,160] (160,180] (120,140] (80,100] <NA>
## [344] (120,140] (80,100] (120,140] (120,140] (100,120] (80,100] <NA>
## [351] (80,100] (120,140] (60,80] (80,100] (80,100] (160,180] (120,140]
## [358] (120,140] (80,100] (180,199] (180,199] (140,160] (100,120] (140,160]
## [365] (140,160] (80,100] (120,140] (100,120] (80,100] (120,140] (160,180]
## [372] (100,120] (80,100] (100,120] (120,140] (120,140] (80,100] (80,100]
## [379] (140,160] (80,100] (100,120] (100,120] (100,120] (80,100] (120,140]
## [386] (100,120] (100,120] (100,120] (140,160] (80,100] (80,100] (160,180]
## [393] (120,140] (100,120] (140,160] (120,140] (80,100] (120,140] (80,100]
## [400] (180,199] (80,100] (120,140] (120,140] (60,80] (160,180] (120,140]
## [407] (100,120] (100,120] (180,199] (160,180] (100,120] (100,120] (140,160]
## [414] (140,160] (120,140] (160,180] (80,100] (140,160] (80,100] (120,140]
## [421] (100,120] (80,100] (100,120] (100,120] (140,160] (180,199] (80,100]
## [428] (180,199] (120,140] (80,100] (80,100] (80,100] (60,80] (120,140]
## [435] (80,100] (140,160] (120,140] (140,160] (80,100] (100,120] (180,199]
## [442] (80,100] (100,120] (100,120] (100,120] (160,180] (80,100] (80,100]
## [449] (100,120] (100,120] (80,100] (120,140] (80,100] (100,120] (80,100]
## [456] (160,180] (120,140] (80,100] (140,160] (120,140] (100,120] (60,80]
## [463] (60,80] (80,100] (100,120] (120,140] (60,80] (80,100] (100,120]
## [470] (140,160] (140,160] (120,140] (100,120] (120,140] (100,120] (120,140]
## [477] (100,120] (100,120] (120,140] (120,140] (140,160] (120,140] (80,100]
## [484] (80,100] (140,160] (120,140] (120,140] (160,180] (80,100] (180,199]
## [491] (80,100] (80,100] (80,100] (120,140] (60,80] (160,180] (100,120]
## [498] (80,100] (180,199] (140,160] (100,120] (80,100] <NA> (80,100]
## [505] (80,100] (60,80] (160,180] (120,140] (80,100] (100,120] (80,100]
## [512] (120,140] (80,100] (80,100] (80,100] (160,180] (140,160] (120,140]
## [519] (60,80] (120,140] (60,80] (120,140] (100,120] (120,140] (120,140]
## [526] (80,100] (80,100] (100,120] (100,120] (100,120] (120,140] (100,120]
## [533] (80,100] (80,100] (60,80] (120,140] (100,120] (40,60] (120,140]
## [540] (120,140] (80,100] (120,140] (80,100] (80,100] (80,100] (180,199]
## [547] (180,199] (120,140] (160,180] (180,199] (100,120] (80,100] (100,120]
## [554] (80,100] (80,100] (120,140] (80,100] (100,120] (100,120] (80,100]
## [561] (120,140] (180,199] (80,100] (80,100] (80,100] (80,100] (80,100]
## [568] (80,100] (140,160] (120,140] (60,80] (120,140] (100,120] (80,100]
## [575] (140,160] (100,120] (100,120] (100,120] (120,140] (180,199] (140,160]
## [582] (100,120] (120,140] (80,100] (120,140] (80,100] (140,160] (100,120]
## [589] (160,180] (60,80] (100,120] (100,120] (120,140] (80,100] (120,140]
## [596] (180,199] (60,80] (80,100] (160,180] (100,120] (100,120] (80,100]
## [603] (120,140] (140,160] (180,199] (120,140] (180,199] (80,100] (140,160]
## [610] (100,120] (100,120] (160,180] (160,180] (100,120] (120,140] (100,120]
## [617] (100,120] (60,80] (100,120] (100,120] (100,120] (80,100] (180,199]
## [624] (80,100] (100,120] (80,100] (120,140] (120,140] (120,140] (80,100]
## [631] (100,120] (100,120] (100,120] (120,140] (80,100] (100,120] (100,120]
## [638] (80,100] (80,100] (80,100] (100,120] (120,140] (140,160] (80,100]
## [645] (100,120] (140,160] (160,180] (160,180] (120,140] (100,120] (80,100]
## [652] (100,120] (120,140] (100,120] (100,120] (140,160] (100,120] (100,120]
## [659] (120,140] (60,80] (160,180] (180,199] (160,180] (140,160] (100,120]
## [666] (100,120] (140,160] (100,120] (80,100] (140,160] (160,180] (80,100]
## [673] (60,80] (120,140] (80,100] (180,199] (140,160] (80,100] (120,140]
## [680] (100,120] (40,60] (160,180] (80,100] (120,140] (120,140] (120,140]
## [687] (120,140] (100,120] (120,140] (140,160] (100,120] (140,160] (120,140]
## [694] (120,140] (80,100] (140,160] (160,180] (80,100] (120,140] (100,120]
## [701] (120,140] (120,140] (160,180] (120,140] (100,120] (60,80] (100,120]
## [708] (120,140] (160,180] (80,100] (140,160] (120,140] (120,140] (120,140]
## [715] (100,120] (180,199] (160,180] (80,100] (100,120] (80,100] (80,100]
## [722] (100,120] (140,160] (100,120] (100,120] (100,120] (100,120] (140,160]
## [729] (160,180] (80,100] (120,140] (100,120] (160,180] (100,120] (100,120]
## [736] (80,100] (120,140] (60,80] (80,100] (100,120] (100,120] (100,120]
## [743] (100,120] (120,140] (140,160] (80,100] (140,160] (80,100] (180,199]
## [750] (160,180] (120,140] (120,140] (100,120] (180,199] (140,160] (120,140]
## [757] (120,140] (120,140] (100,120] (180,199] (80,100] (160,180] (80,100]
## [764] (100,120] (120,140] (120,140] (120,140] (80,100]
## 10 Levels: (0,20] (20,40] (40,60] (60,80] (80,100] (100,120] ... (180,199]levels(intervalo) <-c("0-20","21-40","41-60","61-80","81-100", "101-120","121-140","141-160","161-180","181-200")
table(intervalo,teste) ## teste
## intervalo 0 1
## 0-20 0 0
## 21-40 0 0
## 41-60 4 0
## 61-80 36 2
## 81-100 151 16
## 101-120 152 53
## 121-140 94 63
## 141-160 42 49
## 161-180 12 48
## 181-200 6 35Utilizando o ponto de corte em 80 mg/dl
Com isso, podemos calcular a sensibilidade = 0.99 e a especificidade = 0.08. Nesse caso, temos uma alta sensibilidade que é bastante útil em exames de rastreio, ou seja, dificilmente deixaremos de cuidar de um paciente com diabetes.Já na especificidade, temos um valor bastante baixo, ou seja, um resultado que não é muito agradável, devido ao fato dela poder determinar o quão preciso é o diagnóstico.
#CORTE EM 80
intervalo <- cut(var,breaks=c(0,80,max(var)))
intervalo ## [1] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [8] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [15] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [22] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [29] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [36] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [43] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80] (80,199]
## [50] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [57] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [64] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [71] (80,199] (80,199] (80,199] (80,199] (0,80] <NA> (0,80]
## [78] (80,199] (80,199] (80,199] (80,199] (0,80] (80,199] (80,199]
## [85] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [92] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [99] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [106] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [113] (80,199] (0,80] (80,199] (80,199] (80,199] (0,80] (80,199]
## [120] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [127] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [134] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [141] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [148] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [155] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [162] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [169] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80] (0,80]
## [176] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [183] <NA> (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [190] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [197] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [204] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [211] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [218] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [225] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [232] (80,199] (0,80] (80,199] (0,80] (80,199] (80,199] (80,199]
## [239] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [246] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [253] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [260] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [267] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [274] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [281] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [288] (80,199] (80,199] (80,199] (0,80] (80,199] (80,199] (80,199]
## [295] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [302] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [309] (80,199] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199]
## [316] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [323] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [330] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [337] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] <NA>
## [344] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] <NA>
## [351] (80,199] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199]
## [358] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [365] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [372] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [379] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [386] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [393] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [400] (80,199] (80,199] (80,199] (80,199] (0,80] (80,199] (80,199]
## [407] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [414] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [421] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [428] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80] (80,199]
## [435] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [442] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [449] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [456] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80]
## [463] (0,80] (80,199] (80,199] (80,199] (0,80] (80,199] (80,199]
## [470] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [477] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [484] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [491] (80,199] (80,199] (80,199] (80,199] (0,80] (80,199] (80,199]
## [498] (80,199] (80,199] (80,199] (80,199] (80,199] <NA> (80,199]
## [505] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [512] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [519] (0,80] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199]
## [526] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [533] (80,199] (80,199] (0,80] (80,199] (80,199] (0,80] (80,199]
## [540] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [547] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [554] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [561] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [568] (80,199] (80,199] (80,199] (0,80] (80,199] (80,199] (80,199]
## [575] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [582] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [589] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [596] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [603] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [610] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [617] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [624] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [631] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [638] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [645] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [652] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [659] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [666] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [673] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [680] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199] (80,199]
## [687] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [694] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [701] (80,199] (80,199] (80,199] (80,199] (80,199] (0,80] (80,199]
## [708] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [715] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [722] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [729] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [736] (80,199] (80,199] (0,80] (80,199] (80,199] (80,199] (80,199]
## [743] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [750] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [757] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199] (80,199]
## [764] (80,199] (80,199] (80,199] (80,199] (80,199]
## Levels: (0,80] (80,199]levels(intervalo) <-c("0-80","81-200")
tab <- table(intervalo,teste) Utilizando o ponto de corte em 100 mg/dl
Uilizando o ponto de corte em 100 mg/dl, geramos uma sensibilidade de 0.93 e uma especificidade de 0.38. Nesse caso, a sensibilidade diminuiu um pouco perante o caso anterior, porém ela continua alta e a especificidade cresce consideravelmente para 0.38. Logo, de acordo com Galen e Gambino (1979), que sugerem uma medida de eficiência definida pela soma da sensibilidade e da especificidade, temos que o corte em 100 mg/dl possui uma maior eficácia.
#CORTE EM 100
intervalo <- cut(var,breaks=c(0,100,max(var)))
intervalo ## [1] (100,199] (0,100] (100,199] (0,100] (100,199] (100,199] (0,100]
## [8] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [15] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [22] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100]
## [29] (100,199] (100,199] (100,199] (100,199] (0,100] (0,100] (100,199]
## [36] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199] (100,199]
## [43] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [50] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100]
## [57] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199] (0,100]
## [64] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199]
## [71] (0,100] (100,199] (100,199] (100,199] (0,100] <NA> (0,100]
## [78] (0,100] (100,199] (100,199] (100,199] (0,100] (0,100] (100,199]
## [85] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100]
## [92] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] (0,100]
## [99] (0,100] (100,199] (100,199] (100,199] (100,199] (0,100] (0,100]
## [106] (100,199] (0,100] (100,199] (0,100] (0,100] (100,199] (100,199]
## [113] (0,100] (0,100] (100,199] (100,199] (100,199] (0,100] (0,100]
## [120] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100]
## [127] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [134] (0,100] (0,100] (100,199] (0,100] (0,100] (100,199] (100,199]
## [141] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100]
## [148] (100,199] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199]
## [155] (100,199] (100,199] (0,100] (100,199] (0,100] (100,199] (100,199]
## [162] (100,199] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199]
## [169] (100,199] (100,199] (100,199] (100,199] (0,100] (0,100] (0,100]
## [176] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] (100,199]
## [183] <NA> (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [190] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [197] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [204] (0,100] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [211] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [218] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [225] (0,100] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [232] (100,199] (0,100] (100,199] (0,100] (100,199] (100,199] (100,199]
## [239] (100,199] (100,199] (0,100] (0,100] (100,199] (100,199] (100,199]
## [246] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [253] (0,100] (0,100] (0,100] (100,199] (100,199] (100,199] (100,199]
## [260] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100]
## [267] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [274] (0,100] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199]
## [281] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [288] (100,199] (0,100] (100,199] (0,100] (100,199] (100,199] (100,199]
## [295] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199]
## [302] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [309] (100,199] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199]
## [316] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [323] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [330] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199]
## [337] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100] <NA>
## [344] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] <NA>
## [351] (0,100] (100,199] (0,100] (0,100] (0,100] (100,199] (100,199]
## [358] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [365] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199] (100,199]
## [372] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] (0,100]
## [379] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] (100,199]
## [386] (100,199] (100,199] (100,199] (100,199] (0,100] (0,100] (100,199]
## [393] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199] (0,100]
## [400] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199] (100,199]
## [407] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [414] (100,199] (100,199] (100,199] (0,100] (100,199] (0,100] (100,199]
## [421] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (0,100]
## [428] (100,199] (100,199] (0,100] (0,100] (0,100] (0,100] (100,199]
## [435] (0,100] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199]
## [442] (0,100] (100,199] (100,199] (100,199] (100,199] (0,100] (0,100]
## [449] (100,199] (100,199] (0,100] (100,199] (0,100] (100,199] (0,100]
## [456] (100,199] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100]
## [463] (0,100] (0,100] (100,199] (100,199] (0,100] (0,100] (100,199]
## [470] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [477] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100]
## [484] (0,100] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [491] (0,100] (0,100] (0,100] (100,199] (0,100] (100,199] (100,199]
## [498] (0,100] (100,199] (100,199] (100,199] (0,100] <NA> (0,100]
## [505] (0,100] (0,100] (100,199] (100,199] (0,100] (100,199] (0,100]
## [512] (100,199] (0,100] (0,100] (0,100] (100,199] (100,199] (100,199]
## [519] (0,100] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199]
## [526] (0,100] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [533] (0,100] (0,100] (0,100] (100,199] (100,199] (0,100] (100,199]
## [540] (100,199] (0,100] (100,199] (0,100] (0,100] (0,100] (100,199]
## [547] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [554] (0,100] (0,100] (100,199] (0,100] (100,199] (100,199] (0,100]
## [561] (100,199] (100,199] (0,100] (0,100] (0,100] (0,100] (0,100]
## [568] (0,100] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100]
## [575] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [582] (100,199] (100,199] (0,100] (100,199] (0,100] (100,199] (100,199]
## [589] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] (100,199]
## [596] (100,199] (0,100] (0,100] (100,199] (100,199] (100,199] (0,100]
## [603] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [610] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [617] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100] (100,199]
## [624] (0,100] (100,199] (0,100] (100,199] (100,199] (100,199] (0,100]
## [631] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199]
## [638] (0,100] (0,100] (0,100] (100,199] (100,199] (100,199] (0,100]
## [645] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100]
## [652] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [659] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [666] (100,199] (100,199] (100,199] (0,100] (100,199] (100,199] (0,100]
## [673] (0,100] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199]
## [680] (100,199] (0,100] (100,199] (0,100] (100,199] (100,199] (100,199]
## [687] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [694] (100,199] (0,100] (100,199] (100,199] (0,100] (100,199] (100,199]
## [701] (100,199] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199]
## [708] (100,199] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199]
## [715] (100,199] (100,199] (100,199] (0,100] (100,199] (0,100] (0,100]
## [722] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [729] (100,199] (0,100] (100,199] (100,199] (100,199] (100,199] (100,199]
## [736] (0,100] (100,199] (0,100] (0,100] (100,199] (100,199] (100,199]
## [743] (100,199] (100,199] (100,199] (0,100] (100,199] (0,100] (100,199]
## [750] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199] (100,199]
## [757] (100,199] (100,199] (100,199] (100,199] (0,100] (100,199] (0,100]
## [764] (100,199] (100,199] (100,199] (100,199] (0,100]
## Levels: (0,100] (100,199]levels(intervalo) <-c("0-100","101-200")
table(intervalo,teste) ## teste
## intervalo 0 1
## 0-100 191 18
## 101-200 306 248Construção da Curva ROC
library(nnet)## Warning: package 'nnet' was built under R version 3.4.4mymodel <- multinom(teste~.,data = dat)## # weights: 10 (9 variable)
## initial value 532.337035
## iter 10 value 372.788284
## final value 361.722689
## convergedp <- predict(mymodel,dat)
tab <- table(p, dat$teste)
tab##
## p 0 1
## 0 445 112
## 1 55 156sum(diag(tab))/sum(tab)## [1] 0.78255211-sum(diag(tab))/sum(tab)## [1] 0.2174479table(dat$teste)##
## 0 1
## 500 268500/768## [1] 0.6510417library(ROCR)## Warning: package 'ROCR' was built under R version 3.4.4## Loading required package: gplots## Warning: package 'gplots' was built under R version 3.4.4##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowesspred <- predict(mymodel,dat,type = 'prob')
head(pred)## 1 2 3 4 5 6
## 0.72172708 0.04864139 0.79670075 0.04162462 0.90218749 0.14663096head(dat)## preg plas pres skin insu mass pedi age teste
## 1 6 148 72 35 0 33.6 0.627 50 1
## 2 1 85 66 29 0 26.6 0.351 31 0
## 3 8 183 64 0 0 23.3 0.672 32 1
## 4 1 89 66 23 94 28.1 0.167 21 0
## 5 0 137 40 35 168 43.1 2.288 33 1
## 6 5 116 74 0 0 25.6 0.201 30 0hist(pred)
pred <- prediction(pred,dat$teste)
eval <- performance(pred, "acc")
plot(eval)
abline(h=0.785,v=0.65)
max <- which.max(slot(eval, "y.values")[[1]])
acc <- slot(eval, "y.values")[[1]][max]
acc## [1] 0.7851562cut <- slot(eval, "x.values")[[1]][max]
cut## 294
## 0.4867701print(c(Accuracy=acc, Cutoff = cut))## Accuracy Cutoff.294
## 0.7851562 0.4867701roc <- performance(pred, "tpr", "fpr")
plot(roc,colorize=T,
main ="CURVA ROC",
ylab = "Sensibilidade",
xlab = "1-Especificidade")
abline(a=0,b=1)
auc <- performance(pred,"auc")
auc <- unlist(slot(auc,"y.values"))
auc## [1] 0.8394254auc <- round(auc,4)
legend(.6,.4,auc,title= "AUC")
Curva ROC
Conclusão
A área sob a curva ROC é uma medida da capacidade discriminativa de um teste, isto é, a capacidade de um teste classificar corretamente aqueles com e sem a doença. Em outras palavras, se sortearmos ao acaso um caso com a doença e outro sem a doença da população,a área sob a curva nos dá a probabilidade de classificarmos corretamente este par.
Para o exercício proposto, obtivemos o valor da área sob a curva ROC de 0.8394,valor considerado bom, pois está dentro do intervalo [0.8 à 0.9].
Obs: Não foi utilizado o ponto de corte em 200 mg/dl, devido ao fato da variável “plas” só possuir valores até 199 mg/dl.