data/thyroid.R

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

thyroid <- structure(list(Diagnosis = structure(
  c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), 
    .Label = c("Hypo", "Normal", "Hyper"), class = "factor"), 
    RT3U = c(107.0, 113.0, 127.0, 109.0, 105.0, 105.0, 110.0, 114.0, 
    106.0, 107.0, 106.0, 110.0, 116.0, 112.0, 122.0, 109.0, 111.0, 114.0, 
    119.0, 115.0, 101.0, 103.0, 109.0, 102.0, 121.0, 100.0, 106.0, 116.0, 
    105.0, 110.0, 120.0, 116.0, 110.0, 90.0, 117.0, 117.0, 113.0, 106.0, 
    130.0, 100.0, 121.0, 110.0, 129.0, 121.0, 123.0, 107.0, 109.0, 120.0, 
    100.0, 118.0, 100.0, 103.0, 115.0, 119.0, 106.0, 114.0, 93.0, 120.0, 
    106.0, 110.0, 103.0, 101.0, 115.0, 116.0, 117.0, 106.0, 118.0, 97.0, 
    113.0, 104.0, 96.0, 120.0, 133.0, 126.0, 113.0, 109.0, 119.0, 101.0, 
    108.0, 117.0, 115.0, 91.0, 103.0, 98.0, 111.0, 107.0, 119.0, 122.0, 
    105.0, 109.0, 105.0, 112.0, 112.0, 98.0, 109.0, 114.0, 114.0, 110.0, 
    120.0, 108.0, 108.0, 116.0, 113.0, 105.0, 114.0, 114.0, 105.0, 107.0, 
    116.0, 102.0, 116.0, 118.0, 109.0, 110.0, 104.0, 105.0, 102.0, 112.0, 
    111.0, 111.0, 103.0, 98.0, 117.0, 111.0, 101.0, 106.0, 102.0, 115.0, 
    130.0, 101.0, 110.0, 103.0, 113.0, 112.0, 118.0, 109.0, 116.0, 127.0, 
    108.0, 108.0, 105.0, 98.0, 112.0, 118.0, 94.0, 126.0, 114.0, 111.0, 
    104.0, 102.0, 139.0, 111.0, 113.0, 65.0, 88.0, 65.0, 134.0, 110.0, 
    67.0, 95.0, 89.0, 89.0, 88.0, 105.0, 89.0, 99.0, 80.0, 89.0, 99.0, 68.0, 
    97.0, 84.0, 84.0, 98.0, 94.0, 99.0, 76.0, 110.0, 144.0, 105.0, 88.0, 
    97.0, 106.0, 79.0, 92.0, 125.0, 120.0, 108.0, 120.0, 119.0, 141.0, 
    129.0, 118.0, 120.0, 119.0, 123.0, 115.0, 126.0, 121.0, 131.0, 134.0, 
    141.0, 113.0, 136.0, 120.0, 125.0, 123.0, 112.0, 134.0, 119.0, 118.0, 
    139.0, 103.0, 97.0, 102.0), 
    T4 = c(10.1, 9.9, 12.9, 5.3, 7.3, 
    6.1, 10.4, 9.9, 9.4, 13, 4.2, 11.3, 9.2, 8.1, 9.7, 8.4, 8.4, 
    6.7, 10.6, 7.1, 7.8, 10.1, 10.4, 7.6, 10.1, 6.1, 9.6, 10.1, 
    11.1, 10.4, 8.4, 11.1, 7.8, 8.1, 12.2, 11, 9, 9.4, 9.5, 10.5, 
    10.1, 9.2, 11.9, 13.5, 8.1, 8.4, 10, 6.8, 9.5, 8.1, 11.3, 
    12.2, 8.1, 8, 9.4, 10.9, 8.9, 10.4, 11.3, 8.7, 8.1, 7.1, 
    10.4, 10, 9.2, 6.7, 10.5, 7.8, 11.1, 6.3, 9.4, 12.4, 9.7, 
    9.4, 8.5, 9.7, 12.9, 7.1, 10.4, 6.7, 15.3, 8, 8.5, 9.1, 7.8, 
    13, 11.4, 11.8, 8.1, 7.6, 9.5, 5.9, 9.5, 8.6, 12.4, 9.1, 
    11.1, 8.4, 7.1, 10.9, 8.7, 11.9, 11.5, 7, 8.4, 8.1, 11.1, 
    13.8, 11.5, 9.5, 16.1, 10.6, 8.9, 7, 9.6, 8.7, 8.5, 6.8, 
    8.5, 8.5, 7.3, 10.4, 7.8, 9.1, 6.3, 8.9, 8.4, 10.6, 10, 6.7, 
    6.3, 9.5, 7.8, 10.6, 6.5, 9.2, 7.8, 7.7, 6.5, 7.1, 5.7, 5.7, 
    6.5, 12.2, 7.5, 10.4, 7.5, 11.9, 6.1, 6.6, 16.4, 16, 17.2, 
    25.3, 24.1, 18.2, 16.4, 20.3, 23.3, 11.1, 14.3, 23.8, 12.9, 
    17.4, 20.1, 13, 23, 21.8, 13, 14.7, 14.2, 21.5, 18.5, 16.7, 
    20.5, 17.5, 25.3, 15.2, 22.3, 12, 16.5, 15.1, 13.4, 19, 11.1, 
    2.3, 6.8, 3.5, 3, 3.8, 5.6, 1.5, 3.6, 1.9, 0.8, 5.6, 6.3, 
    0.5, 4.7, 2.7, 2, 2.5, 5.1, 1.4, 3.4, 3.7, 1.9, 2.6, 1.9, 
    5.1, 6.5, 4.2, 5.1, 4.7, 5.3), T3 = c(2.2, 3.1, 2.4, 1.6, 
    1.5, 2.1, 1.6, 2.4, 2.2, 1.1, 1.2, 2.3, 2.7, 1.9, 1.6, 2.1, 
    1.5, 1.5, 2.1, 1.3, 1.2, 1.3, 1.9, 1.8, 1.7, 2.4, 2.4, 2.2, 
    2, 1.8, 1.1, 2, 1.9, 1.6, 1.9, 1.4, 2, 1.5, 1.7, 2.4, 2.4, 
    1.6, 2.7, 1.5, 2.3, 1.8, 1.3, 1.9, 2.5, 1.9, 2.5, 1.2, 1.7, 
    2, 1.7, 2.1, 1.5, 2.1, 1.8, 1.9, 1.4, 2.2, 1.8, 1.7, 1.9, 
    1.5, 2.1, 1.3, 1.7, 2, 1.5, 2.4, 2.9, 2.3, 1.8, 1.4, 1.5, 
    1.6, 2.1, 2.2, 2.3, 1.7, 1.8, 1.4, 2, 1.5, 2.3, 2.7, 2, 1.3, 
    1.8, 1.7, 2, 1.6, 2.3, 2.6, 2.4, 1.4, 1.2, 1.2, 1.2, 1.8, 
    1.5, 1.5, 1.6, 1.6, 1.1, 1.5, 1.8, 1.4, 0.9, 1.8, 1.7, 1, 
    1.1, 1.5, 1.2, 1.7, 1.6, 1.6, 1, 1.6, 2, 1.7, 1.5, 0.7, 1.5, 
    0.8, 1.6, 1.3, 1, 2.9, 2, 1.6, 1.2, 1.8, 1.4, 1.8, 1, 1.3, 
    1, 0.4, 1.2, 1.5, 1.2, 1.7, 1.1, 2.3, 1.8, 1.2, 3.8, 2.1, 
    1.8, 5.8, 5.5, 10, 4.8, 3.7, 7.4, 2.7, 4.1, 5.4, 2.7, 1.6, 
    7.3, 3.6, 10, 7.1, 3.1, 7.8, 3.6, 2.7, 4.4, 4.3, 1.8, 1.9, 
    4.5, 1.9, 3.3, 3.3, 4.9, 1.8, 3, 5.5, 2, 0.9, 2.1, 0.6, 2.5, 
    1.1, 1.8, 0.6, 1.5, 0.7, 0.7, 1.1, 1.2, 0.2, 1.8, 0.8, 0.5, 
    1.3, 0.7, 0.3, 1.8, 1.1, 0.3, 0.7, 0.6, 1.1, 1.3, 0.7, 1.4, 
    1.1, 1.4), 
    TSH = c(0.9, 2, 1.4, 1.4, 1.5, 1.4, 1.6, 1.5, 
    1.5, 0.9, 1.6, 0.9, 1, 3.7, 0.9, 1.1, 0.8, 1, 1.3, 1.3, 1, 
    0.7, 0.4, 2, 1.3, 1.8, 1, 1.6, 1, 1, 1.4, 1.2, 2.1, 1.4, 
    1.2, 1.5, 1.8, 0.8, 0.4, 0.9, 0.8, 1.5, 1.2, 1.6, 1, 1.5, 
    1.8, 1.3, 1.3, 1.5, 0.7, 1.3, 0.6, 0.6, 0.9, 0.3, 0.8, 1.1, 
    0.9, 1.6, 0.5, 0.8, 1.6, 1.5, 1.5, 1.2, 0.7, 1.2, 0.8, 1.2, 
    1, 0.8, 0.8, 1, 0.8, 1.1, 1.3, 1.5, 1.3, 1.8, 2, 2.1, 1.9, 
    1.9, 1.8, 2.8, 2.2, 1.7, 1.9, 2.2, 1.6, 2, 1.2, 1.6, 1.7, 
    1.5, 2, 1, 1.5, 1.9, 2.2, 1.9, 1.9, 2.7, 1.6, 1.6, 0.8, 1, 
    1.4, 1.1, 1.3, 1.4, 1, 1.6, 1.3, 1.1, 1.3, 1.4, 1.1, 1.2, 
    0.7, 2.3, 1, 1.2, 0.9, 1, 0.8, 2.1, 0.9, 1, 0.8, 1.4, 1.1, 
    0.9, 1.2, 1.1, 1.1, 1.9, 0.9, 1.6, 0.9, 1.3, 1.2, 1, 1.3, 
    1.2, 1.6, 0.9, 0.5, 1.4, 1.1, 0.9, 1, 1.3, 0.8, 1.3, 0.6, 
    0.6, 1.8, 1.6, 0.5, 0.5, 0.1, 0.3, 1.1, 0.7, 0.9, 0.7, 0.5, 
    0.6, 1.5, 1.1, 1.1, 1.7, 1.4, 1.4, 1.2, 0.7, 1.3, 1.1, 0.8, 
    1.2, 1.1, 0.9, 0.7, 16.5, 10.4, 1.7, 1.2, 23, 9.2, 12.5, 
    11.6, 18.5, 56.4, 13.7, 4.7, 12.2, 11.2, 9.9, 12.2, 8.5, 
    5.8, 32.6, 7.5, 8.5, 22.8, 41, 18.4, 7, 1.7, 4.3, 1.2, 2.1, 
    1.3), 
    DTSH = c(2.7, 5.9, 0.6, 1.5, -0.1, 7, 2.7, 5.7, 0, 
    3.1, 1.4, 3.3, 4.2, 2, 2.2, 3.6, 1.2, 3.5, 1.1, 2, 1.7, 0.1, 
    -0.1, 2.5, 0.1, 3.8, 1.3, 0.8, 1, 2.3, 1.4, 2.3, 6.4, 1.1, 
    3.9, 2.1, 1.6, 0.5, 3.2, 1.9, 3, 0.3, 3.5, 0.5, 5.1, 0.8, 
    4.3, 1.9, -0.2, 13.7, -0.3, 2.7, 2.2, 3.2, 3.1, 1.4, 2.7, 
    1.8, 1, 4.4, 3.8, 2.2, 2, 4.3, 6.8, 3.9, 3.5, 0.9, 2.3, 4, 
    3.1, 1.9, 1.9, 4, 0.5, 2.1, 3.6, 1.6, 2.4, 6.7, 2, 4.6, 1.1, 
    -0.3, 4.1, 1.7, 1.6, 2.3, -0.5, 1.9, 3.6, 1.3, 0.7, 6, 0.8, 
    1.5, -0.3, 1.9, 4.3, 1, 2.5, 1.5, 2.9, 4.3, -0.2, 0.5, 1.2, 
    1.9, 5.4, 1.6, 1.5, 3, 0.9, 4.3, 0.8, 1.5, 1.4, 3.3, 3.9, 
    7.7, 0.5, -0.7, 3.9, 4.1, 2.9, 2.3, 2.4, 4.6, 4.6, 5.7, 1, 
    -0.1, 3, -0.1, 1.7, 4.4, 3.7, 6.4, 1.5, 2.2, 0.9, 2.8, 2, 
    2.3, 4.4, 3.5, 4.4, 3.8, 0.8, 1.3, -0.2, -0.1, 0, 0.2, 0.1, 
    0.1, 0.1, 0.2, -0.6, -0.3, 0.2, 0.1, 0.2, 0.4, -0.2, -0.1, 
    -0.1, -0.1, -0.1, -0.2, 0.3, -0.6, -0.3, 0.2, -0.5, 0.3, 
    -0.1, -0.2, 0.6, 0, 0.1, -0.2, 0, 0.3, -0.2, 9.5, 38.6, 1.4, 
    4.5, 5.7, 14.4, 2.9, 48.8, 24, 21.6, 56.3, 14.4, 8.8, 53, 
    4.7, 2.2, 7.5, 19.6, 8.4, 21.5, 25.9, 22.2, 19, 8.2, 40.8, 
    11.5, 6.3, 5, 12.6, 6.7)), 
  class = "data.frame", row.names = c(NA, -215L)
)