This function generates data from two logistic regression trees: one with three apparent clusters (in terms of variance of the features) but a single logistic regression generating y | x, and one with a single apparent cluster but three different logistic regressions generating y | x given a categorical feature.
generateData(n = 100, scenario = "tree", visualize = FALSE)
| n | The number of observations to draw. |
|---|---|
| scenario | The "no tree" scenario denotes the first scenario where there is a single logistic regression generating the data. The "tree" scenario generates data from the second data generating mechanism where there are three logistic regressions. |
| visualize | Whether (TRUE) or not (FALSE) to plot the generated data. |
Generates and returns data according to a true logistic regression tree (if scenario = "tree") or a single regression tree (if scenario = "no tree"). Eventually plots this dataset (if visualize = TRUE).
generateData(scenario = "tree")#> x1 x2 x3 y c #> 1 1 -2.10006528 2.435323325 0 1 #> 2 1 0.38297558 0.168057124 1 1 #> 3 1 -3.65589542 -0.200995520 1 1 #> 4 1 -0.00835693 -2.865131202 0 1 #> 5 1 0.93232908 -0.418855862 0 1 #> 6 2 1.72261741 -0.470168967 0 1 #> 7 2 -2.73272649 1.600961818 1 1 #> 8 1 -0.37098795 0.105052275 1 1 #> 9 2 -0.36629941 -0.958684986 1 1 #> 10 1 -0.42405817 -0.074947349 1 1 #> 11 1 -0.83054908 -0.377225165 0 1 #> 12 2 0.94347306 0.667195674 1 1 #> 13 1 3.09753734 4.133126363 0 1 #> 14 2 -2.44648410 0.069797071 1 1 #> 15 1 0.76864042 0.866563604 1 1 #> 16 2 -2.79451724 0.177292312 1 1 #> 17 1 -0.78301877 -2.867580737 0 1 #> 18 1 -0.07890286 1.293129723 0 1 #> 19 1 0.81449451 -0.364855109 0 1 #> 20 2 -1.37111224 -0.309130792 0 1 #> 21 1 0.70223163 0.028766388 0 1 #> 22 2 0.54442688 0.044341131 0 1 #> 23 2 -1.95681532 0.824741313 1 1 #> 24 2 1.10666448 -3.411172285 0 1 #> 25 1 2.83275739 4.023835775 0 1 #> 26 2 -0.14616766 -0.541831883 1 1 #> 27 2 -1.40377103 0.320033625 1 1 #> 28 1 -0.02392547 1.611518823 1 1 #> 29 1 -1.24018343 -0.997632373 0 1 #> 30 1 -2.26859948 1.670928628 1 1 #> 31 1 1.40304478 -0.368844618 0 1 #> 32 1 0.26473292 -1.766344963 1 1 #> 33 1 0.36552820 -1.463775924 1 1 #> 34 4 -0.44496004 0.122579444 0 2 #> 35 4 -0.58082036 -1.957675515 0 2 #> 36 4 -1.17814898 -1.417368090 0 2 #> 37 3 -1.58510530 0.681512391 0 2 #> 38 3 -1.19331214 -1.282803751 1 2 #> 39 4 -2.63441314 -0.430342828 0 2 #> 40 4 -1.03580685 1.342442438 1 2 #> 41 3 -0.83781299 0.100956660 0 2 #> 42 3 -0.80499499 -0.244014506 1 2 #> 43 3 0.34069070 -1.240965254 0 2 #> 44 4 1.46768238 2.814758432 1 2 #> 45 3 -0.31332398 1.149660298 0 2 #> 46 4 -2.09911569 1.469935043 1 2 #> 47 3 0.38780593 1.982671487 1 2 #> 48 3 -0.66269918 -1.679566243 0 2 #> 49 3 0.85289979 0.771899728 0 2 #> 50 3 3.19027569 -2.263649754 0 2 #> 51 3 0.63728766 2.299112220 1 2 #> 52 3 -2.52642230 0.643721057 1 2 #> 53 4 0.37410268 0.183155122 1 2 #> 54 3 1.60925738 -1.707018601 0 2 #> 55 3 3.05905389 -0.837022694 0 2 #> 56 4 0.67418067 1.578807805 0 2 #> 57 4 2.08772107 1.016525466 1 2 #> 58 4 0.63984982 0.057749321 1 2 #> 59 3 0.16137599 -0.534571781 1 2 #> 60 4 0.03344210 1.174266153 1 2 #> 61 4 0.90541652 1.206617424 1 2 #> 62 3 -0.39397586 -2.850091235 0 2 #> 63 3 -0.79239612 1.403676429 1 2 #> 64 4 0.28822413 -0.463577255 1 2 #> 65 3 -1.71929950 0.394600015 0 2 #> 66 4 1.26927700 -2.685887784 0 2 #> 67 6 1.86189461 -1.969221370 1 3 #> 68 6 0.91813642 1.120542881 1 3 #> 69 6 -0.64407013 -2.343777652 0 3 #> 70 5 2.04069199 0.106580039 1 3 #> 71 5 -0.10628615 -0.959302155 1 3 #> 72 5 -0.40823053 -1.267793608 0 3 #> 73 6 -3.67002004 1.012867047 0 3 #> 74 6 0.09822996 1.730063691 0 3 #> 75 6 -1.64776335 -2.529757114 1 3 #> 76 5 -0.94976726 -1.354222424 1 3 #> 77 5 -3.09548168 1.976450547 0 3 #> 78 5 3.97339804 1.650284617 1 3 #> 79 5 -1.73009758 1.805651759 0 3 #> 80 6 -0.51095681 -2.146906165 1 3 #> 81 6 1.17954386 2.074366291 0 3 #> 82 5 -1.90576967 0.004688911 1 3 #> 83 6 0.81321232 -0.116830237 1 3 #> 84 5 0.11265885 0.662142339 0 3 #> 85 5 0.83777163 0.193384344 1 3 #> 86 6 0.62310960 -1.245321391 1 3 #> 87 5 -2.17844965 -0.755389365 0 3 #> 88 6 1.41180918 -1.790461773 1 3 #> 89 5 -0.50840381 -1.127584984 0 3 #> 90 5 -0.11336137 2.183762105 0 3 #> 91 5 0.06030659 -1.242905299 1 3 #> 92 5 0.18645160 0.434661691 1 3 #> 93 5 -1.49764883 -0.720080226 0 3 #> 94 5 1.85008510 -0.907244031 0 3 #> 95 5 0.51063673 2.190165271 1 3 #> 96 5 -0.70905372 0.224519031 0 3 #> 97 6 1.06312959 -2.149981650 1 3 #> 98 6 -2.29343807 -0.015454978 0 3 #> 99 5 0.35613802 -0.318354052 1 3 #> 100 5 1.31516599 0.936198619 1 3