This function will be called after an instance is built. User
input will be stored in the environment. The response variable of this model
is y. The formula of y is defined in HETER_MODEL$formula,
the null formula is defined in HETER_MODEL$null_formula.
Arguments
- a
Numeric. Default is
a = 0.- b
Numeric. Default is
b = 1.- x
Random variable or closed form expression. Default is
x = rand_uniform(-1, 1, env = new.env(parent = parent.env(self))).- e
Random variable or closed form expression. Default is
e = rand_normal(0, 1, env = new.env(parent = parent.env(self))).
Examples
# Instantiate
test <- heter_model(a = 0, b = 20)
test
#>
#> ── <HETER_MODEL object>
#> y = 1 + x + sqrt(1 + (2 - abs(a)) * (x - a)^2 * b) * e
#> - x: <RAND_UNIFORM object>
#> [a: -1, b: 1]
#> - e: <RAND_NORMAL object>
#> [mu: 0, sigma: 1]
#> Parameters:
#> - a: 0
#> - b: 20
# Generate data
test$gen(10)
#> y x e .resid .fitted
#> 1 3.7910489 0.3085354 1.1321921 2.1870174 1.6040315
#> 2 2.0431958 0.8577522 0.0336174 -0.1517121 2.1949079
#> 3 4.6438956 -0.8441994 0.8262286 4.2800369 0.3638587
#> 4 -0.3796699 -0.3955702 -0.3652580 -1.2261875 0.8465176
#> 5 -5.3437042 -0.7955397 -1.0815457 -5.7599135 0.4162093
#> 6 4.3652842 -0.3533806 1.5187571 3.4733767 0.8919074
#> 7 -1.0329121 -0.3253130 -0.7464567 -1.9550161 0.9221040
#> 8 -0.2426286 0.5883926 -0.4751776 -2.1477453 1.9051167
#> 9 4.8129207 -0.7120485 0.9808994 4.3068871 0.5060335
#> 10 -2.7805898 -0.9721954 -0.4508224 -3.0067436 0.2261538
# Generate lineup
test$gen_lineup(10, k = 3)
#> y x e .resid .fitted test_name
#> 1 3.23852596 0.41177906 0.65481615 2.866709354 0.3718166 BP-test
#> 2 1.50832328 -0.08641925 0.52187839 1.006945284 0.5013780 BP-test
#> 3 0.24122020 -0.42833001 -0.11443457 -0.349075065 0.5902953 BP-test
#> 4 1.52595469 0.57330265 -0.01258834 1.196143891 0.3298108 BP-test
#> 5 0.58233618 -0.99922370 0.09089323 -0.156425626 0.7387618 BP-test
#> 6 0.55951863 0.94997310 -0.22828732 0.327664697 0.2318539 BP-test
#> 7 -3.94335276 0.30007383 -2.44428945 -4.344219415 0.4008667 BP-test
#> 8 -1.42627450 0.77649787 -0.63904863 -1.703242378 0.2769679 BP-test
#> 9 4.30100941 0.52043656 0.80828664 3.957450262 0.3435592 BP-test
#> 10 -2.49287159 0.65302041 -0.97564103 -2.801951004 0.3090794 BP-test
#> 11 4.32796204 0.41177906 0.65481615 3.956145437 0.3718166 BP-test
#> 12 3.22909096 -0.08641925 0.52187839 2.727712961 0.5013780 BP-test
#> 13 -0.96324678 -0.42833001 -0.11443457 -1.553542046 0.5902953 BP-test
#> 14 -0.02111171 0.57330265 -0.01258834 -0.350922516 0.3298108 BP-test
#> 15 -0.13512099 -0.99922370 0.09089323 -0.873882798 0.7387618 BP-test
#> 16 -3.21496999 0.94997310 -0.22828732 -3.446823928 0.2318539 BP-test
#> 17 0.17324743 0.30007383 -2.44428945 -0.227619220 0.4008667 BP-test
#> 18 3.30407606 0.77649787 -0.63904863 3.027108178 0.2769679 BP-test
#> 19 -2.59062780 0.52043656 0.80828664 -2.934186950 0.3435592 BP-test
#> 20 -0.01490971 0.65302041 -0.97564103 -0.323989120 0.3090794 BP-test
#> 21 -2.50129275 0.41177906 0.65481615 -2.873109358 0.3718166 BP-test
#> 22 1.89676394 -0.08641925 0.52187839 1.395385944 0.5013780 BP-test
#> 23 0.59721766 -0.42833001 -0.11443457 0.006922392 0.5902953 BP-test
#> 24 4.22712014 0.57330265 -0.01258834 3.897309333 0.3298108 BP-test
#> 25 0.31728483 -0.99922370 0.09089323 -0.421476973 0.7387618 BP-test
#> 26 -0.84355220 0.94997310 -0.22828732 -1.075406131 0.2318539 BP-test
#> 27 -2.59579687 0.30007383 -2.44428945 -2.996663529 0.4008667 BP-test
#> 28 -1.60447120 0.77649787 -0.63904863 -1.881439081 0.2769679 BP-test
#> 29 4.45811516 0.52043656 0.80828664 4.114556004 0.3435592 BP-test
#> 30 0.14300081 0.65302041 -0.97564103 -0.166078602 0.3090794 BP-test
#> statistic p_value k null
#> 1 2.614535 0.2705583 3 FALSE
#> 2 2.614535 0.2705583 3 FALSE
#> 3 2.614535 0.2705583 3 FALSE
#> 4 2.614535 0.2705583 3 FALSE
#> 5 2.614535 0.2705583 3 FALSE
#> 6 2.614535 0.2705583 3 FALSE
#> 7 2.614535 0.2705583 3 FALSE
#> 8 2.614535 0.2705583 3 FALSE
#> 9 2.614535 0.2705583 3 FALSE
#> 10 2.614535 0.2705583 3 FALSE
#> 11 1.539717 0.4630786 1 TRUE
#> 12 1.539717 0.4630786 1 TRUE
#> 13 1.539717 0.4630786 1 TRUE
#> 14 1.539717 0.4630786 1 TRUE
#> 15 1.539717 0.4630786 1 TRUE
#> 16 1.539717 0.4630786 1 TRUE
#> 17 1.539717 0.4630786 1 TRUE
#> 18 1.539717 0.4630786 1 TRUE
#> 19 1.539717 0.4630786 1 TRUE
#> 20 1.539717 0.4630786 1 TRUE
#> 21 2.451625 0.2935191 2 TRUE
#> 22 2.451625 0.2935191 2 TRUE
#> 23 2.451625 0.2935191 2 TRUE
#> 24 2.451625 0.2935191 2 TRUE
#> 25 2.451625 0.2935191 2 TRUE
#> 26 2.451625 0.2935191 2 TRUE
#> 27 2.451625 0.2935191 2 TRUE
#> 28 2.451625 0.2935191 2 TRUE
#> 29 2.451625 0.2935191 2 TRUE
#> 30 2.451625 0.2935191 2 TRUE
# Plot the lineup
test$plot_lineup(test$gen_lineup(100))
