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 CUBIC_MODEL$formula, the null
formula is defined in CUBIC_MODEL$null_formula, the alternative is
defined in CUBIC_MODEL$alt_formula.
Arguments
- a
Numeric. Default is
a = 1.- b
Numeric. Default is
b = 1.- c
Numeric. Default is
c = 1.- sigma
Positive numeric. Default is
sigma = 1.- x
Random variable or closed form expression. Default is
x = rand_uniform(-1, 1, env = new.env(parent = parent.env(self))).- z
Random variable or closed form expression. Default is
z = rand_uniform(-1, 1, env = new.env(parent = parent.env(self))).- e
Random variable or closed form expression. Default is
e = rand_normal(0, sigma, env = new.env(parent = parent.env(self))).
Examples
# Instantiate
x <- rand_uniform()
z <- rand_uniform()
e <- rand_normal()
test <- cubic_model(a = 200, b = 200, c = 1, x = x, z = z, e = e)
test
#>
#> ── <CUBIC_MODEL object>
#> y = 1 + (2 - c) * x + c * z + a * (((2 - c) * x)^2 + (c * z)^2) + b * (((2 - c) * x)^3 + (c * z)^3) + e
#> - x: <RAND_UNIFORM object>
#> [a: 0, b: 1]
#> - z: <RAND_UNIFORM object>
#> [a: 0, b: 1]
#> - e: <RAND_NORMAL object>
#> [mu: 0, sigma: 1]
#> Parameters:
#> - a: 200
#> - b: 200
#> - c: 1
#> - sigma: 1
# Generate data
test$gen(10)
#> y x z e .resid .fitted
#> 1 142.261912 0.50613202 0.4635440 0.23195582 -66.925513 209.1874
#> 2 290.318543 0.14852255 0.8733548 -2.55009678 -3.880668 294.1992
#> 3 350.623812 0.61950183 0.7900710 0.43012355 -43.509727 394.1335
#> 4 534.580165 0.84462647 0.8526638 -0.69666013 45.140550 489.4396
#> 5 6.660578 0.04072794 0.1335064 1.10036207 87.643775 -80.9832
#> 6 153.678911 0.32775041 0.6198681 -1.27667090 -75.585953 229.2649
#> 7 584.973574 0.79517564 0.9541092 -0.56985678 62.991878 521.9817
#> 8 323.493326 0.21486282 0.9018162 0.82060968 -3.440864 326.9342
#> 9 173.308391 0.65581186 0.3266761 0.58069824 -16.387581 189.6960
#> 10 442.258050 0.42121073 0.9893733 -0.04571533 13.954104 428.3039
# Generate lineup
test$gen_lineup(10, k = 3)
#> y x z e .resid .fitted test_name
#> 1 36.24446 0.23011326 0.30207653 -2.0781075 -11.337853 47.58231 F-test
#> 2 370.38420 0.78813454 0.66124136 0.5204272 -34.782762 405.16696 F-test
#> 3 212.07907 0.01767204 0.77138057 -0.5777430 36.104331 175.97474 F-test
#> 4 357.89635 0.85754417 0.51425928 2.2306483 -8.093742 365.99010 F-test
#> 5 551.61866 0.76828639 0.93756790 -0.4757793 32.982394 518.63627 F-test
#> 6 457.98661 0.69734395 0.87949402 -0.4314085 -9.771313 467.75792 F-test
#> 7 82.45019 0.30156060 0.44287898 0.4315099 -52.301848 134.75204 F-test
#> 8 170.50860 0.70162816 0.07838648 -0.1328384 50.879395 119.62921 F-test
#> 9 431.00046 0.88238663 0.64117824 0.4085165 0.672237 430.32822 F-test
#> 10 138.68686 0.62520825 0.19502245 0.7223721 -4.350839 143.03770 F-test
#> 11 94.22715 0.23011326 0.30207653 -2.0781075 46.644835 47.58231 F-test
#> 12 389.24929 0.78813454 0.66124136 0.5204272 -15.917668 405.16696 F-test
#> 13 145.90705 0.01767204 0.77138057 -0.5777430 -30.067686 175.97474 F-test
#> 14 367.16545 0.85754417 0.51425928 2.2306483 1.175358 365.99010 F-test
#> 15 532.01487 0.76828639 0.93756790 -0.4757793 13.378599 518.63627 F-test
#> 16 435.87184 0.69734395 0.87949402 -0.4314085 -31.886077 467.75792 F-test
#> 17 153.35819 0.30156060 0.44287898 0.4315099 18.606148 134.75204 F-test
#> 18 74.53132 0.70162816 0.07838648 -0.1328384 -45.097885 119.62921 F-test
#> 19 478.29653 0.88238663 0.64117824 0.4085165 47.968312 430.32822 F-test
#> 20 138.23377 0.62520825 0.19502245 0.7223721 -4.803935 143.03770 F-test
#> 21 73.03134 0.23011326 0.30207653 -2.0781075 25.449029 47.58231 F-test
#> 22 459.13155 0.78813454 0.66124136 0.5204272 53.964594 405.16696 F-test
#> 23 153.36176 0.01767204 0.77138057 -0.5777430 -22.612982 175.97474 F-test
#> 24 320.36488 0.85754417 0.51425928 2.2306483 -45.625214 365.99010 F-test
#> 25 489.50304 0.76828639 0.93756790 -0.4757793 -29.133229 518.63627 F-test
#> 26 465.65637 0.69734395 0.87949402 -0.4314085 -2.101550 467.75792 F-test
#> 27 149.51801 0.30156060 0.44287898 0.4315099 14.765970 134.75204 F-test
#> 28 93.16773 0.70162816 0.07838648 -0.1328384 -26.461481 119.62921 F-test
#> 29 466.27908 0.88238663 0.64117824 0.4085165 35.950858 430.32822 F-test
#> 30 138.84171 0.62520825 0.19502245 0.7223721 -4.195994 143.03770 F-test
#> statistic p_value k null
#> 1 2016.6763772 1.791589e-05 3 FALSE
#> 2 2016.6763772 1.791589e-05 3 FALSE
#> 3 2016.6763772 1.791589e-05 3 FALSE
#> 4 2016.6763772 1.791589e-05 3 FALSE
#> 5 2016.6763772 1.791589e-05 3 FALSE
#> 6 2016.6763772 1.791589e-05 3 FALSE
#> 7 2016.6763772 1.791589e-05 3 FALSE
#> 8 2016.6763772 1.791589e-05 3 FALSE
#> 9 2016.6763772 1.791589e-05 3 FALSE
#> 10 2016.6763772 1.791589e-05 3 FALSE
#> 11 4.5409905 1.220741e-01 1 TRUE
#> 12 4.5409905 1.220741e-01 1 TRUE
#> 13 4.5409905 1.220741e-01 1 TRUE
#> 14 4.5409905 1.220741e-01 1 TRUE
#> 15 4.5409905 1.220741e-01 1 TRUE
#> 16 4.5409905 1.220741e-01 1 TRUE
#> 17 4.5409905 1.220741e-01 1 TRUE
#> 18 4.5409905 1.220741e-01 1 TRUE
#> 19 4.5409905 1.220741e-01 1 TRUE
#> 20 4.5409905 1.220741e-01 1 TRUE
#> 21 0.7775372 6.067149e-01 2 TRUE
#> 22 0.7775372 6.067149e-01 2 TRUE
#> 23 0.7775372 6.067149e-01 2 TRUE
#> 24 0.7775372 6.067149e-01 2 TRUE
#> 25 0.7775372 6.067149e-01 2 TRUE
#> 26 0.7775372 6.067149e-01 2 TRUE
#> 27 0.7775372 6.067149e-01 2 TRUE
#> 28 0.7775372 6.067149e-01 2 TRUE
#> 29 0.7775372 6.067149e-01 2 TRUE
#> 30 0.7775372 6.067149e-01 2 TRUE
# Plot the lineup
test$plot_lineup(test$gen_lineup(100))
