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 QUARTIC_MODEL$formula, the null
formula is defined in QUARTIC_MODEL$null_formula, the alternative is
defined in QUARTIC_MODEL$alt_formula.
Arguments
- a
Numeric. Default is
a = 1.- b
Numeric. Default is
b = 1.- c
Numeric. Default is
c = 1.- sigma
Positive numeric. Default is
sigma = 1.- x
Random variable or closed form expression. Default is
x = rand_uniform(-1, 1, env = new.env(parent = parent.env(self))).- e
Random variable or closed form expression. Default is
e = rand_normal(0, sigma, env = new.env(parent = parent.env(self))).
Examples
# Instantiate
x <- rand_uniform()
e <- rand_normal()
test <- quartic_model(a = 200, b = 200, c = 20, x = x, e = e)
test
#>
#> ── <QUARTIC_MODEL object>
#> y = 1 + x + a * x^2 + b * x^3 + c * x^4 + e
#> - x: <RAND_UNIFORM object>
#> [a: 0, b: 1]
#> - e: <RAND_NORMAL object>
#> [mu: 0, sigma: 1]
#> Parameters:
#> - a: 200
#> - b: 200
#> - c: 20
#> - sigma: 1
# Generate data
test$gen(10)
#> y x e .resid .fitted
#> 1 266.48809 0.8326320 0.938881482 -1.800431 268.288523
#> 2 26.51895 0.3091217 0.008264311 30.234716 -3.715769
#> 3 205.34567 0.7511824 -0.403337227 -20.623480 225.969154
#> 4 299.17899 0.8731442 0.071562534 9.841233 289.337754
#> 5 218.28410 0.7693290 0.067087294 -17.113629 235.397727
#> 6 328.83247 0.9072684 -0.614122881 21.764498 307.067971
#> 7 345.78039 0.9256728 -0.840134614 29.149906 316.630481
#> 8 111.29668 0.5848244 -1.035898785 -28.236563 139.533243
#> 9 212.67593 0.7591172 1.533917315 -17.415968 230.091894
#> 10 257.80709 0.8236224 -0.632095091 -5.800283 263.607369
# Generate lineup
test$gen_lineup(10, k = 3)
#> y x e .resid .fitted test_name
#> 1 54.3271881 0.4284965 -0.23259193 -1.5709352 55.89812 F-test
#> 2 39.1302801 0.3733132 -0.90920584 8.0983724 31.03191 F-test
#> 3 122.5927703 0.6104263 -1.81007892 -15.2849231 137.87769 F-test
#> 4 21.1949939 0.2725567 0.90514030 35.5650532 -14.37006 F-test
#> 5 110.0813949 0.5798953 -0.01716754 -14.0387046 124.12010 F-test
#> 6 103.9378940 0.5716289 -2.47815205 -16.4572710 120.39516 F-test
#> 7 295.0216335 0.8686505 -0.23346133 40.7852952 254.23634 F-test
#> 8 121.5066421 0.6049306 -0.23855210 -13.8946337 135.40128 F-test
#> 9 90.9914387 0.5342331 0.25253545 -12.5527408 103.54418 F-test
#> 10 84.1062062 0.5147296 0.92304862 -10.6495125 94.75572 F-test
#> 11 61.5382280 0.4284965 -0.23259193 5.6401047 55.89812 F-test
#> 12 2.4728132 0.3733132 -0.90920584 -28.5590945 31.03191 F-test
#> 13 151.5442297 0.6104263 -1.81007892 13.6665363 137.87769 F-test
#> 14 -0.6372846 0.2725567 0.90514030 13.7327747 -14.37006 F-test
#> 15 122.1688830 0.5798953 -0.01716754 -1.9512165 124.12010 F-test
#> 16 76.1001927 0.5716289 -2.47815205 -44.2949723 120.39516 F-test
#> 17 254.3409886 0.8686505 -0.23346133 0.1046503 254.23634 F-test
#> 18 147.1006095 0.6049306 -0.23855210 11.6993337 135.40128 F-test
#> 19 104.6043717 0.5342331 0.25253545 1.0601922 103.54418 F-test
#> 20 123.6574099 0.5147296 0.92304862 28.9016913 94.75572 F-test
#> 21 10.5135488 0.4284965 -0.23259193 -45.3845745 55.89812 F-test
#> 22 31.4304791 0.3733132 -0.90920584 0.3985714 31.03191 F-test
#> 23 140.7893153 0.6104263 -1.81007892 2.9116219 137.87769 F-test
#> 24 -3.7014761 0.2725567 0.90514030 10.6685833 -14.37006 F-test
#> 25 153.2225809 0.5798953 -0.01716754 29.1024814 124.12010 F-test
#> 26 113.2009035 0.5716289 -2.47815205 -7.1942615 120.39516 F-test
#> 27 238.3662294 0.8686505 -0.23346133 -15.8701090 254.23634 F-test
#> 28 162.9525012 0.6049306 -0.23855210 27.5512254 135.40128 F-test
#> 29 108.2715220 0.5342331 0.25253545 4.7273425 103.54418 F-test
#> 30 87.8448377 0.5147296 0.92304862 -6.9108810 94.75572 F-test
#> statistic p_value k null
#> 1 1124.3940219 1.716002e-07 3 FALSE
#> 2 1124.3940219 1.716002e-07 3 FALSE
#> 3 1124.3940219 1.716002e-07 3 FALSE
#> 4 1124.3940219 1.716002e-07 3 FALSE
#> 5 1124.3940219 1.716002e-07 3 FALSE
#> 6 1124.3940219 1.716002e-07 3 FALSE
#> 7 1124.3940219 1.716002e-07 3 FALSE
#> 8 1124.3940219 1.716002e-07 3 FALSE
#> 9 1124.3940219 1.716002e-07 3 FALSE
#> 10 1124.3940219 1.716002e-07 3 FALSE
#> 11 0.2059589 8.881709e-01 1 TRUE
#> 12 0.2059589 8.881709e-01 1 TRUE
#> 13 0.2059589 8.881709e-01 1 TRUE
#> 14 0.2059589 8.881709e-01 1 TRUE
#> 15 0.2059589 8.881709e-01 1 TRUE
#> 16 0.2059589 8.881709e-01 1 TRUE
#> 17 0.2059589 8.881709e-01 1 TRUE
#> 18 0.2059589 8.881709e-01 1 TRUE
#> 19 0.2059589 8.881709e-01 1 TRUE
#> 20 0.2059589 8.881709e-01 1 TRUE
#> 21 1.8892705 2.492128e-01 2 TRUE
#> 22 1.8892705 2.492128e-01 2 TRUE
#> 23 1.8892705 2.492128e-01 2 TRUE
#> 24 1.8892705 2.492128e-01 2 TRUE
#> 25 1.8892705 2.492128e-01 2 TRUE
#> 26 1.8892705 2.492128e-01 2 TRUE
#> 27 1.8892705 2.492128e-01 2 TRUE
#> 28 1.8892705 2.492128e-01 2 TRUE
#> 29 1.8892705 2.492128e-01 2 TRUE
#> 30 1.8892705 2.492128e-01 2 TRUE
# Plot the lineup
test$plot_lineup(test$gen_lineup(100))
