hySAINT {hySAINT} | R Documentation |
This is the main function of package hySAINT. It implements both genetic algorithm and simulated annealing. The simulated annealing technique is used within mutation operator.
hySAINT(
X,
y,
heredity = "Strong",
r1,
r2,
sigma,
interaction.ind = NULL,
varind = NULL,
numElite = 40,
max.iter = 500,
initial.temp = 1000,
cooling.rate = 0.95,
lambda = 10
)
X |
Input data. An optional data frame, or numeric matrix of dimension
|
y |
Response variable. A |
heredity |
Whether to enforce Strong, Weak, or No heredity. Default is "Strong". |
r1 |
At most how many main effects do you want to include in your model?.
For high-dimensional data, |
r2 |
At most how many interaction effects do you want to include in your model? |
sigma |
The standard deviation of the noise term. In practice, sigma is usually
unknown. Users can estimate sigma from function |
interaction.ind |
A two-column numeric matrix. Each row represents a unique
interaction pair, with the columns indicating the index numbers of the variables
involved in each interaction. Note that interaction.ind must be generated
outside of this function using |
varind |
A numeric vector that specifies the indices of variables to be extracted from |
numElite |
Number of elite parents. Default is 40. |
max.iter |
Maximum number of iterations. Default is 500. |
initial.temp |
Initial temperature. Default is 1000. |
cooling.rate |
A numeric value represents the speed at which the temperature decreases. Default is 0.95. |
lambda |
A numeric value defined by users. The number needs to satisfy the condition:
|
An object with S3 class "hySAINT"
.
Final.variable.names |
Name of the selected effects. |
Final.variable.idx |
Index of the selected effects. |
Final.model.score |
Final Model ABC. |
All.iter.score |
Best ABC scores from initial parents and all iterations. |
ABC
, EVA
, Initial
,
Crossover
, Mutation
set.seed(0)
interaction.ind <- t(combn(10,2))
X <- matrix(rnorm(100*10,1,0.1), 100, 10)
epl <- rnorm(100,0,0.01)
y <- 1+X[,1]+X[,2]+X[,3]+X[,1]*X[,2]+X[,1]*X[,3]+epl
hySAINT(X, y, r1 = 5, r2 = 2, sigma = 0.01, interaction.ind = interaction.ind, max.iter = 5)