hySAINT (version 1.2.1)

Mutation: Performing mutation

Description

This function gives mutant from parents.

Usage

Mutation(
  myParent,
  EVAoutput,
  r1,
  r2,
  initial.temp = 1000,
  cooling.rate = 0.95,
  X,
  y,
  heredity = "Strong",
  sigma,
  varind = NULL,
  interaction.ind = NULL,
  lambda = 10
)

Value

Mutant. A numeric matrix with dimensions numElite by r1+r2.

Arguments

myParent

A numeric matrix with dimension numElite by r1 + r2.

EVAoutput

The output from function EVA.

r1

At most how many main effects do you want to include in your model?. For high-dimensional data, r1 cannot be larger than the number of screened main effects.

r2

At most how many interaction effects do you want to include in your model?

initial.temp

Initial temperature. Default is 1000.

cooling.rate

A numeric value represents the speed at which the temperature decreases. Default is 0.95.

X

Input data. An optional data frame, or numeric matrix of dimension n observations by p main effects.

y

Response variable. A n-dimensional vector.

heredity

Whether to enforce Strong, Weak, or No heredity. Default is "Strong".

sigma

The standard deviation of the noise term. In practice, sigma is usually unknown. Users can estimate sigma from function selectiveInference::estimateSigma, then use the output as the sigma value.

varind

A numeric vector that specifies the indices of variables to be extracted from X.

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 t(utils::combn(p,2)).

lambda

A numeric value defined by users. The number needs to satisfy the condition: \(\lambda\geq 5.1/log(2)\). Default is 10.

See Also

EVA, Initial.

Examples

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
EVAoutput <- EVA(X, y, r1 = 5, sigma = 0.01,
  interaction.ind = interaction.ind)
myParent <- Initial(X = X, y = y, EVAoutput, r1 = 5, r2 = 2)
Mutation(myParent, EVAoutput, r1 = 5, r2 = 2, X = X, y = y,
  sigma = 0.1, interaction.ind = interaction.ind)