AVGAS {AVGAS}

A Variable selection using Genetic AlgorithmS

Description

A Variable selection using Genetic AlgorithmS

Usage

AVGAS(
  X,
  y,
  heredity = "Strong",
  nmain.p,
  r1,
  r2,
  sigma = NULL,
  interaction.ind = NULL,
  lambda = 10,
  q = 40,
  allout = "No",
  interonly = "No",
  pi1 = 0.32,
  pi2 = 0.32,
  pi3 = 0.32,
  aprob = 0.9,
  dprob = 0.9,
  aprobm = 0.1,
  aprobi = 0.9,
  dprobm = 0.9,
  dprobi = 0.1,
  take = 3
)

Arguments

X

Input data. An optional data frame, or numeric matrix of dimension n by nmain.p. Note that the two-way interaction effects should not be included in X because this function automatically generates the corresponding two-way interaction effects if needed.

y

Response variable. A n-dimensional vector, where n is the number of observations in X.

heredity

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

nmain.p

A numeric value that represents the total number of main effects in X.

r1

A numeric value indicating the maximum number of main effects. This number can be different from the r1 defined in detect.

r2

A numeric value indicating the maximum number of interaction effects. This number can be different from the r1 defined in detect.

sigma

The standard deviation of the noise term. In practice, sigma is usually unknown. In such case, this function automatically estimate sigma using root mean square error (RMSE). Default is NULL. Otherwise, users need to enter a numeric value.

interaction.ind

A two-column numeric matrix containing all possible two-way interaction effects. It must be generated outside of this function using t(utils::combn()). See Example section for details.

lambda

A numeric value defined by users. Default is 10. For guidance on selecting an appropriate value, please refer to the Details section.

q

A numeric value indicating the number of models in each generation (e.g., the population size). Default is 40.

allout

Whether to print all outputs from this function. A "Yes" or "No" logical vector. Default is "No". See Value section for details.

interonly

Whether or not to consider fitted models with only two-way interaction effects. A “Yes" or "No" logical vector. Default is "No".

pi1

A numeric value between 0 and 1, defined by users. Default is 0.32. For guidance on selecting an appropriate value, please refer to ABC.

pi2

A numeric value between 0 and 1, defined by users. Default is 0.32. For guidance on selecting an appropriate value, please refer to ABC.

pi3

A numeric value between 0 and 1, defined by users. Default is 0.32. For guidance on selecting an appropriate value, please refer to ABC.

aprob

A numeric value between 0 and 1, defined by users. The addition probability during mutation. Default is 0.9.

dprob

A numeric value between 0 and 1, defined by users. The deletion probability during mutation. Default is 0.9.

aprobm

A numeric value between 0 and 1, defined by users. The main effect addition probability during addition. Default is 0.1.

aprobi

A numeric value between 0 and 1, defined by users. The interaction effect addition probability during addition. Default is 0.9.

dprobm

A numeric value between 0 and 1, defined by users. The main effect deletion probability during deletion. Default is 0.9.

dprobi

A numeric value between 0 and 1, defined by users. The interaction effect deletion probability during deletion. Default is 0.1.

take

Only used when allout = "No". Number of top candidate models to display. Default is 3.

Value

A list of output. The components are:

final_model

The final selected model.

cleaned_candidate_model

All candidate models where each row corresponding to a fitted model; the first 1 to r1 + r2 columns representing the predictor indices in that model, and the last column is a numeric value representing the ABC score of that fitted model. Duplicated models are not allowed.

InterRank

Rank of all candidate interaction effects. A two-column numeric matrix. The first column contains indices of ranked two-way interaction effects, and the second column contains its corresponding ABC score.

See Also

initial, cross, mut, ABC, Genone, and Extract.

Examples

# allout = "No"


# allout = "Yes"