ahm {AHM}

This is one of the main functions. The function ahm computes the proposed additive heredity model.

Description

This is one of the main functions. The function ahm computes the proposed additive heredity model.

Usage

ahm(y, x, num_major = 3, dist_minor = c(2, 2, 1), type = "weak",
  alpha = 0, lambda_seq = seq(0, 5, 0.01), nfolds = NULL,
  mapping_type = c("power"), powerh = 0, rep_gcv = 100)

Arguments

y

numeric vector

x

data.frame Note the column names of the x should be in the order of major components, minor components, and no interactions are needed.

num_major

number of major components

dist_minor

the allocation of number of minor components nested under major components

type

heredity type, weak heredity is the current support type

alpha

0 is for the ridge in glmnet https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html

lambda_seq

a numeric vector for the options of lambda used in ridge regression for estimating the initials

nfolds

used in cv.glmnet for initial value of parameters in the non-negative garrote method

mapping_type

the form of the coefficient function of major components in front of corresponding minor terms. Currently only support "power"

powerh

the power parameter used for the power function

rep_gcv

the number of choices of tuning parameter used in the GCV selection

Value

Return a list

Examples

data("pringles_fat")
data_fat = pringles_fat
h_tmp = 1.3
x = data_fat[,c("c1","c2","c3","x11","x12","x21","x22")]
y = data_fat[,1]
out = ahm (y, x, num_major = 3, dist_minor = c(2,2,1),
           type = "weak", alpha=0, lambda_seq=seq(0,5,0.01), nfold = NULL,
           mapping_type = c("power"), powerh = h_tmp,
           rep_gcv=100)
summary(out)