ahm: This is one of the main functions. The function ahm computes...

In AHM: Additive Heredity Model: Method for the Mixture-of-Mixtures Experiments

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)

AHM documentation built on July 28, 2019, 9:02 a.m.