cv.ahm {AHM}

This is one of the main functions. It perform the cross validation on ahm models to select the optimal setting of hyper parameter h

Description

This is one of the main functions. It perform the cross validation on ahm models to select the optimal setting of hyper parameter h

Usage

cv.ahm(y, x, powerh_path = NULL, metric = c("mse", "AICc"), 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"), 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 between major or minor components are needed.

powerh_path

if NULL, then the default is the vector: round(seq(0.001,2,length.out =15),3)

metric

"mse" or "AICc" the metric used in cross validtion where the minimum is selected as the optimal

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"

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]
powerh_path = round(seq(0.001,2,length.out =15),3)
num_major = 3; dist_minor = c(2,2,1)
res = cv.ahm (y, x, powerh_path=powerh_path, metric = "mse", num_major, dist_minor, type = "weak"
, alpha=0, lambda_seq=seq(0,5,0.01), nfolds=NULL, mapping_type = c("power"), rep_gcv=100)
object = res$metric_mse