HFWM_est: Estimation Function for Hierarchical Finlay-Wilkinson Model

In spFW: Hierarchical Spatial Finlay-Wilkinson Model

Estimation Function for Hierarchical Finlay-Wilkinson Model

Description

This function ignores spatial effects.

Usage

HFWM_est(Y, VAR, ENV, kin_info = FALSE, A = NULL, env_info = FALSE,
  Z = NULL, inits = NULL, hyper_para = NULL, M_iter = 5000,
  burn_in = 3000, thin = 5, save_chain = FALSE, seed = NULL)

Arguments

Y	A length-N numerical response vector
VAR	A length-N factor/character vector indicating the genotype information of Y
ENV	A length-N factor/character vector indicating the field information of Y
kin_info	A logical parameter controling if to use kinship matrix
A	kinship matrix, give value only if kin_info = TRUE
env_info	A logical parameter controling whether to use environmental covariates
Z	environmental covariates matrix with rownames = field names, give value only if env_info = TRUE
inits	initial values, default is given
hyper_para	hyper-parameter values, default is given
M_iter	Total iteration number
burn_in	Burn in number
thin	Thinning value
save_chain	A logical parameter controling whether to save MCMC chain: 'Chains.rds' in current working directory
seed	Random seed value

Value

Mean estimates and RMSE value

Examples

library(spFW)

# load data
data(spFW_example_data)
Y <- spFW_example_data$yield
VAR <- spFW_example_data$geno
ENV <- spFW_example_data$loc
COOR <- spFW_example_data[,c(4,5)]

# run model
fit0 <- HFWM_est(Y, VAR, ENV, M_iter = 1000, burn_in = 500, thin = 5)

# plot estimated Y
plot(Y, fit0$yhat)

spFW documentation built on March 30, 2022, 1:06 a.m.