spFW (version 0.1.0)

HSFWM_est: Estimation Function for Hierarchical Spatial Finlay-Wilkinson Model

Description

This function considers spatial adjustments.

Usage

HSFWM_est(Y, VAR, ENV, COOR, 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

COOR

A N by 2 numerical matrix indicating the spatial locations 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

# NOT RUN {
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
fit1 <- HSFWM_est(Y, VAR, ENV, COOR,
                  M_iter = 1000, burn_in = 500, thin = 5)

# plot estimated Y
plot(Y, fit1$yhat)


# }