Repository Mirror for your Cloud Server and Webhosting

Type:

Package

Title:

Hierarchical Spatial Finlay-Wilkinson Model

Version:

0.1.0

Author:

Xingche Guo <xguo@iastate.edu>

Maintainer:

Xingche Guo <xguo@iastate.edu>

Description:

Estimation and Prediction Functions Using Bayesian Hierarchical Spatial Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

6.1.1

Depends:

R (≥ 2.10)

LinkingTo:

Rcpp, RcppArmadillo

Imports:

Rcpp

NeedsCompilation:

yes

Packaged:

2022-03-29 07:41:56 UTC; apple

Repository:

CRAN

Date/Publication:

2022-03-29 18:40:02 UTC

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)

Prediction Function for Hierarchical Finlay-Wilkinson Model

Description

This function ignores spatial effects.

Usage

HFWM_pred(Y, VAR, ENV, VAR2, ENV2, save_int = FALSE, kin_info = FALSE,
  A = NULL, inits = NULL, hyper_para = NULL, M_iter = 5000,
  burn_in = 3000, thin = 5, seed = NULL)

Arguments

Y

A length-N1 numerical response vector from training set

VAR

A length-N1 factor/character vector indicating the genotype information of Y

ENV

A length-N1 factor/character vector indicating the field information of Y

VAR2

A length-N2 factor/character vector indicating the genotype information of testing set

ENV2

A length-N2 factor/character vector indicating the field information of of testing set

save_int

A logical parameter controling whether to save prediction credible intervals

kin_info

A logical parameter controling if to use kinship matrix

A

kinship matrix, give value only if kin_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

seed

Random seed value

Value

Mean prediction values and/or prediction intervals

Examples

library(spFW)

# load and split data
data(spFW_example_data)
idx_pred <- sample(125, 25)

Y0 <- spFW_example_data$yield
VAR0 <- spFW_example_data$geno
ENV0 <- spFW_example_data$loc


Y1 <- Y0[-idx_pred]
Y2 <- Y0[idx_pred]
VAR1 <- VAR0[-idx_pred]
VAR2 <- VAR0[idx_pred]
ENV1 <- ENV0[-idx_pred]
ENV2 <- ENV0[idx_pred]
order_y <- order(Y2)

# run model
pred0 <- HFWM_pred(Y1, VAR1, ENV1, VAR2, ENV2, save_int = TRUE,
                   M_iter = 1000, burn_in = 500, thin = 5)

# visualize prediction results
plot(1:25, pred0$PY[order_y], ylim = c(50, 250), pch = 15, col = "red",
     xlab = "Plant ID for Prediction", ylab = "Yield",
     main = "95% Prediction Intervals with Predicted Mean (Red) Versus True Yield (Blue)")
points(1:25, Y2[order_y], col = "blue")
for (i in 1:25){
  lines(x = c(i,i), y = c(pred0$PY_CI[,order_y][1,i], pred0$PY_CI[,order_y][4,i]))
}

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

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)

Prediction Function for Hierarchical Spatial Finlay-Wilkinson Model

Description

This function considers spatial adjustments.

Usage

HSFWM_pred(Y, VAR, ENV, COOR, VAR2, ENV2, COOR2, save_int = FALSE,
  kin_info = FALSE, A = NULL, inits = NULL, hyper_para = NULL,
  M_iter = 5000, burn_in = 3000, thin = 5, seed = NULL)

Arguments

Y

A length-N1 numerical response vector from training set

VAR

A length-N1 factor/character vector indicating the genotype information of Y

ENV

A length-N1 factor/character vector indicating the field information of Y

COOR

A N1 by 2 numerical matrix indicating the spatial locations of Y

VAR2

A length-N2 factor/character vector indicating the genotype information of testing set

ENV2

A length-N2 factor/character vector indicating the field information of of testing set

COOR2

A N2 by 2 numerical matrix indicating the spatial locations of testing set

save_int

A logical parameter controling whether to save prediction credible intervals

kin_info

A logical parameter controling if to use kinship matrix

A

kinship matrix, give value only if kin_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

seed

Random seed value

Value

Mean prediction values and/or prediction intervals

Examples

library(spFW)

# load and split data
data(spFW_example_data)
idx_pred <- sample(125, 25)

Y0 <- spFW_example_data$yield
VAR0 <- spFW_example_data$geno
ENV0 <- spFW_example_data$loc
COOR0 <- spFW_example_data[,c(4,5)]

Y1 <- Y0[-idx_pred]
Y2 <- Y0[idx_pred]
VAR1 <- VAR0[-idx_pred]
VAR2 <- VAR0[idx_pred]
ENV1 <- ENV0[-idx_pred]
ENV2 <- ENV0[idx_pred]
COOR1 <- COOR0[-idx_pred,]
COOR2 <- COOR0[idx_pred,]
order_y <- order(Y2)


# run model
pred1 <- HSFWM_pred(Y1, VAR1, ENV1, COOR1, VAR2, ENV2, COOR2, save_int = TRUE,
                    M_iter = 1000, burn_in = 500, thin = 5)

# visualize prediction results
plot(1:25, pred1$PY[order_y], ylim = c(50, 250), pch = 15, col = "red",
     xlab = "Plant ID for Prediction", ylab = "Yield",
     main = "95% Prediction Intervals with Predicted Mean (Red) Versus True Yield (Blue)")
points(1:25, Y2[order_y], col = "blue")
for (i in 1:25){
  lines(x = c(i,i), y = c(pred1$PY_CI[,order_y][1,i], pred1$PY_CI[,order_y][4,i]))
}

Example Data for Analysis

Description

A data frame containing the plant yield, genotypes, environments, and within-field positions.

Usage

spFW_example_data

Format

A data frame with 5 elements, which are:

yield: Plant yield
geno: Plant genotype ID
loc: Plant environment ID
range: Plant x-coordinate at a given environment
pass: Plant y-coordinate at a given environment