metan (version 1.2.1)

predict.waas: Predict the means of a waas object

Description

Predict the means of a waas object considering a specific number of axis.

Usage

# S3 method for waas
predict(object, naxis = 2, ...)

Arguments

object

An object of class waas

naxis

The the number of axis to be use in the prediction. If object has more than one variable, then naxis must be a vector.

...

Additional parameter for the function

Value

A list where each element is the predicted values by the AMMI model for each variable.

Details

This function is used to predict the response variable of a two-way table (for examples the yielding of the i-th genotype in the j-th environment) based on AMMI model. This prediction is based on the number of multiplicative terms used. If naxis = 0, only the main effects (AMMI0) are used. In this case, the predicted mean will be the predicted value from OLS estimation. If naxis = 1 the AMMI1 (with one multiplicative term) is used for predicting the response variable. If naxis = min(gen-1;env-1), the AMMIF is fitted and the predicted value will be the cell mean, i.e. the mean of R-replicates of the i-th genotype in the j-th environment. The number of axis to be used must be carefully chosen. Procedures based on Postdictive success (such as Gollobs's d.f.) or Predictive sucess (such as cross-validation) should be used to do this. This package provide both. waas function compute traditional AMMI analysis showing the number of significant axis. On the other hand, cv_ammif function provide a cross-validation, estimating the RMSPD of all AMMI-family models, based on resampling procedures.

Examples

# NOT RUN {
library(metan)
model = waas(data_ge,
             env = ENV,
             gen = GEN,
             rep = REP,
             resp = c(GY, HM))
# Predict GY with 3 IPCA and HM with 1 IPCA
predict = predict(model, naxis = c(3, 1))

# }