predict.performs_ammi: Predict the means of a performs_ammi object

In metan: Multi Environment Trials Analysis

Predict the means of a performs_ammi object

Description

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

Usage

## S3 method for class 'performs_ammi'
predict(object, naxis = 2, ...)

Arguments

object	An object of class performs_ammi
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

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 success (such as cross-validation) should be used to do this. This package provide both. performs_ammi() 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.

Value

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

Author(s)

Tiago Olivoto [e-mail]

Examples

library(metan)
model <- performs_ammi(data_ge, ENV, GEN, REP,
                       resp = c(GY, HM))
# Predict GY with 3 IPCA and HM with 1 IPCA
predict <- predict(model, naxis = c(3, 1))

metan documentation built on April 3, 2025, 6:04 p.m.