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

# S3 method for 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

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 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.

Examples

# \donttest{ library(metan) model <- performs_ammi(data_ge, ENV, GEN, REP, resp = c(GY, HM))
#> New names: #> * `` -> ...15
#> variable GY #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 13 279.574 21.5057 62.33 0.00e+00 . . #> REP(ENV) 28 9.662 0.3451 3.57 3.59e-08 . . #> GEN 9 12.995 1.4439 14.93 2.19e-19 . . #> GEN:ENV 117 31.220 0.2668 2.76 1.01e-11 . . #> PC1 21 10.749 0.5119 5.29 0.00e+00 34.4 34.4 #> PC2 19 9.924 0.5223 5.40 0.00e+00 31.8 66.2 #> PC3 17 4.039 0.2376 2.46 1.40e-03 12.9 79.2 #> PC4 15 3.074 0.2049 2.12 9.60e-03 9.8 89 #> PC5 13 1.446 0.1113 1.15 3.18e-01 4.6 93.6 #> PC6 11 0.932 0.0848 0.88 5.61e-01 3 96.6 #> PC7 9 0.567 0.0630 0.65 7.53e-01 1.8 98.4 #> PC8 7 0.362 0.0518 0.54 8.04e-01 1.2 99.6 #> PC9 5 0.126 0.0252 0.26 9.34e-01 0.4 100 #> Residuals 252 24.367 0.0967 NA NA . . #> Total 419 357.816 0.8540 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...15
#> variable HM #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 13 5710.32 439.255 57.22 1.11e-16 . . #> REP(ENV) 28 214.93 7.676 2.70 2.20e-05 . . #> GEN 9 269.81 29.979 10.56 7.41e-14 . . #> GEN:ENV 117 1100.73 9.408 3.31 1.06e-15 . . #> PC1 21 381.13 18.149 6.39 0.00e+00 34.6 34.6 #> PC2 19 319.43 16.812 5.92 0.00e+00 29 63.6 #> PC3 17 114.26 6.721 2.37 2.10e-03 10.4 74 #> PC4 15 81.96 5.464 1.92 2.18e-02 7.4 81.5 #> PC5 13 68.11 5.240 1.84 3.77e-02 6.2 87.7 #> PC6 11 59.07 5.370 1.89 4.10e-02 5.4 93 #> PC7 9 46.69 5.188 1.83 6.33e-02 4.2 97.3 #> PC8 7 26.65 3.808 1.34 2.32e-01 2.4 99.7 #> PC9 5 3.41 0.682 0.24 9.45e-01 0.3 100 #> Residuals 252 715.69 2.840 NA NA . . #> Total 419 8011.48 19.120 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #> #> All variables with significant (p < 0.05) genotype-vs-environment interaction #> Done!
# Predict GY with 3 IPCA and HM with 1 IPCA predict <- predict(model, naxis = c(3, 1)) # }