Predict the means of a waas object considering a specific number of axis.
Usage
# S3 method for class 'waas'
predict(object, naxis = 2, ...)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.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
model <- waas(data_ge,
env = ENV,
gen = GEN,
rep = REP,
resp = c(GY, HM))
#> variable GY
#> ---------------------------------------------------------------------------
#> AMMI analysis table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion Accumulated
#> ENV 13 279.574 21.5057 62.33 0.00e+00 NA NA
#> REP(ENV) 28 9.662 0.3451 3.57 3.59e-08 NA NA
#> GEN 9 12.995 1.4439 14.93 2.19e-19 NA NA
#> GEN:ENV 117 31.220 0.2668 2.76 1.01e-11 NA NA
#> 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 89.0
#> 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 3.0 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 0.4 100.0
#> Residuals 252 24.367 0.0967 NA NA NA NA
#> Total 536 389.036 0.7258 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable HM
#> ---------------------------------------------------------------------------
#> AMMI analysis table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion Accumulated
#> ENV 13 5710.32 439.255 57.22 1.11e-16 NA NA
#> REP(ENV) 28 214.93 7.676 2.70 2.20e-05 NA NA
#> GEN 9 269.81 29.979 10.56 7.41e-14 NA NA
#> GEN:ENV 117 1100.73 9.408 3.31 1.06e-15 NA NA
#> 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 29.0 63.6
#> PC3 17 114.26 6.721 2.37 2.10e-03 10.4 74.0
#> 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 93.0
#> 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 0.3 100.0
#> Residuals 252 715.69 2.840 NA NA NA NA
#> Total 536 9112.21 17.000 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))
predict
#> $GY
#> # A tibble: 140 × 7
#> ENV GEN Y res_ols pred_ols res_ammi[,1] pred_ammi[,1]
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 E1 G1 2.37 -0.0843 2.45 0.0777 2.53
#> 2 E1 G10 1.97 -0.344 2.32 -0.321 2.00
#> 3 E1 G2 2.90 0.311 2.59 0.244 2.83
#> 4 E1 G3 2.89 0.0868 2.80 -0.0626 2.74
#> 5 E1 G4 2.59 0.100 2.49 0.0354 2.52
#> 6 E1 G5 2.19 -0.196 2.38 -0.0886 2.30
#> 7 E1 G6 2.30 -0.0797 2.38 -0.106 2.27
#> 8 E1 G7 2.77 0.186 2.59 0.234 2.82
#> 9 E1 G8 2.90 0.0493 2.85 0.0112 2.86
#> 10 E1 G9 2.33 -0.0307 2.36 -0.0233 2.33
#> # ℹ 130 more rows
#>
#> $HM
#> # A tibble: 140 × 7
#> ENV GEN Y res_ols pred_ols res_ammi[,1] pred_ammi[,1]
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 E1 G1 46.5 0.117 46.4 0.0381 46.4
#> 2 E1 G10 46.9 -0.923 47.8 -0.242 47.6
#> 3 E1 G2 45.3 -0.660 46.0 0.213 46.2
#> 4 E1 G3 45.9 -1.05 46.9 0.0465 47.0
#> 5 E1 G4 48.3 0.931 47.4 -0.0275 47.3
#> 6 E1 G5 49.9 1.26 48.6 0.215 48.8
#> 7 E1 G6 48.2 0.189 48.1 0.0745 48.1
#> 8 E1 G7 47.4 0.154 47.3 -0.166 47.1
#> 9 E1 G8 48.0 -0.466 48.4 -0.00568 48.4
#> 10 E1 G9 47.7 0.450 47.2 -0.146 47.1
#> # ℹ 130 more rows
#>
# }