Obtains predictions from an object fitted with gamem().
Usage
# S3 method for gamem
predict(object, ...)Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
model <- gamem(data_g,
gen = GEN,
rep = REP,
resp = everything())
#> Evaluating trait PH |=== | 7% 00:00:00
Evaluating trait EH |====== | 13% 00:00:00
Evaluating trait EP |========= | 20% 00:00:00
Evaluating trait EL |============ | 27% 00:00:00
Evaluating trait ED |=============== | 33% 00:00:01
Evaluating trait CL |================== | 40% 00:00:01
Evaluating trait CD |===================== | 47% 00:00:01
Evaluating trait CW |======================= | 53% 00:00:01
Evaluating trait KW |========================== | 60% 00:00:02
Evaluating trait NR |============================= | 67% 00:00:02
Evaluating trait NKR |================================ | 73% 00:00:02
Evaluating trait CDED |================================== | 80% 00:00:02
Evaluating trait PERK |==================================== | 87% 00:00:02
Evaluating trait TKW |======================================== | 93% 00:00:03
Evaluating trait NKE |===========================================| 100% 00:00:03
#> Method: REML/BLUP
#> Random effects: GEN
#> Fixed effects: REP
#> Denominador DF: Satterthwaite's method
#> ---------------------------------------------------------------------------
#> P-values for Likelihood Ratio Test of the analyzed traits
#> ---------------------------------------------------------------------------
#> model PH EH EP EL ED CL CD CW KW
#> Complete NA NA NA NA NA NA NA NA NA
#> Genotype 0.051 0.454 0.705 0.786 2.73e-05 2.25e-06 0.118 1.24e-05 0.0253
#> NR NKR CDED PERK TKW NKE
#> NA NA NA NA NA NA
#> 0.0056 0.216 9.14e-06 4.65e-07 0.00955 0.00952
#> ---------------------------------------------------------------------------
#> Variables with nonsignificant Genotype effect
#> PH EH EP EL CD NKR
#> ---------------------------------------------------------------------------
predict(model)
#> # A tibble: 39 × 17
#> GEN REP PH EH EP EL ED CL CD CW KW NR NKR
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 H1 1 2.12 1.06 0.502 14.9 50.5 31.5 16.0 26.9 156. 15.8 29.9
#> 2 H1 2 2.20 1.08 0.491 14.5 49.5 29.9 15.4 24.4 146. 16.1 28.6
#> 3 H1 3 2.24 1.11 0.494 14.6 50.7 30.6 16.0 27.1 159. 15.7 30.0
#> 4 H10 1 2.02 1.03 0.503 14.8 44.6 26.0 15.6 14.0 132. 15.4 32.1
#> 5 H10 2 2.10 1.06 0.492 14.4 43.7 24.4 15.1 11.6 122. 15.7 30.9
#> 6 H10 3 2.14 1.08 0.495 14.5 44.9 25.1 15.6 14.2 135. 15.3 32.2
#> 7 H11 1 2.06 1.06 0.507 14.9 47.4 27.4 15.7 17.3 145. 16.1 31.7
#> 8 H11 2 2.14 1.08 0.496 14.5 46.4 25.8 15.2 14.9 135. 16.4 30.5
#> 9 H11 3 2.18 1.11 0.499 14.5 47.6 26.5 15.8 17.5 148. 16.0 31.8
#> 10 H12 1 2.26 1.12 0.506 14.8 48.0 26.9 15.3 18.9 150. 16.1 30.4
#> # … with 29 more rows, and 4 more variables: CDED <dbl>, PERK <dbl>, TKW <dbl>,
#> # NKE <dbl>
# }