Print an object of class mgidi Print a mgidi object in two ways. By default, the results are shown in the R console. The results can also be exported to the directory.
Source: R/mgidi.R
print.mgidi.RdPrint an object of class mgidi
Print a mgidi object in two ways. By default, the results are shown in
the R console. The results can also be exported to the directory.
Usage
# S3 method for class 'mgidi'
print(x, export = FALSE, file.name = NULL, digits = 4, ...)Arguments
- x
An object of class
mgidi.- export
A logical argument. If
TRUE|T, a *.txt file is exported to the working directory- file.name
The name of the file if
export = TRUE- digits
The significant digits to be shown.
- ...
Options used by the tibble package to format the output. See
tibble::print()for more details.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
model <- gamem(data_g,
gen = GEN,
rep = REP,
resp = c(KW, NR, NKE, NKR))
#> Evaluating trait KW |=========== | 25% 00:00:00
Evaluating trait NR |====================== | 50% 00:00:00
Evaluating trait NKE |================================ | 75% 00:00:00
Evaluating trait NKR |===========================================| 100% 00:00:00
#> 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 KW NR NKE NKR
#> Complete NA NA NA NA
#> Genotype 0.0253 0.0056 0.00952 0.216
#> ---------------------------------------------------------------------------
#> Variables with nonsignificant Genotype effect
#> NKR
#> ---------------------------------------------------------------------------
mgidi_index <- mgidi(model)
#>
#> -------------------------------------------------------------------------------
#> Principal Component Analysis
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 4
#> PC Eigenvalues `Variance (%)` `Cum. variance (%)`
#> <chr> <dbl> <dbl> <dbl>
#> 1 PC1 2.42 60.6 60.6
#> 2 PC2 1.19 29.8 90.3
#> 3 PC3 0.32 8 98.3
#> 4 PC4 0.07 1.66 100
#> -------------------------------------------------------------------------------
#> Factor Analysis - factorial loadings after rotation-
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 5
#> VAR FA1 FA2 Communality Uniquenesses
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 KW -0.9 0.04 0.82 0.18
#> 2 NR -0.92 -0.12 0.87 0.13
#> 3 NKE -0.7 -0.69 0.96 0.04
#> 4 NKR 0.05 -0.98 0.97 0.03
#> -------------------------------------------------------------------------------
#> Comunalit Mean: 0.9033994
#> -------------------------------------------------------------------------------
#> Selection differential
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 11
#> VAR Factor Xo Xs SD SDperc h2 SG SGperc sense goal
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 KW FA1 147. 163. 16.2 11.0 0.659 10.7 7.27 increase 100
#> 2 NR FA1 15.8 17.4 1.63 10.3 0.736 1.20 7.60 increase 100
#> 3 NKE FA1 468. 532. 64.0 13.7 0.713 45.6 9.74 increase 100
#> 4 NKR FA2 30.4 31.2 0.814 2.68 0.452 0.368 1.21 increase 100
#> ------------------------------------------------------------------------------
#> Selected genotypes
#> -------------------------------------------------------------------------------
#> H13 H5
#> -------------------------------------------------------------------------------
print(mgidi_index)
#> -------------------------------------------------------------------------------
#> Correlation matrix used used in factor analysis
#> -------------------------------------------------------------------------------
#> KW NR NKE NKR
#> KW 1.000 0.688 0.56 -0.027
#> NR 0.688 1.000 0.73 0.047
#> NKE 0.560 0.731 1.00 0.610
#> NKR -0.027 0.047 0.61 1.000
#> -------------------------------------------------------------------------------
#> Principal component analysis
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 4
#> PC Eigenvalues `Variance (%)` `Cum. variance (%)`
#> <chr> <dbl> <dbl> <dbl>
#> 1 PC1 2.422 60.55 60.55
#> 2 PC2 1.192 29.79 90.34
#> 3 PC3 0.3201 8.002 98.34
#> 4 PC4 0.06631 1.658 100
#> -------------------------------------------------------------------------------
#> Initial loadings
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 3
#> VAR PC1 PC2
#> <chr> <dbl> <dbl>
#> 1 KW -0.7827 0.4545
#> 2 NR -0.8731 0.3232
#> 3 NKE -0.9353 -0.2852
#> 4 NKR -0.4153 -0.8939
#> -------------------------------------------------------------------------------
#> Loadings after varimax rotation
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 3
#> VAR FA1 FA2
#> <chr> <dbl> <dbl>
#> 1 KW -0.9042 0.04004
#> 2 NR -0.9235 -0.1182
#> 3 NKE -0.6966 -0.6862
#> 4 NKR 0.04631 -0.9846
#> -------------------------------------------------------------------------------
#> Scores for genotypes-ideotype
#> -------------------------------------------------------------------------------
#> # A tibble: 14 × 3
#> GEN FA1 FA2
#> <chr> <dbl> <dbl>
#> 1 H1 -1.615 -0.2874
#> 2 H10 -0.2495 -2.888
#> 3 H11 -1.220 -2.278
#> 4 H12 -1.813 -1.450
#> 5 H13 -3.589 -2.244
#> 6 H2 -1.816 -0.6748
#> 7 H3 -0.5856 -1.045
#> 8 H4 -0.2922 -2.673
#> 9 H5 -2.432 -2.315
#> 10 H6 -1.405 0.2438
#> 11 H7 -1.449 -0.8412
#> 12 H8 -0.6321 -0.4303
#> 13 H9 0.01742 -1.398
#> 14 ID1 -3.458 -2.745
#> -------------------------------------------------------------------------------
#> Multi-trait genotype-ideotype distance index
#> -------------------------------------------------------------------------------
#> # A tibble: 13 × 2
#> Genotype MGIDI
#> <chr> <dbl>
#> 1 H13 0.5181
#> 2 H5 1.112
#> 3 H12 2.093
#> 4 H11 2.286
#> 5 H2 2.642
#> 6 H7 2.767
#> 7 H1 3.072
#> 8 H4 3.166
#> 9 H10 3.211
#> 10 H3 3.337
#> 11 H6 3.626
#> 12 H8 3.653
#> 13 H9 3.727
#> -------------------------------------------------------------------------------
#> Selection differential
#> -------------------------------------------------------------------------------
#> # A tibble: 4 × 11
#> VAR Factor Xo Xs SD SDperc h2 SG SGperc sense goal
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 KW FA1 146.8 163.0 16.19 11.03 0.6594 10.67 7.271 increase 100
#> 2 NR FA1 15.78 17.42 1.630 10.33 0.7359 1.200 7.601 increase 100
#> 3 NKE FA1 467.9 531.9 63.97 13.67 0.7126 45.58 9.742 increase 100
#> 4 NKR FA2 30.4 31.21 0.8142 2.678 0.4523 0.3683 1.211 increase 100
#> -------------------------------------------------------------------------------
#> Selected genotypes
#> -------------------------------------------------------------------------------
#> H13 H5
# }