Print the ge_factanal
object in two ways. By default, the results are
shown in the R console. The results can also be exported to the directory.
# S3 method for ge_factanal print(x, export = FALSE, file.name = NULL, digits = 4, ...)
x | An object of class |
---|---|
export | A logical argument. If |
file.name | The name of the file if |
digits | The significant digits to be shown. |
... | Options used by the tibble package to format the output. See
|
Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ model <- ge_factanal(data_ge2, env = ENV, gen = GEN, rep = REP, resp = PH ) print(model)#> Variable PH #> ------------------------------------------------------------------------------------ #> Correlation matrix among environments #> ------------------------------------------------------------------------------------ #> # A tibble: 4 x 5 #> ENV A1 A2 A3 A4 #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 A1 1 0.3919 -0.4723 -0.4983 #> 2 A2 0.3919 1 -0.2203 0.2722 #> 3 A3 -0.4723 -0.2203 1 0.3238 #> 4 A4 -0.4983 0.2722 0.3238 1 #> ------------------------------------------------------------------------------------ #> Eigenvalues and explained variance #> ------------------------------------------------------------------------------------ #> # A tibble: 4 x 4 #> PCA Eigenvalues Variance Cumul_var #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 1.926 48.15 48.15 #> 2 PC2 1.263 31.58 79.74 #> 3 PC3 0.5928 14.82 94.56 #> 4 PC4 0.2178 5.444 100 #> ------------------------------------------------------------------------------------ #> Initial loadings #> ------------------------------------------------------------------------------------ #> # A tibble: 4 x 3 #> Env FA1 FA2 #> <chr> <dbl> <dbl> #> 1 A1 -0.8912 0.08731 #> 2 A2 -0.3718 0.8816 #> 3 A3 0.7660 -0.04547 #> 4 A4 0.6380 0.6902 #> ------------------------------------------------------------------------------------ #> Loadings after varimax rotation and commonalities #> ------------------------------------------------------------------------------------ #> # A tibble: 4 x 5 #> Env FA1 FA2 Communality Uniquenesses #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 A1 -0.8763 0.1839 0.8018 0.1982 #> 2 A2 -0.2735 0.9169 0.9154 0.08461 #> 3 A3 0.7564 -0.1287 0.5888 0.4112 #> 4 A4 0.7095 0.6166 0.8835 0.1165 #> ------------------------------------------------------------------------------------ #> Environmental stratification based on factor analysis #> ------------------------------------------------------------------------------------ #> # A tibble: 4 x 6 #> Env Factor Mean Min Max CV #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 A1 FA1 2.793 2.692 2.935 2.810 #> 2 A3 FA1 2.167 2.028 2.599 7.724 #> 3 A4 FA1 2.518 2.305 2.638 3.655 #> 4 A2 FA2 2.462 1.959 2.947 16.20 #> ------------------------------------------------------------------------------------ #> Mean = mean; Min = minimum; Max = maximum; CV = coefficient of variation (%) #> ------------------------------------------------------------------------------------ #> #> #># }