Creates a table of variance components derived from a user-defined covariance matrix.
Value
A table which partitions the total variance into main effect and interaction variance,
heterogeneity of variance and lack of correlation, and non-crossover and crossover interaction.
When correction = TRUE
, a sample correction is applied to the variance components.
Examples
# Generate a structured covariance matrix and then measure the partitioning of variance.
diag_mat <- rand_diag_mat(
n = 10
)
cor_mat <- struc_cor_mat(
n = 10,
)
cov_mat <- sqrt(diag_mat) %*% cor_mat %*% sqrt(diag_mat)
measure_variances(
mat = cov_mat
)
#> Component Variance Proportion
#> 1 Main effect 0.29577237 0.50755525
#> 2 Interaction 0.28696690 0.49244475
#> 3 Heterogeniety of scale 0.02264701 0.03886302
#> 4 Lack of correlation 0.26431989 0.45358173
#> 5 Non-crossover 0.32363778 0.55537322
#> 6 Crossover 0.25910149 0.44462678
#> 7 Total 0.58273927 1.00000000