Skip to contents

Component loadings over time, and the variance each component explains

Source: R/pca.R
pca_components.Rd

The loadings are what makes a principal component readable. Plotted against time rather than tabulated, a component that is flat and positive is overall magnitude and one that crosses zero separates early from late.

Usage

pca_components(x)

Arguments

x

A dataset from synpmx_pca(), or the fit itself.

Value

A data frame with one row per component and retained grid cell, carrying variance_explained as an attribute.

See also

synpmx_pca(), pca_report().

Examples

data <- pmx_simulated_fixture(60)
head(pca_components(synpmx_pca(data, pmx_generated_roles(), seed = 1)))
#>    feature          kind endpoint  time covariate patients component    loading
#> 1 dv_cp__1 endpoint_cell       cp  0.25      <NA>       60       PC1 -0.2672606
#> 2 dv_cp__2 endpoint_cell       cp  1.00      <NA>       60       PC1 -0.2672596
#> 3 dv_cp__3 endpoint_cell       cp  2.00      <NA>       60       PC1 -0.2672610
#> 4 dv_cp__4 endpoint_cell       cp  6.00      <NA>       60       PC1 -0.2672620
#> 5 dv_cp__5 endpoint_cell       cp 12.25      <NA>       60       PC1 -0.2672614
#> 6 dv_cp__6 endpoint_cell       cp 13.00      <NA>       60       PC1 -0.2672595