One row per released quantity: what it is, how many numbers it holds, and
the smallest number of patients standing behind any one of them. That last
column is where disclosure risk sits. A grid cell is backed by the patients
who reached it, a covariate mean by the whole cohort, and a covariate level
by the patients who hold it, at least min_category_patients.
Arguments
- x
A dataset from
synpmx_pca(), or the fit itself.
Examples
data <- pmx_simulated_fixture(60)
pca_report(synpmx_pca(data, pmx_generated_roles(), seed = 1))
#> What the PCA fit read out of the source data
#>
#> subjects: 60 components retained: 1
#>
#> quantity
#> visit grid
#> feature centers
#> feature scales
#> loadings
#> score means
#> score covariance
#> endpoint transforms
#> assay limits
#> dosing model
#> visit model
#> arm constants
#> what numbers
#> Nominal times modelled, per endpoint 14
#> Mean of each grid cell and covariate 14
#> Standard deviation of the same 14
#> Component loadings on each feature 14
#> Mean score vector, per arm 1
#> Residual covariance between components 1
#> Log or identity, per endpoint 2
#> Censoring boundary, per endpoint 0
#> Planned cycles per arm; the dose ladder and three rates, pooled 8
#> One attendance rate per endpoint, at the visits each arm has 14
#> Strata and kept columns, one value per arm 0
#> min_patients
#> 60
#> 60
#> 60
#> 60
#> 60
#> 60
#> 60
#> 60
#> 60
#> 60
#> 60