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 or a covariate mean is backed by the whole cohort, while a rare covariate level can be backed by a single patient.
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 what
#> visit grid Nominal times modelled, per endpoint
#> feature centers Mean of each grid cell and covariate
#> feature scales Standard deviation of the same
#> loadings Component loadings on each feature
#> score means Mean score vector, per arm
#> score covariance Residual covariance between components
#> endpoint transforms Log or identity, per endpoint
#> assay limits Censoring boundary, per endpoint
#> dosing model Planned cycles, the dose ladder, and three rates, per arm
#> visit model Probability of a visit, per arm, endpoint and time
#> arm constants Strata and kept columns, one value per arm
#> numbers min_patients
#> 14 60
#> 14 60
#> 14 60
#> 14 60
#> 1 60
#> 1 60
#> 2 60
#> 0 60
#> 8 60
#> 14 60
#> 0 60