Print method for multisite simulation data
Source:R/class-multisitedgp_data.R
print.multisitedgp_data.RdPrint a multisitedgp_data object with a four-axis "realized vs.
intended" header summarizing the most-relevant diagnostics, followed
by the underlying tibble. The header lists, for each canonical
diagnostic axis (informativeness I, heterogeneity ratio R,
\(\sigma_\tau\), dependence \(\rho_S\)), the realized value and
the design target (or "no target" when the design did not constrain
that axis), plus a per-axis pass / warn / fail flag.
Usage
# S3 method for class 'multisitedgp_data'
print(x, n = 6L, ...)Details
The header is a one-glance summary — when something looks off, run
summary(x) for the full diagnostic report including all
Group A/B/C/D scalars, the realized residual / marginal Spearman /
Pearson correlations, and the feasibility status.
See also
summary.multisitedgp_data for the full
diagnostic report.
Other family-design:
as_tibble.multisitedgp_data(),
design_grid(),
is_multisitedgp_data(),
is_multisitedgp_design(),
multisitedgp_design(),
summary.multisitedgp_data(),
update_multisitedgp_design(),
validate_multisitedgp_design()
Examples
dat <- sim_multisite(J = 10L, seed = 1L)
print(dat, n = 3)
#> # A multisitedgp_data: 10 sites, paradigm = "site_size"
#> # Realized vs intended:
#> # I: realized=0.336 (no target)
#> # R: realized=6.316 (no target)
#> # sigma_tau: target=0.200, realized=0.156, FAIL
#> # rho_S: target=0.000, realized=-0.467, PASS
#> # rho_S_marg: realized=-0.467 (no target)
#> # Feasibility: FAIL (n_eff=3.454)
#> # A tibble: 10 × 7
#> site_index z_j tau_j tau_j_hat se_j se2_j n_j
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 -0.626 -0.125 0.296 0.459 0.211 19
#> 2 2 0.184 0.0367 0.226 0.243 0.0588 68
#> 3 3 -0.836 -0.167 -0.143 0.329 0.108 37
#> # ℹ 7 more rows
#> # Use summary(df) for the full diagnostic report.