Adaptive hierarchy single image #2
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Implements conditional variance structure to address high uncertainty in spatial interaction curves (SICs) when patients have only one image. Changes: - Preprocessing detects single-image patients and passes flag to Stan - Stan models collapse image-level variance for single-image patients - Multi-image patients retain full 3-level hierarchical structure This resolves identifiability issues where image and patient levels were confounded, reducing unnecessary uncertainty in estimates.
When num_pt_groups == 1, the model cannot estimate between-group variance since there's no replication at the group level. This commit modifies the hierarchy to: - Estimate beta_global as fixed population mean with direct prior - Skip estimation of sigma_beta_global (undefined with one group) - Patients still vary around the group mean via sigma_beta_indiv Changes apply to both centered (SHADE.stan) and non-centered (SHADE_ncp.stan) parameterizations. Three cases now handled: - num_pt_groups > 1: Full hierarchy with between-group variance - num_pt_groups == 1: Fixed group mean, patient variation only - num_pt_groups == 0: Two-level hierarchy (patient + image)
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