regression.stan File Reference

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Functions
vector	regression_predictor (array[] real intercept, vector beta, int nobs, int neffs, matrix fdesign, tuple(vector, array[] int, array[] int) sparse, vector beta_sd, matrix rdesign, int add_intercept, int sparse_design, int arima_present, int arima_T, int arima_G, int arima_p, int arima_d, int arima_q, int arima_n_obs, matrix arima_z, vector arima_pacf, vector arima_theta, array[] real arima_sigma, array[] int arima_flat_idx, int gp_present, int gp_T, int gp_G, int gp_M, real gp_L, int gp_type, real gp_nu, int gp_d, matrix gp_PHI, matrix gp_eta, array[] real gp_rho, array[] real gp_alpha, array[] int gp_flat_idx)
void	regression_priors_lp (vector beta, vector beta_sd, array[,] real beta_sd_p, int fixed, int random, int arima_present, int arima_p, int arima_q, matrix arima_z, vector arima_pacf, vector arima_theta, array[] real arima_sigma, array[,] real arima_sigma_p, array[,] real arima_pacf_p)
void	gp_priors_lp (int gp_present, matrix gp_eta, array[] real gp_rho, array[] real gp_alpha, array[,] real gp_rho_p, array[,] real gp_alpha_p)

Function Documentation

gp_priors_lp()

void gp_priors_lp	(	int	gp_present,
		matrix	gp_eta,
		array[]real	gp_rho,
		array[]real	gp_alpha,
		array real	gp_rho_p[,],
		array real	gp_alpha_p[,] )

Priors for an approximate Gaussian process latent term.

The spectral coefficients eta get a unit-normal (non-centred) prior. The length scale rho gets a log-normal prior and the magnitude alpha a half-normal prior, with each prior (mean, sd) supplied as data. Inert when gp_present == 0.

Definition at line 111 of file regression.stan.

regression_predictor()

vector regression_predictor	(	array[]real	intercept,
		vector	beta,
		int	nobs,
		int	neffs,
		matrix	fdesign,
		tuple(vector, array[] int, array[] int)	sparse,
		vector	beta_sd,
		matrix	rdesign,
		int	add_intercept,
		int	sparse_design,
		int	arima_present,
		int	arima_T,
		int	arima_G,
		int	arima_p,
		int	arima_d,
		int	arima_q,
		int	arima_n_obs,
		matrix	arima_z,
		vector	arima_pacf,
		vector	arima_theta,
		array[]real	arima_sigma,
		array[]int	arima_flat_idx,
		int	gp_present,
		int	gp_T,
		int	gp_G,
		int	gp_M,
		real	gp_L,
		int	gp_type,
		real	gp_nu,
		int	gp_d,
		matrix	gp_PHI,
		matrix	gp_eta,
		array[]real	gp_rho,
		array[]real	gp_alpha,
		array[]int	gp_flat_idx )

Single entry points for the regression layer.

regression_predictor() composes combine_effects() (fixed + partially-pooled random effects via the design matrices) with apply_arima_residual() (parameter-dependent ARIMA(p, d, q) latent residual) and apply_gp_term() (Hilbert-space approximate Gaussian process). Each module that owns a per-observation predictor calls this once instead of calling the layers separately, so every module routed through it gains the ARIMA and GP latent terms for free.

regression_priors_lp() is the matching one-shot priors helper: effect priors plus ARIMA priors (shocks, MA coefficients, latent standard deviation; partial autocorrelations get an implicit uniform prior via their parameter bounds). GP priors are supplied by the separate gp_priors_lp() helper so the long argument list stays manageable.

Both layers are inert when their term is absent: when arima_present == 0 and gp_present == 0, regression_predictor() returns the bare combine_effects() result; each apply_* call short-circuits on its own presence flag.

Every per-observation module routes through these helpers: expr, expl, refnp, and miss apply the latent terms directly at the observation level, while the sparse-row modules refp and rep supply a flat_idx built from the joint (covariate row x time x group) deduplication so the same gather works at sparse-row granularity. The ARIMA and GP terms share this scheme. See vignettes/arima.Rmd and vignettes/gaussian-process.Rmd.

Definition at line 32 of file regression.stan.

regression_priors_lp()

void regression_priors_lp	(	vector	beta,
		vector	beta_sd,
		array real	beta_sd_p[,],
		int	fixed,
		int	random,
		int	arima_present,
		int	arima_p,
		int	arima_q,
		matrix	arima_z,
		vector	arima_pacf,
		vector	arima_theta,
		array[]real	arima_sigma,
		array real	arima_sigma_p[,],
		array real	arima_pacf_p[,] )

Definition at line 76 of file regression.stan.