log_expected_obs_from_latent.stan File Reference

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Functions
array[] vector	log_expected_obs_from_latent (array[] vector exp_llatent, int rd_n, vector w, array[] int v, array[] int u, int t, int g, vector latent_obs_prop)

Function Documentation

log_expected_obs_from_latent()

array[] vector log_expected_obs_from_latent	(	array[]vector	exp_llatent,
		int	rd_n,
		vector	w,
		array[]int	v,
		array[]int	u,
		int	t,
		int	g,
		vector	latent_obs_prop
	)

Compute log of expected observations from latent values

This function calculates the expected observations in log scale based on latent expected values, weighting factors, and observational proportions. The weighting factors are derived from a sparse matrix, which is constructed using the csr_extract and convolution_matrix R functions in epinowcast.

Parameters

exp_llatent	Array of vectors of log latent expected values.
rd_n	Length of the reporting delay (1 for immediate reporting).
w	Vector of weighting factors derived from a sparse matrix.
v,u	Arrays for sparse matrix representation, as obtained from csr_extract.
t	Number of time periods.
g	Number of groups.
latent_obs_prop	Vector of observational proportions for latent values.

Returns

An array of vectors containing log-transformed expected observed values for each group and time period.

Note

The function performs different operations based on the value of rd_n:

1. If rd_n is 1 (immediate reporting): a. Directly adds the log latent values, log of weights, and observational proportions for each group.
2. If rd_n > 1 (delayed reporting): a. Uses a convolution matrix constructed using convolution_matrix or otherwise, representing reporting delays. b. Prior to being used as an input this is converted to a sparse matrix format using csr_extract. c. Applies the sparse matrix multiplication to the latent values in a sparse matrix multiplication. d. Converts the resulting values back to the log scale and adds the observational proportions for each group.

These steps account for different reporting delays and the distribution of observations over time.

See also

csr_matrix and convolution_matrix stan function and epinowcast R function for details on sparse matrix construction and convolution matrix generation.

Definition at line 50 of file log_expected_obs_from_latent.stan.