gp_opts: Approximate Gaussian Process Settings

In epiforecasts/EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Approximate Gaussian Process Settings

Description

Defines a list specifying the structure of the approximate Gaussian process. Custom settings can be supplied which override the defaults.

Usage

gp_opts(
  basis_prop = 0.2,
  boundary_scale = 1.5,
  ls_mean = 21,
  ls_sd = 7,
  ls_min = 0,
  ls_max = 60,
  alpha = Normal(mean = 0, sd = 0.01),
  kernel = c("matern", "se", "ou", "periodic"),
  matern_order = 3/2,
  matern_type,
  w0 = 1,
  alpha_mean,
  alpha_sd
)

Arguments

basis_prop	Numeric, the proportion of time points to use as basis functions. Defaults to 0.2. Decreasing this value results in a decrease in accuracy but a faster compute time (with increasing it having the first effect). In general smaller posterior length scales require a higher proportion of basis functions. See (Riutort-Mayol et al. 2020 https://arxiv.org/abs/2004.11408) for advice on updating this default.
boundary_scale	Numeric, defaults to 1.5. Boundary scale of the approximate Gaussian process. See (Riutort-Mayol et al. 2020 https://arxiv.org/abs/2004.11408) for advice on updating this default.
ls_mean	Numeric, defaults to 21 days. The mean of the lognormal length scale.
ls_sd	Numeric, defaults to 7 days. The standard deviation of the log normal length scale. If ls_sd = 0, inverse-gamma prior on Gaussian process length scale will be used with recommended parameters inv_gamma(1.499007, 0.057277 * ls_max).
ls_min	Numeric, defaults to 0. The minimum value of the length scale.
ls_max	Numeric, defaults to 60. The maximum value of the length scale. Updated in create_gp_data() to be the length of the input data if this is smaller.
alpha	A ⁠<dist_spec>⁠ giving the prior distribution of the magnitude parameter of the Gaussian process kernel. Should be approximately the expected standard deviation of the Gaussian process (logged Rt in case of the renewal model, logged infections in case of the nonmechanistic model). Defaults to a half-normal distribution with mean 0 and sd 0.01: Normal(mean = 0, sd = 0.01) (a lower limit of 0 will be enforced automatically to ensure positivity)
kernel	Character string, the type of kernel required. Currently supporting the Matern kernel ("matern"), squared exponential kernel ("se"), periodic kernel, Ornstein-Uhlenbeck #' kernel ("ou"), and the periodic kernel ("periodic").
matern_order	Numeric, defaults to 3/2. Order of Matérn Kernel to use. Common choices are 1/2, 3/2, and 5/2. If kernel is set to "ou", matern_order will be automatically set to 1/2. Only used if the kernel is set to "matern".
matern_type	Deprecated; Numeric, defaults to 3/2. Order of Matérn Kernel to use. Currently, the orders 1/2, 3/2, 5/2 and Inf are supported.
w0	Numeric, defaults to 1.0. Fundamental frequency for periodic kernel. They are only used if kernel is set to "periodic".
alpha_mean	Deprecated; use alpha instead.
alpha_sd	Deprecated; use alpha instead.

Value

A ⁠<gp_opts>⁠ object of settings defining the Gaussian process

Examples

# default settings
gp_opts()

# add a custom length scale
gp_opts(ls_mean = 4)

# use linear kernel
gp_opts(kernel = "periodic")

epiforecasts/EpiNow2 documentation built on Dec. 10, 2024, 5:32 p.m.