Defines a list specifying the structure of the approximate Gaussian
process. Custom settings can be supplied which override the defaults.
gp_opts(
basis_prop = 0.2,
boundary_scale = 1.5,
ls_mean = 21,
ls_sd = 7,
ls_min = 0,
ls_max = 60,
alpha_sd = 0.05,
kernel = "matern",
matern_type = 3/2
)
A list of settings defining the Gaussian process
Numeric, proportion of time points to use as basis functions. Defaults to 0.1. 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.
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.
Numeric, defaults to 21 days. The mean of the lognormal length scale.
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)
.
Numeric, defaults to 0. The minimum value of the length scale.
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.
Numeric, defaults to 0.05. The standard deviation of the magnitude parameter o the Gaussian process kernel. Should be approximately the expected standard deviation of the logged Rt.
Character string, the type of kernel required. Currently
supporting the squared exponential kernel ("se") and the 3 over 2 Matern
kernel ("matern", with matern_type = 3/2
). Defaulting to the Matern 3 over
2 kernel as discontinuities are expected in Rt and infections.
Numeric, defaults to 3/2. Type of Matern Kernel to use. Currently only the Matern 3/2 kernel is supported.
Sam Abbott
# default settings
gp_opts()
# add a custom length scale
gp_opts(ls_mean = 4)
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