Approximate Gaussian Process Settings

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_mean = 0,
  alpha_sd = 0.01,
  kernel = c("matern", "se", "ou", "periodic"),
  matern_order = 3/2,
  matern_type,
  w0 = 1
)

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_mean: Numeric, defaults to 0. The mean of the magnitude parameter of the Gaussian process kernel. Should be approximately the expected variance of the logged Rt.
alpha_sd: Numeric, defaults to 0.01. The standard deviation of the magnitude parameter of the Gaussian process kernel. Should be approximately the expected standard deviation of the logged Rt.
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".

Value

A <gp_opts> object of settings defining the Gaussian process

Examples

# default settings
gp_opts()
#> $basis_prop
#> [1] 0.2
#> 
#> $boundary_scale
#> [1] 1.5
#> 
#> $ls_mean
#> [1] 21
#> 
#> $ls_sd
#> [1] 7
#> 
#> $ls_min
#> [1] 0
#> 
#> $ls_max
#> [1] 60
#> 
#> $alpha_mean
#> [1] 0
#> 
#> $alpha_sd
#> [1] 0.01
#> 
#> $kernel
#> [1] "matern"
#> 
#> $matern_order
#> [1] 1.5
#> 
#> $w0
#> [1] 1
#> 
#> attr(,"class")
#> [1] "gp_opts" "list"   

# add a custom length scale
gp_opts(ls_mean = 4)
#> $basis_prop
#> [1] 0.2
#> 
#> $boundary_scale
#> [1] 1.5
#> 
#> $ls_mean
#> [1] 4
#> 
#> $ls_sd
#> [1] 7
#> 
#> $ls_min
#> [1] 0
#> 
#> $ls_max
#> [1] 60
#> 
#> $alpha_mean
#> [1] 0
#> 
#> $alpha_sd
#> [1] 0.01
#> 
#> $kernel
#> [1] "matern"
#> 
#> $matern_order
#> [1] 1.5
#> 
#> $w0
#> [1] 1
#> 
#> attr(,"class")
#> [1] "gp_opts" "list"   

# use linear kernel
gp_opts(kernel = "periodic")
#> $basis_prop
#> [1] 0.2
#> 
#> $boundary_scale
#> [1] 1.5
#> 
#> $ls_mean
#> [1] 21
#> 
#> $ls_sd
#> [1] 7
#> 
#> $ls_min
#> [1] 0
#> 
#> $ls_max
#> [1] 60
#> 
#> $alpha_mean
#> [1] 0
#> 
#> $alpha_sd
#> [1] 0.01
#> 
#> $kernel
#> [1] "periodic"
#> 
#> $matern_order
#> [1] 1.5
#> 
#> $w0
#> [1] 1
#> 
#> attr(,"class")
#> [1] "gp_opts" "list"