ferrn (version 0.3.0)

sample_bases: Function to calculate smoothness and squintability

Description

Function to calculate smoothness and squintability

Usage

sample_bases(
  idx,
  data = sine1000,
  n_basis = 300,
  parallel = FALSE,
  best = matrix(c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1), nrow = 6),
  min_proj_dist = NA,
  step_size = NA,
  seed = 123
)
# S3 method for basis_df
print(x, width = NULL, ...)
# S3 method for basis_df
tbl_sum(x)
calc_smoothness(
  basis_df,
  start_params = c(0.001, 0.5, 2, 2),
  other_gp_params = NULL,
  verbose = FALSE
)
# S3 method for smoothness_res
print(x, width = NULL, ...)
# S3 method for smoothness_res
tbl_sum(x)
calc_squintability(
  basis_df,
  method = c("ks", "nls"),
  scale = TRUE,
  bin_width = 0.005,
  other_params = NULL
)
# S3 method for squintability_res
print(x, width = NULL, ...)
# S3 method for squintability_res
tbl_sum(x)
fit_ks(basis_df, idx, other_params = NULL)
fit_nls(basis_df, other_params = NULL)

Arguments

idx

character, the name of projection pursuit index function, e.g. "holes"

data

a matrix or data frame, the high dimensional data to be projected

n_basis

numeric, the number of random bases to generate

parallel

logic, whether to use parallel computing for calculating the index. Recommend for the stringy index.

best

a matrix, the theoretical/ empirical best projection matrix to calculate the projection distance from the simulated random bases.

min_proj_dist

only for squintability, the threshold for projection distance for the random basis to be considered in sampling

step_size

numeric, step size for interpolating from each random basis to the best basis, recommend 0.005

seed

numeric, seed for sampling random bases

x

objects with specialised printing methods

width

only used when max.levels is NULL, see above.

...

further arguments passed to or from other methods.

basis_df

the basis data frame returned from sample_bases

start_params

list, the starting parameters for the Gaussian process for smoothness

other_gp_params

list, additional parameters to be passed to [GpGp::fit_model()] for calculating smoothness

verbose

logical, whether to print optimisation progression when fitting the Gaussian process

method

either "ks" (kernel smoothing) or "nls" (non-linear least square) for calculating squintability.

scale

logic, whether to scale the index value to 0-1 in squintability

bin_width

numeric, the bin width to average the index value before fitting the kernel, recommend to set as the same as `step` parameter

other_params

list additional parameters for fitting kernel smoothing or non-linear least square, see [stats::ksmooth()] and [stats::nls()] for details

Examples

if (FALSE) {
library(GpGp)
library(fields)
library(tourr)
basis_smoothness <- sample_bases(idx = "holes")
calc_smoothness(basis_smoothness)
basis_squint <- sample_bases(idx = "holes", n_basis = 100, step_size = 0.01, min_proj_dist = 1.5)
calc_squintability(basis_squint, method = "ks", bin_width = 0.01)
}