densityratio (version 0.2.2)

kliep: Kullback-Leibler importance estimation procedure

Description

Kullback-Leibler importance estimation procedure

Usage

kliep(
  df_numerator,
  df_denominator,
  scale = "numerator",
  nsigma = 10,
  sigma_quantile = NULL,
  sigma = NULL,
  ncenters = 200,
  centers = NULL,
  cv = TRUE,
  nfold = 5,
  epsilon = NULL,
  maxit = 5000,
  progressbar = TRUE
)

Value

kliep-object, containing all information to calculate the density ratio using optimal sigma and optimal weights.

Arguments

df_numerator

data.frame with exclusively numeric variables with the numerator samples

df_denominator

data.frame with exclusively numeric variables with the denominator samples (must have the same variables as df_denominator)

scale

"numerator", "denominator", or NULL, indicating whether to standardize each numeric variable according to the numerator means and standard deviations, the denominator means and standard deviations, or apply no standardization at all.

nsigma

Integer indicating the number of sigma values (bandwidth parameter of the Gaussian kernel gram matrix) to use in cross-validation.

sigma_quantile

NULL or numeric vector with probabilities to calculate the quantiles of the distance matrix to obtain sigma values. If NULL, nsigma values between 0.25 and 0.75 are used.

sigma

NULL or a scalar value to determine the bandwidth of the Gaussian kernel gram matrix. If NULL, nsigma values between 0.25 and 0.75 are used.

ncenters

Maximum number of Gaussian centers in the kernel gram matrix. Defaults to all numerator samples.

centers

Option to specify the Gaussian samples manually.

cv

Logical indicating whether or not to do cross-validation

nfold

Number of cross-validation folds used in order to calculate the optimal sigma value (default is 5-fold cv).

epsilon

Numeric scalar or vector with the learning rate for the gradient-ascent procedure. If a vector, all values are used as the learning rate. By default, 10^{1:-5} is used.

maxit

Maximum number of iterations for the optimization scheme.

progressbar

Logical indicating whether or not to display a progressbar.

References

Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., Von Bünau, P., & Kawanabe, M. (2008). Direct importance estimation for covariate shift adaptation. Annals of the Institute of Statistical Mathematics 60, 699-746. Doi: https://doi.org/10.1007/s10463-008-0197-x.

Examples

set.seed(123)
# Fit model
dr <- kliep(numerator_small, denominator_small)
# Inspect model object
dr
# Obtain summary of model object
summary(dr)
# Plot model object
plot(dr)
# Plot density ratio for each variable individually
plot_univariate(dr)
# Plot density ratio for each pair of variables
plot_bivariate(dr)
# Predict density ratio and inspect first 6 predictions
head(predict(dr))
# Fit model with custom parameters
kliep(numerator_small, denominator_small,
      nsigma = 1, ncenters = 100, nfold = 10,
      epsilon = 10^{2:-5}, maxit = 500)