Extract summary from spectral object, including two-sample significance test for homogeneity of the numerator and denominator samples

Extract summary from spectral object, including two-sample significance test for homogeneity of the numerator and denominator samples

Usage

# S3 method for class 'spectral'
summary(
  object,
  test = FALSE,
  n_perm = 100,
  parallel = FALSE,
  cluster = NULL,
  ...
)

Arguments

object

Object of class spectral

test

logical indicating whether to statistically test for homogeneity of the numerator and denominator samples.

n_perm

Scalar indicating number of permutation samples

parallel

logical indicating to run the permutation test in parallel

cluster

NULL or a cluster object created by makeCluster. If NULL and parallel = TRUE, it uses the number of available cores minus 1.

...

further arguments passed to or from other methods.

Value

Summary of the fitted density ratio model

Examples

set.seed(123)
# Fit model
dr <- spectral(numerator_small, denominator_small)
# Inspect model object
dr
#> 
#> Call:
#> spectral(df_numerator = numerator_small, df_denominator = denominator_small)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 100
#>   sigma: num [1:10] 0.807 1.191 1.455 1.688 1.913 ...
#> 
#> 
#> Subspace dimension (J): num [1:50] 1 2 4 6 8 10 11 13 15 17 ...
#> 
#> Optimal sigma: 3.582214
#> Optimal subspace: 8
#> Optimal kernel weights (cv): num [1:8] 1.0045 -0.6689 -0.0938 0.8499 0.0228 ...
#>  
# Obtain summary of model object
summary(dr)
#> 
#> Call:
#> spectral(df_numerator = numerator_small, df_denominator = denominator_small)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 100
#> 
#> Optimal sigma: 3.582214
#> Optimal subspace: 8
#> Optimal kernel weights (cv): num [1:8] 1.0045 -0.6689 -0.0938 0.8499 0.0228 ...
#>  
#> Pearson divergence between P(nu) and P(de): 0.8063
#> For a two-sample homogeneity test, use 'summary(x, test = TRUE)'.
#> 
# Plot model object
plot(dr)
#> Warning: Negative estimated density ratios for 22 observation(s) converted to 0.01 before applying logarithmic transformation
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Plot density ratio for each variable individually
plot_univariate(dr)
#> Warning: Negative estimated density ratios for 22 observation(s) converted to 0.01 before applying logarithmic transformation
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 
# Plot density ratio for each pair of variables
plot_bivariate(dr)
#> Warning: Negative estimated density ratios for 22 observation(s) converted to 0.01 before applying logarithmic transformation
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 
# Predict density ratio and inspect first 6 predictions
head(predict(dr))
#> , , 1
#> 
#>           [,1]
#> [1,] 2.8779372
#> [2,] 3.8658889
#> [3,] 3.7674156
#> [4,] 4.8603842
#> [5,] 0.6970124
#> [6,] 2.1671079
#> 
# Fit model with custom parameters
spectral(numerator_small, denominator_small, sigma = 2)
#> 
#> Call:
#> spectral(df_numerator = numerator_small, df_denominator = denominator_small,     sigma = 2)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 100
#>   sigma: num 2
#> 
#> 
#> Subspace dimension (J): num [1:50] 1 2 4 6 8 10 11 13 15 17 ...
#> 
#> Optimal sigma: 2
#> Optimal subspace: 4
#> Optimal kernel weights (cv): num [1:4] 0.98 -0.8324 -0.0561 0.6471
#>