Print a summary.spectral object

Print a summary.spectral object

Usage

# S3 method for class 'summary.spectral'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

x

Object of class summary.spectral.

digits

Number of digits to use when printing the output.

...

further arguments on how to format the number of digits.

Value

invisble The inputted summary.spectral object.

See also

print, summary.spectral, spectral

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
#>