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

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

Usage

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

Arguments

object: Object of class lhss
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 (minimal example to limit computation time)
dr <- lhss(numerator_small, denominator_small,
           nsigma = 3, lambda = c(0.1, 1), ncenters = 50, maxit = 100)
# Inspect model object
dr
#> 
#> Call:
#> lhss(df_numerator = numerator_small, df_denominator = denominator_small,     nsigma = 3, lambda = c(0.1, 1), ncenters = 50, maxit = 100)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 50
#>   sigma: num [1:3, 1:2] 0.0793 0.9371 2.677 0.0693 0.9633 ...
#> 
#> Regularization parameter (lambda): num [1:2] 0.1 1
#> 
#> Subspace dimension (m): 1
#> Optimal sigma: 2.677003
#> Optimal lambda: 0.1
#> Optimal kernel weights (loocv): num [1:51] 6.666 -0.3081 -0.0385 -0.0353 0.0108 ...
#>  
# Obtain summary of model object
summary(dr)
#> 
#> Call:
#> lhss(df_numerator = numerator_small, df_denominator = denominator_small,     nsigma = 3, lambda = c(0.1, 1), ncenters = 50, maxit = 100)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 50
#> 
#> Subspace dimension (m): 1
#> Optimal sigma: 2.677003
#> Optimal lambda: 0.1
#> Optimal kernel weights (loocv): num [1:51] 6.666 -0.3081 -0.0385 -0.0353 0.0108 ...
#>  
#> Pearson divergence between P(nu) and P(de): 0.6358
#> For a two-sample homogeneity test, use 'summary(x, test = TRUE)'.
#> 
# Plot model object
plot(dr)
#> Warning: Negative estimated density ratios for 12 observation(s) converted to 0.01 before applying logarithmic transformation
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

# Plot density ratio for each variable individually
plot_univariate(dr)
#> Warning: Negative estimated density ratios for 12 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 12 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,] 2.1625009
#> [2,] 3.6694722
#> [3,] 2.7969688
#> [4,] 4.1387638
#> [5,] 0.1585054
#> [6,] 1.2616673
#>