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 = 5, nlambda = 3, ncenters = 50, maxit = 100)
# Inspect model object
dr
#> 
#> Call:
#> lhss(df_numerator = numerator_small, df_denominator = denominator_small,     nsigma = 5, nlambda = 3, ncenters = 50, maxit = 100)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 50
#>   sigma: num [1:5, 1:3] 0.00952 0.36681 1.30318 3.4202 10.53644 ...
#> 
#> Regularization parameter (lambda): num [1:3] 1e+03 1e+00 1e-03
#> 
#> Subspace dimension (m): 1
#> Optimal sigma: 0.3841266
#> Optimal lambda: 1
#> Optimal kernel weights (loocv): num [1:51] 0.2707 0.0121 0.097 0.0135 0.0836 ...
#>  
# Obtain summary of model object
summary(dr)
#> 
#> Call:
#> lhss(df_numerator = numerator_small, df_denominator = denominator_small,     nsigma = 5, nlambda = 3, ncenters = 50, maxit = 100)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 50
#> 
#> Subspace dimension (m): 1
#> Optimal sigma: 0.3841266
#> Optimal lambda: 1
#> Optimal kernel weights (loocv): num [1:51] 0.2707 0.0121 0.097 0.0135 0.0836 ...
#>  
#> Pearson divergence between P(nu) and P(de): 0.2529
#> For a two-sample homogeneity test, use 'summary(x, test = TRUE)'.
#> 
# Plot model object
plot(dr)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Plot density ratio for each variable individually
plot_univariate(dr)
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 
# Plot density ratio for each pair of variables
plot_bivariate(dr)
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 
# Predict density ratio and inspect first 6 predictions
head(predict(dr))
#> , , 1
#> 
#>           [,1]
#> [1,] 0.8873535
#> [2,] 1.0458496
#> [3,] 0.8122089
#> [4,] 0.4962786
#> [5,] 1.3122394
#> [6,] 1.3092069
#>