Print a lhss object

Print a lhss object

Usage

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

Arguments

x

Object of class lhss.

digits

Number of digits to use when printing the output.

...

further arguments on how to format the number of digits.

Value

invisble The inputted lhss object.

See also

print, lhss

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
#>