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

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

Usage

# S3 method for class 'kmm'
summary(
  object,
  test = FALSE,
  n_perm = 100,
  parallel = FALSE,
  cluster = NULL,
  min_pred = 1e-06,
  ...
)

Arguments

object

Object of class kmm

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.

min_pred

Scalar indicating the minimum value for the predicted density ratio values (used in the divergence statistic) to avoid negative density ratio values.

...

further arguments passed to or from other methods.

Value

Summary of the fitted density ratio model

Examples

set.seed(123)
# Fit model
dr <- kmm(numerator_small, denominator_small)
# Inspect model object
dr
#> 
#> Call:
#> kmm(df_numerator = numerator_small, df_denominator = denominator_small)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 150
#>   sigma: num [1:10] 0.801 1.2 1.483 1.723 1.954 ...
#> 
#> Optimal sigma (5-fold cv): 3.67
#> Optimal kernel weights (5-fold cv):  num [1:150, 1] 0.23 0.416 -0.166 1.512 0.831 ...
#> 
#> Optimization parameters:
#>   Optimization method:  Unconstrained 
#> 
# Obtain summary of model object
summary(dr)
#> 
#> Call:
#> kmm(df_numerator = numerator_small, df_denominator = denominator_small)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 150
#> Optimal sigma: 3.669758
#> Optimal kernel weights: num [1:150, 1] 0.23 0.416 -0.166 1.512 0.831 ...
#>  
#> Pearson divergence between P(nu) and P(de): 0.9439
#> For a two-sample homogeneity test, use 'summary(x, test = TRUE)'.
#> 
# Plot model object
plot(dr)
#> Warning: Negative estimated density ratios for 19 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 19 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 19 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,] 3.1261579
#> [2,] 4.0233887
#> [3,] 3.6868339
#> [4,] 5.5934888
#> [5,] 0.6302996
#> [6,] 1.5225886
# Fit model with custom parameters
kmm(numerator_small, denominator_small,
    nsigma = 5, ncenters = 100, nfold = 10,
    constrained = TRUE)
#> 
#> Call:
#> kmm(df_numerator = numerator_small, df_denominator = denominator_small,     constrained = TRUE, nsigma = 5, ncenters = 100, nfold = 10)
#> 
#> Kernel Information:
#>   Kernel type: Gaussian with L2 norm distances
#>   Number of kernels: 100
#>   sigma: num [1:5] 0.811 1.577 2.094 2.66 3.706
#> 
#> Optimal sigma (10-fold cv): 2.094
#> Optimal kernel weights (10-fold cv):  num [1:100, 1] -0.000498 -0.000999 -0.001187 -0.001022 -0.000275 ...
#> 
#> Optimization parameters:
#>   Optimization method:  Constrained 
#>