Obtain predicted density ratio values from a naivedensityratio object

Obtain predicted density ratio values from a naivedensityratio object

Usage

# S3 method for class 'naivedensityratio'
predict(object, newdata = NULL, log = FALSE, tol = 1e-06, ...)

Arguments

object

A naive object

newdata

Optional matrix new data set to compute the density

log

A logical indicating whether to return the log of the density ratio

tol

Minimal density value to avoid numerical issues

...

Additional arguments to be passed to the function

Value

An array with predicted density ratio values from possibly new data, but otherwise the numerator samples.

See also

predict, naive

Examples

set.seed(123)
# Fit model
dr <- naive(numerator_small, denominator_small)
# Inspect model object
dr
#> 
#> Call:
#> naive(df_numerator = numerator_small, df_denominator = denominator_small)
#> 
#> Naive density ratio
#>   Number of variables: 3
#>   Number of numerator samples: 50
#>   Number of denominator samples: 100
#>   Numerator density: num [1:50] 1.41 5.74 1.87 4.13 1.67 ...
#>   Denominator density: num [1:100] 2.93 0.071 1.065 1.59 2.115 ...
#>  
# Obtain summary of model object
summary(dr)
#> 
#> Call:
#> naive(df_numerator = numerator_small, df_denominator = denominator_small)
#> 
#> Naive density ratio estimate:
#>   Number of variables: 
#>   Number of numerator samples: 50
#>   Number of denominator samples: 100
#>   Density ratio for numerator samples: num [1:50] 0.344 1.747 0.628 1.419 0.511 ...
#>   Density ratio for denominator samples: num [1:100] 1.0751 -2.6454 0.0626 0.464 0.7493 ...
#>  
#> 
#> Squared average log density ratio difference for numerator and denominator samples (SALDRD): 13.56
#> For a two-sample homogeneity test, use 'summary(x, test = TRUE)'.
#> 
# Plot model object
plot(dr)
#> Warning: Negative estimated density ratios for 25 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 25 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 25 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] 1.410607 5.739287 1.874031 4.131255 1.666760 4.095855
# Fit model with custom parameters
naive(numerator_small, denominator_small, m=2, kernel="epanechnikov")
#> 
#> Call:
#> naive(df_numerator = numerator_small, df_denominator = denominator_small,     m = 2, kernel = "epanechnikov")
#> 
#> Naive density ratio
#>   Number of variables: 3
#>   Number of numerator samples: 50
#>   Number of denominator samples: 100
#>   Numerator density: num [1:50] 0.572 1.421 0.945 1.058 0.936 ...
#>   Denominator density: num [1:100] 1.391 1.459 0.572 0.943 1.314 ...
#>