Cross validation for joint density classification

Performs k-fold cross validation for a jdify object.

cv_jdify(formula, data, jd_method = "cctools", folds = 10, cores = 1, ...)

Arguments

formula	an object of class "formula"; same as stats::lm().
data	matrix, data frame, list or environment (or object coercible by base::as.data.frame()) containing the variables in the model.
jd_method	an object of class "jd_method" defining the method for joint density estimation, see jd_method().
folds	number of folds.
cores	number of cores for parallelized cross validation (based on foreach::foreach()).
...	further arguments passed to fit_fun().

Value

A list with elements

• folds1``, ...,foldsk: for each fold: the fitted model$fit, estimated conditional probabilities ($probs), and indexes for training and test data ($train_index,$test_index`).

• cv_probs: aggragated out-of-sample probs in same order as original data.

References

Nagler, T. (2017). A generic approach to nonparametric function estimation with mixed data. arXiv:1704.07457

Examples

# simulate training and test data
dat <- data.frame(
    cl = as.factor(rbinom(100, 1, 0.5)),
    x1 = rnorm(100),
    x2 =  ordered(rbinom(10, 1, 0.3), 0:1)
)

cv <- cv_jdify(cl ~ x1 + x2, dat)
probs <- cv$cv_probs
assess_clsfyr(probs[, 1], dat[, 1] == 0, measure = c("ACC", "F1"))
#>    threshold measure      value
#> 1        0.0     ACC 0.60000000
#> 2        0.1     ACC 0.59000000
#> 3        0.2     ACC 0.59000000
#> 4        0.3     ACC 0.56000000
#> 5        0.4     ACC 0.55000000
#> 6        0.5     ACC 0.54000000
#> 7        0.6     ACC 0.51000000
#> 8        0.7     ACC 0.42000000
#> 9        0.8     ACC 0.41000000
#> 10       0.9     ACC 0.40000000
#> 11       1.0     ACC 0.38000000
#> 12       0.0      F1 0.75000000
#> 13       0.1      F1 0.74213836
#> 14       0.2      F1 0.74213836
#> 15       0.3      F1 0.71794872
#> 16       0.4      F1 0.70967742
#> 17       0.5      F1 0.69333333
#> 18       0.6      F1 0.62595420
#> 19       0.7      F1 0.35555556
#> 20       0.8      F1 0.09230769
#> 21       0.9      F1 0.06250000
#> 22       1.0      F1 0.00000000