xnet (version 0.1.11)

permtest: Calculate the relative importance of the edges

Description

This function does a permutation-based evaluation of the impact of different edges on the final result. It does so by permuting the kernel matrices, refitting the model and calculating a loss function.

Usage

permtest(x, ...)
# S3 method for permtest
print(x, digits = max(3L, getOption("digits") - 3), ...)
# S4 method for tskrrHeterogeneous
permtest(
  x,
  n = 100,
  permutation = c("both", "row", "column"),
  exclusion = c("interaction", "row", "column", "both"),
  replaceby0 = FALSE,
  fun = loss_mse,
  exact = FALSE
)
# S4 method for tskrrHomogeneous
permtest(
  x,
  n = 100,
  permutation = c("both"),
  exclusion = c("interaction", "both"),
  replaceby0 = FALSE,
  fun = loss_mse,
  exact = FALSE
)
# S4 method for tskrrTune
permtest(x, permutation = c("both", "row", "column"), n = 100)

Arguments

x

either a tskrr-class or a tskrrTune-class object

...

arguments passed to other methods

digits

the number of digits shown in the output

n

the number of permutations for every kernel matrix

permutation

a character string that defines whether the row, column or both kernel matrices should be permuted. Ignored in case of a homogeneous network

exclusion

the exclusion to be used in the loo function. See also get_loo_fun

replaceby0

a logical value indicating whether loo removes a value in the leave-one-out procedure or replaces it by zero. See also get_loo_fun.

fun

a function (or a character string with the name of a function) that calculates the loss. See also tune and loss_functions

exact

a logical value that indicates whether or not an exact p-value should be calculated, or be approximated based on a normal distribution.

Value

An object of the class permtest.

Warning

It should be noted that this normal approximation is an ad-hoc approach. There's no guarantee that the actual distribution of the loss under the null hypothesis is normal. Depending on the loss function, a significant deviation from the theoretic distribution can exist. Hence this functions should only be used as a rough guidance in model evaluation.

Details

The test involved uses a normal approximation. It assumes that under the null hypothesis, the loss values are approximately normally distributed. The cumulative probability of a loss as small or smaller than the one found in the original model, is calculated based on a normal distribution from which the mean and sd are calculated from the permutations.

Examples

# NOT RUN {
# Heterogeneous network

data(drugtarget)

mod <- tskrr(drugTargetInteraction, targetSim, drugSim)
permtest(mod, fun = loss_auc)

# }