Resampler: Resampler

In flowml: A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data

Resampler

Description

Model validation by repeated bootstrapping

Format

[R6::R6Class] object.

Details

Uses repeated bootstrapping to validate models without a test data set. For each experiment multiple metrics are measured. For classification experiments the confusion matrix is calculated additionally. In order to test hypotheses, either features or the response variable can be permuted.

Active bindings

permute

returns the instance variable 'permute'. (character)

permute_alphabet

returns the instance variable 'permute_alphabet'. (character)

n_resample

returns the instance variable 'n_resample'. (integer)

fml_method

returns the instance variable 'fml_method'. (character)

fml_type

returns the instance variable 'fml_type'. (character)

fml_type_alphabet

returns the instance variable 'fml_type_alphabet'. (character)

pre_process_lst

returns the instance variable 'pre_process_lst'. (character)

hyper_parameters

returns the instance variable 'hyper_parameters'. (list)

response_var

returns the instance variable 'response_var'. (character)

n_features

returns the instance variable 'n_features'. (integer)

strata_var

returns the instance variable 'strata_var'. (character)

metrics_df

returns the instance variable 'metrics_df'. (tibble::tibble)

confusion_df

returns the instance variable 'confusion_df'. (tibble::tibble)

Methods

Public methods

• Resampler$new()

• Resampler$print()

• Resampler$fit()

• Resampler$clone()

---

Method new()

checks, if permutation is requested. If true, performs the permutation task.

Checks if ml.type is classification. If true, calculates confusion matrix.

Creates and returns instance of Resampler class.

Usage

Resampler$new(
  n_resample = 500,
  fml_method = "pcr",
  fml_type = "classification",
  hyper_parameters = "list",
  pre_process_lst = c("center", "scale"),
  permute = NULL,
  n_features = 0,
  response_var = "character",
  strata_var = NULL
)

Arguments

n_resample

number of bootstrap resamples. The default is 500 (integer)

fml_method

ML model that is being used. The default is 'pcr' (character).

fml_type

ML model type. Needs to be 'classification', 'regression' or 'censored'. Default is 'classification' (character).

hyper_parameters

List of model hyper parameters. (list)

pre_process_lst

Vector of pre-processing steps. Default is 'c("center", "scale")' (character).

permute

Permutation method. Needs to be 'none', 'features' or 'response'. (character)

n_features

Number of features to be chosen in the permutation experiment. Default is 0 (integer).

response_var

Response variable of the model (character).

strata_var

Stratification variable (character).

Returns

Resampler

---

Method print()

Print instance variables of Resampler class.

Usage

Resampler$print()

Returns

character

---

Method fit()

Runs the bootstrap analysis based on the instance variables chosen under initialize.

Usage

Resampler$fit(data_df = "tbl_df")

Arguments

data_df

data set to be analyzed (tibble::tibble).

Returns

None

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Resampler$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Sebastian Malkusch

flowml documentation built on May 29, 2024, 10:43 a.m.