Root class for common functionality of this package

Description

Root class for common functionality of this package

Root class for common functionality of this package

Public fields

Methods

Public methods

• CBRBase$new()

• CBRBase$fit()

• CBRBase$calc_distance_matrix()

• CBRBase$get_similar_cases()

• CBRBase$clone()

Method new()

Initialize object for searching similar cases

Usage

CBRBase$new(formula, data)

Arguments

Method fit()

Fit the Model

Usage

CBRBase$fit()

Arguments

Method calc_distance_matrix()

Calculates the distance matrix

Usage

CBRBase$calc_distance_matrix(query = NULL)

Arguments

Method get_similar_cases()

Extracts similar cases

Usage

CBRBase$get_similar_cases(query, k = 1, addDistance = T, merge = F)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

CBRBase$clone(deep = FALSE)

Arguments

Case Based Reasoning

Description

A R package for Case Based Reasoning using statistical/ML models.

Cox-Beta Model for Case-Based-Reasoning

Description

Cox-Beta Model for Case-Based-Reasoning

Cox-Beta Model for Case-Based-Reasoning

Details

Regression beta coefficients obtained from a CPH regression model fitted on the training data are used for building a weighted distance measure between train and test data. Afterwards, we will use these weights for calculating a (n x m)-distance matrix, where n is the number of observations in the training data, and m is the number of observations of the test data. The user can use this distance matrix for further cluster analysis or for extracting for each test observation k (= 1,...,l) similar cases from the train data. We use the rms-package for model fitting, variable selection, and checking model assumptions. If the user omits the test data, this functions returns a n x n-distance matrix.

Super classes

CaseBasedReasoning::CBRBase -> CaseBasedReasoning::RegressionModel -> CoxModel

Public fields

Methods

Public methods

• CoxModel$check_ph()

• CoxModel$clone()

Method check_ph()

Check proportional hazard assumption graphically

Usage

CoxModel$check_ph()

Method clone()

The objects of this class are cloneable with this method.

Usage

CoxModel$clone(deep = FALSE)

Arguments

Linear Regression Model for Case-Based-Reasoning

Description

Linear Regression Model for Case-Based-Reasoning

Linear Regression Model for Case-Based-Reasoning

Super classes

CaseBasedReasoning::CBRBase -> CaseBasedReasoning::RegressionModel -> LinearModel

Public fields

Methods

Public methods

• LinearModel$clone()

Method clone()

The objects of this class are cloneable with this method.

Usage

LinearModel$clone(deep = FALSE)

Arguments

Logistic Regression Model for Case-Based-Reasoning

Description

Logistic Regression Model for Case-Based-Reasoning

Logistic Regression Model for Case-Based-Reasoning

Super classes

CaseBasedReasoning::CBRBase -> CaseBasedReasoning::RegressionModel -> LogisticModel

Public fields

Methods

Public methods

• LogisticModel$clone()

Method clone()

The objects of this class are cloneable with this method.

Usage

LogisticModel$clone(deep = FALSE)

Arguments

RandomForest Model for Searching Similar Cases

Description

RandomForest Model for Searching Similar Cases

RandomForest Model for Searching Similar Cases

Details

This class uses the proximity or depth matrix of the RandomForest algorithm as a similarity matrix of training and query observations. By default all cases with at least one missing values are dropped from learning, calculating the distance matrix and searching for similar cases.

Super class

CaseBasedReasoning::CBRBase -> RFModel

Public fields

Methods

Public methods

• RFModel$print()

• RFModel$new()

• RFModel$fit()

• RFModel$set_distance_method()

• RFModel$clone()

Method print()

Prints information of the initialized object

Usage

RFModel$print()

Method new()

Initialize a RandomForest object for searching similar cases.

Usage

RFModel$new(formula, data, ...)

Arguments

Method fit()

Fit the RandomForest

Usage

RFModel$fit()

Arguments

Method set_distance_method()

Set the distance method. Available are Proximity and Depth

Usage

RFModel$set_distance_method(method = "Depth")

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

RFModel$clone(deep = FALSE)

Arguments

References

Englund and Verikas. A novel approach to estimate proximity in a random forest: An exploratory study.

Root class for Regression Models, e.g., CPH, logistic, and linear regression

Description

Root class for Regression Models, e.g., CPH, logistic, and linear regression

Root class for Regression Models, e.g., CPH, logistic, and linear regression

Super class

CaseBasedReasoning::CBRBase -> RegressionModel

Public fields

Methods

Public methods

• RegressionModel$print()

• RegressionModel$variable_selection()

• RegressionModel$fit()

• RegressionModel$clone()

Method print()

Prints information of the initialized object

Usage

RegressionModel$print()

Method variable_selection()

Fast backward variable selection with penalization

Usage

RegressionModel$variable_selection(x)

Arguments

Method fit()

Fit the RandomForest

Usage

RegressionModel$fit()

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

RegressionModel$clone(deep = FALSE)

Arguments

Converts a distance vector into an object of class dist

Description

Converts a distance vector into an object of class dist

Usage

asDistObject(x, n, method)

Arguments

Call a function by character strings using the namespace and custom parameters.

Description

Call a function by character strings using the namespace and custom parameters.

Usage

call_function(func_list)

Arguments

Depth Distance

Description

This function returns for each observation the pairwise sum of edges between the corresponding terminal nodes over each tree in the random forest.

Usage

depth_distance(x, y = NULL, rfObject)

Arguments

Examples

require(ranger)
rf <- ranger(Species ~ ., data = iris, num.trees = 5, write.forest = TRUE)
depth_distance(x=iris[, -5], rfObject=rf)

Distance calculation based on RandomForest Proximity or Depth

Description

Distance calculation based on RandomForest Proximity or Depth

Usage

distanceRandomForest(
  x,
  y = NULL,
  rfObject,
  method = "Proximity",
  threads = NULL
)

Arguments

Value

a dist or a matrix object with pairwise distance of observations in x vs y (if not null)

Examples

library(ranger)
# proximity pairwise distances
rf.fit <- ranger(Species ~ ., data = iris, num.trees = 500, write.forest = TRUE)
distanceRandomForest(x = iris[, -5], rfObject = rf.fit, method = "Proximity", threads = 1)

# depth distance for train versus test subset
set.seed(1234L)
learn <- sample(1:150, 100)
test <- (1:150)[-learn]
rf.fit <- ranger(Species ~ ., data = iris[learn, ], num.trees = 500, write.forest = TRUE)
distanceRandomForest(x = iris[learn, -5], y = iris[test, -5], rfObject = rf.fit, method = "Depth")

Number of Edges between Terminal Nodes

Description

first two columns are terminal node IDs; If an ID pair do not appear in a tree -1 is inserted

Usage

edges_between_terminal_nodes(rfObject)

Arguments

Value

a matrix object with pairwise terminal node edge length

Examples

require(ranger)
rf.fit <- ranger(Species ~ ., data = iris, num.trees = 5, write.forest = TRUE)
edges_between_terminal_nodes(rf.fit)

Generate Grid

Description

Generates a uniform grid over the distribution of the time2event variable, calculates closest point and returns this point for each input time2event element. Memory consumption will increase when performing the randomForest model with many unique time2event values. Therefore, we offer a reduction of the time2event values by choosing closest elements in a grid.

Usage

generate_grid(t2e, grid_length = 250)

Arguments

Value

a list with new_t2e and grid_error

Get proximity matrix of an ranger object

Description

Get proximity matrix of an ranger object

Usage

proximity_distance(x, y = NULL, rfObject, as_dist = TRUE)

Arguments

Value

a dist or a matrix object with pairwise proximity of observations in x vs y (if not null)

Examples

require(ranger)
rf <- ranger(Species ~ ., data = iris, num.trees = 5, write.forest = TRUE)
proximity_distance(x = iris[, -5], rfObject = rf)

set.seed(1234L)
learn <- sample(1:150, 100)
test <- (1:150)[-learn]
rf <- ranger(Species ~ ., data = iris[learn, ], num.trees = 500, write.forest = TRUE)
proximity_distance(x = iris[learn, -5], y = iris[test, -5], rfObject = rf)

Forest2Matrix

Description

Transform trees of a ranger-object to a matrix

Usage

ranger_forests_to_matrix(rfObject)

Arguments

Value

a matrix object with Column 1: tree ID Column 2: node ID Column 3: child node ID 1 Column 4: child node ID 2

Examples

library(ranger)
rf.fit <- ranger(Species ~ ., data = iris, num.trees = 5, write.forest = TRUE)
forest_matrix <- ranger_forests_to_matrix(rf.fit)

Get the terminal node id of a RandomForest Object

Description

Extracts for each observation and for each tree in the forest the terminal node id. The index of terminal nodes are starting with 1, e.g., the root node has id 1

Usage

terminalNodes(x, rfObject)

Arguments

Value

Matrix with terminal node IDs for all observations in x (rows) and trees (columns)

Examples

library(ranger)
rf.fit <- ranger(Species ~ ., data = iris, num.trees = 5, write.forest = TRUE)
dfNodes <- terminalNodes(iris[, -5], rf.fit)

Weighted Distance calculation

Description

Weighted Distance calculation

Usage

weightedDistance(x, y = NULL, weights = NULL)

Arguments

Value

a dist or matrix object

Examples

require(ranger)
rf <- ranger(Species ~ ., data = iris, num.trees = 5, write.forest = TRUE)
terminalNodes(iris[, -5], rf)