Package 'ranktreeEnsemble' reference manual

Title:	Ensemble Models of Rank-Based Trees with Extracted Decision Rules
Description:	Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
Authors:	Ruijie Yin [aut], Chen Ye [aut], Min Lu [aut, cre]
Maintainer:	Min Lu <[email protected]>
License:	GPL (>= 2)
Version:	0.21
Built:	2025-03-12 06:02:41 UTC
Source:	https://github.com/transbioinfolab/ranktreeensemble

Extract Interpretable Decision Rules from a Random Forest Model

Description

Extract rules from a random forest (rfsrc) object

Usage

extract.rules(object, subtrees = 5,
              treedepth = 2,
              digit = 2,
              pairs = TRUE)
extract.rules(object, subtrees = 5,
              treedepth = 2,
              digit = 2,
              pairs = TRUE)

Arguments

`object`	A random forest `(rfsrc)` object
`subtrees`	Number of trees to extract rules
`treedepth`	Tree depth. The larger the number, the longer the extracted rules are.
`digit`	Digit to be displayed in the extracted rules.
`pairs`	Are varibles in `(object)` generated from the `pair` function? Set `pairs = FALSE` to extract rules from regular random forest `(rfsrc)` object with continuous predictors.

Value

`rule`	Interpretable extracted rules. Note that the performance score displayed is inaccurate based on few samples.
`rule.raw`	Rules directly extracted from trees for prediction purpose
`data`	Data used to grow trees from the argument `(object)`.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


data(tnbc)
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
objr <- extract.rules(obj)
objr$rule[,1:3]

#### extract rules from a regular random forest
library(randomForestSRC)
obj2 <- rfsrc(subtype~., data = tnbc[1:100,c(1:5,337)])
objr2 <- extract.rules(obj2, pairs = FALSE)
objr2$rule[,1:3]

data(tnbc)
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
objr <- extract.rules(obj)
objr$rule[,1:3]

#### extract rules from a regular random forest
library(randomForestSRC)
obj2 <- rfsrc(subtype~., data = tnbc[1:100,c(1:5,337)])
objr2 <- extract.rules(obj2, pairs = FALSE)
objr2$rule[,1:3]

Variable Importance Index for Each Predictor

Description

The function computes variable importance for each predictor from a rank-based random forests model or boosting model. A higher value indicates a more important predictor. The random forest implementation was performed via the function vimp directly imported from the randomForestSRC package. Use the command package?randomForestSRC for more information. The boosting implementation was performed via the function relative.influence directly imported from the gbm package. For technical details, see the vignette: utils::browseVignettes("gbm").

Usage

importance(object, ...)
importance(object, ...)

Arguments

`object`	An object of class `rfsrc` generated from the function `rforest` or `gbm` generated from the function `rboost`.
`...`	Further arguments passed to or from other methods.

Value

For the boosting model, a vector of variable importance values is given. For the random forest model, a matrix of variable importance values is given for the variable importance index for all the class labels, followed by the index for each class label.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


data(tnbc)
######################################################
# Random Forest
######################################################
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
importance(obj)
######################################################
# Boosting
######################################################
obj <- rboost(subtype~., data = tnbc[,c(1:10,337)])
importance(obj)

data(tnbc)
######################################################
# Random Forest
######################################################
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
importance(obj)
######################################################
# Boosting
######################################################
obj <- rboost(subtype~., data = tnbc[,c(1:10,337)])
importance(obj)

Transform Continuous Variables into Ranked Binary Pairs

Description

The function transforms a dataset with $p$ continuous predictors into $\frac{p*(p-1)}{2}$ binary predictors of ranked pairs

Usage

pair(data, yvar.name = NULL)
pair(data, yvar.name = NULL)

Arguments

`data`	A dataset with $p$ continuous variables or with $p+1$ variables including a dependent variable.
`yvar.name`	The column name of the independent variable in `data`. By default, there is no dependent variable.

Value

A data frame with the transformed data. The dependent variable is moved to the last column of the data.

Note

The function is efficiently coded in C++.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


data(tnbc)
datp <- pair(tnbc[101:105,c(1:5,337)],"subtype")
datp
datp <- pair(tnbc[105:110,1:5])
datp

data(tnbc)
datp <- pair(tnbc[101:105,c(1:5,337)],"subtype")
datp
datp <- pair(tnbc[105:110,1:5])
datp

Prediction or Extract Predicted Values for Random Forest, Random Forest Rule or Boosting Models

Description

Obtain predicted values using a random forest (rfsrc), random forest extracted rule (rules) or boosting (gbm) object. If no new data is provided, it extracts the out-of-bag predicted values of the outcome for the training data.

Usage

predict(object,
        newdata = NULL,
        newdata.pair = FALSE, ...)
predict(object,
        newdata = NULL,
        newdata.pair = FALSE, ...)

Arguments

`object`	An object of class `rfsrc` generated from the function `rforest` or `gbm` generated from the function `rboost`.
`newdata`	Test data. If missing, the original training data is used for extracting the out-of-bag predicted values without running the model again.
`newdata.pair`	Is `newdata` already converted into binary ranked pairs from the `pair` function?
`...`	Further arguments passed to or from other methods.

Details

For the boosting (gbm) object, the cross-validation predicted values are provided if cv.folds>=2.

Value

`value`	Predicted value of the outcome. For the random forest `(rfsrc)` object, it is the predicted probability. For the boosting `(gbm)` object, it is the fitted values on the scale of regression function (e.g. log-odds scale). For the random forest extracted rule `(rules)` object, it is empty.
`label`	Predicted label of the outcome.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


data(tnbc)
######################################################
# Random Forest
######################################################
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
predict(obj)$label
predict(obj, tnbc[101:110,1:5])$label

datp <- pair(tnbc[101:110,1:5])
predict(obj, datp, newdata.pair = TRUE)$label
######################################################
# Random Forest Extracted Rule
######################################################
 objr <- extract.rules(obj)
 predict(objr)$label[1:5]
 predict(obj, tnbc[101:110,1:5])$label
######################################################
# Boosting
######################################################
obj <- rboost(subtype~., data = tnbc[1:100,c(1:5,337)])
predict(obj)$label
predict(obj, tnbc[101:110,1:5])$label


data(tnbc)
######################################################
# Random Forest
######################################################
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
predict(obj)$label
predict(obj, tnbc[101:110,1:5])$label

datp <- pair(tnbc[101:110,1:5])
predict(obj, datp, newdata.pair = TRUE)$label
######################################################
# Random Forest Extracted Rule
######################################################
 objr <- extract.rules(obj)
 predict(objr)$label[1:5]
 predict(obj, tnbc[101:110,1:5])$label
######################################################
# Boosting
######################################################
obj <- rboost(subtype~., data = tnbc[1:100,c(1:5,337)])
predict(obj)$label
predict(obj, tnbc[101:110,1:5])$label

Ensemble Models of Rank-Based Trees for Single Sample Classification with Interpretable Rules

Description

The package ranktreeEnsemble implements an ensemble of rank-based trees in boosting with the LogitBoost cost and random forests on both binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Interpretable rules can be extracted from trees.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


library(ranktreeEnsemble)
data(tnbc)
########### performance of Random Rank Forest
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
obj
# variable importance
importance(obj)
########### predict new data from Random Rank Forest
predict(obj, tnbc[101:110,1:10])$label
########### extract decision rules from rank-based trees
objr <- extract.rules(obj)
objr$rule[1:5,]
predict(objr, tnbc[101:110,1:10])$label
########### filter decision rules with higher performance
objrs <- select.rules(objr,tnbc[110:130,c(1:10,337)])
predict(objrs, tnbc[101:110,1:10])$label

library(ranktreeEnsemble)
data(tnbc)
########### performance of Random Rank Forest
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
obj
# variable importance
importance(obj)
########### predict new data from Random Rank Forest
predict(obj, tnbc[101:110,1:10])$label
########### extract decision rules from rank-based trees
objr <- extract.rules(obj)
objr$rule[1:5,]
predict(objr, tnbc[101:110,1:10])$label
########### filter decision rules with higher performance
objrs <- select.rules(objr,tnbc[110:130,c(1:10,337)])
predict(objrs, tnbc[101:110,1:10])$label

Generalized Boosted Modeling via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles

Description

The function fits generalized boosted models via Rank-Based Trees on both binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. The boosting implementation was directly imported from the gbm package. For technical details, see the vignette: utils::browseVignettes("gbm").

Usage

rboost(
  formula,
  data,
  dimreduce = TRUE,
  datrank = TRUE,
  distribution = "multinomial",
  weights,
  ntree = 100,
  nodedepth = 3,
  nodesize = 5,
  shrinkage = 0.05,
  bag.fraction = 0.5,
  train.fraction = 1,
  cv.folds = 5,
  keep.data = TRUE,
  verbose = TRUE,
  class.stratify.cv = TRUE,
  n.cores = NULL
)
rboost(
  formula,
  data,
  dimreduce = TRUE,
  datrank = TRUE,
  distribution = "multinomial",
  weights,
  ntree = 100,
  nodedepth = 3,
  nodesize = 5,
  shrinkage = 0.05,
  bag.fraction = 0.5,
  train.fraction = 1,
  cv.folds = 5,
  keep.data = TRUE,
  verbose = TRUE,
  class.stratify.cv = TRUE,
  n.cores = NULL
)

Arguments

`formula`	Object of class 'formula' describing the model to fit.
`data`	Data frame containing the y-outcome and x-variables.
`dimreduce`	Dimension reduction via variable importance weighted forests. `FALSE` means no dimension reduction; `TRUE` means reducing 75% variables before binary rank conversion and then fitting a weighted forest; a numeric value x% between 0 and 1 means reducing x% variables before binary rank conversion and then fitting a weighted forest.
`datrank`	If using ranked raw data for fitting the dimension reduction model.
`distribution`	Either a character string specifying the name of the distribution to use: if the response has only 2 unique values, `bernoulli` is assumed; otherwise, if the response is a factor, `multinomial` is assumed.
`weights`	an optional vector of weights to be used in the fitting process. It must be positive but does not need to be normalized.
`ntree`	Integer specifying the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion, which matches `n.tree` in the `gbm` package.
`nodedepth`	Integer specifying the maximum depth of each tree. A value of 1 implies an additive model. This matches `interaction.depth` in the `gbm` package.
`nodesize`	Integer specifying the minimum number of observations in the terminal nodes of the trees, which matches `n.minobsinnode` in the `gbm` package.. Note that this is the actual number of observations, not the total weight.
`shrinkage`	a shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction; 0.001 to 0.1 usually work, but a smaller learning rate typically requires more trees. Default is 0.05.
`bag.fraction`	the fraction of the training set observations randomly selected to propose the next tree in the expansion. This introduces randomnesses into the model fit. If `bag.fraction` < 1 then running the same model twice will result in similar but different fits. `gbm` uses the R random number generator so `set.seed` can ensure that the model can be reconstructed. Preferably, the user can save the returned `gbm.object` using `save`. Default is 0.5.
`train.fraction`	The first `train.fraction * nrows(data)` observations are used to fit the `gbm` and the remaining observations are used for computing out-of-sample estimates of the loss function.
`cv.folds`	Number of cross-validation folds to perform. If `cv.folds`>1 then `gbm`, in addition to the usual fit, will perform cross-validation and calculate an estimate of generalization error returned in `cv.error`.
`keep.data`	a logical variable indicating whether to keep the data and an index of the data stored with the object. Keeping the data and index makes subsequent calls to `gbm.more` faster at the cost of storing an extra copy of the dataset.
`verbose`	Logical indicating whether or not to print out progress and performance indicators (`TRUE`). If this option is left unspecified for `gbm.more`, then it uses `verbose` from `object`. Default is `TRUE`.
`class.stratify.cv`	Logical indicating whether or not the cross-validation should be stratified by class. The purpose of stratifying the cross-validation is to help avoid situations in which training sets do not contain all classes.
`n.cores`	The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores. If `n.cores` is not specified by the user, it is guessed using the `detectCores` function in the `parallel` package. Note that the documentation for `detectCores` makes clear that it is not failsafe and could return a spurious number of available cores.

Value

`fit`	A vector containing the fitted values on the scale of regression function (e.g. log-odds scale for bernoulli).
`train.error`	A vector of length equal to the number of fitted trees containing the value of the loss function for each boosting iteration evaluated on the training data.
`valid.error`	A vector of length equal to the number of fitted trees containing the value of the loss function for each boosting iteration evaluated on the validation data.
`cv.error`	If `cv.folds` < 2 this component is `NULL`. Otherwise, this component is a vector of length equal to the number of fitted trees containing a cross-validated estimate of the loss function for each boosting iteration.
`oobag.improve`	A vector of length equal to the number of fitted trees containing an out-of-bag estimate of the marginal reduction in the expected value of the loss function. The out-of-bag estimate uses only the training data and is useful for estimating the optimal number of boosting iterations. See `gbm.perf`.
`cv.fitted`	If cross-validation was performed, the cross-validation predicted values on the scale of the linear predictor. That is, the fitted values from the i-th CV-fold, for the model having been trained on the data in all other folds.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples

data(tnbc)
obj <- rboost(subtype~., data = tnbc[,c(1:10,337)])
obj
data(tnbc)
obj <- rboost(subtype~., data = tnbc[,c(1:10,337)])
obj

Random Forest via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles

Description

The function implements the ensembled rank-based trees in random forests on both binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. The random forest implementation was directly imported from the randomForestSRC package. Use the command package?randomForestSRC for more information.

Usage

rforest(formula, data,
  dimreduce = TRUE,
  datrank = TRUE,
  ntree = 500, mtry = NULL,
  nodesize = NULL, nodedepth = NULL,
  splitrule = NULL, nsplit = NULL,
  importance = c(FALSE, TRUE, "none", "anti", "permute", "random"),
  bootstrap = c("by.root", "none"),
  membership = FALSE,
  na.action = c("na.omit", "na.impute"), nimpute = 1,
  perf.type = NULL,
  xvar.wt = NULL, yvar.wt = NULL, split.wt = NULL, case.wt  = NULL,
  forest = TRUE,
  var.used = c(FALSE, "all.trees", "by.tree"),
  split.depth = c(FALSE, "all.trees", "by.tree"),
  seed = NULL,
  statistics = FALSE,
  ...)

## convenient interface for growing a rank-based tree
rforest.tree(formula, data, dimreduce = FALSE,
             ntree = 1, mtry = ncol(data),
             bootstrap = "none", ...)
rforest(formula, data,
  dimreduce = TRUE,
  datrank = TRUE,
  ntree = 500, mtry = NULL,
  nodesize = NULL, nodedepth = NULL,
  splitrule = NULL, nsplit = NULL,
  importance = c(FALSE, TRUE, "none", "anti", "permute", "random"),
  bootstrap = c("by.root", "none"),
  membership = FALSE,
  na.action = c("na.omit", "na.impute"), nimpute = 1,
  perf.type = NULL,
  xvar.wt = NULL, yvar.wt = NULL, split.wt = NULL, case.wt  = NULL,
  forest = TRUE,
  var.used = c(FALSE, "all.trees", "by.tree"),
  split.depth = c(FALSE, "all.trees", "by.tree"),
  seed = NULL,
  statistics = FALSE,
  ...)

## convenient interface for growing a rank-based tree
rforest.tree(formula, data, dimreduce = FALSE,
             ntree = 1, mtry = ncol(data),
             bootstrap = "none", ...)

Arguments

`formula`	Object of class 'formula' describing the model to fit. Interaction terms are not supported.
`data`	Data frame containing the y-outcome and x-variables.
`dimreduce`	Dimension reduction via variable importance weighted forests. `FALSE` means no dimension reduction; `TRUE` means reducing 75% variables before binary rank conversion and then fitting a weighted forest; a numeric value x% between 0 and 1 means reducing x% variables before binary rank conversion and then fitting a weighted forest.
`datrank`	If using ranked raw data for fitting the dimension reduction model.
`ntree`	Number of trees.
`mtry`	Number of variables to possibly split at each node. Default is number of variables divided by 3 for regression. For all other families (including unsupervised settings), the square root of number of variables. Values are rounded up.
`nodesize`	Minumum size of terminal node. The defaults are: survival (15), competing risk (15), regression (5), classification (1), mixed outcomes (3), unsupervised (3). It is recommended to experiment with different `nodesize` values.
`nodedepth`	Maximum depth to which a tree should be grown. Parameter is ignored by default.
`splitrule`	Splitting rule (see below).
`nsplit`	Non-negative integer specifying number of random splits for splitting a variable. When zero, all split values are used (deterministic splitting), which can be slower. By default 10 is used.
`importance`	Method for computing variable importance (VIMP); see below. Default action is `importance="none"` but VIMP can be recovered later using `vimp` or `predict`.
`bootstrap`	Bootstrap protocol. Default is `by.root` which bootstraps the data by sampling without replacement. If `none`, the data is not bootstrapped (it is not possible to return OOB ensembles or prediction error in this case).
`membership`	Should terminal node membership and inbag information be returned?
`na.action`	Action taken if the data contains `NA`'s. Possible values are `na.omit` or `na.impute`. The default `na.omit` removes the entire record if any entry is `NA`. Selecting `na.impute` imputes the data (see below for details). Also see the function `impute` for fast imputation.
`nimpute`	Number of iterations of the missing data algorithm. Performance measures such as out-of-bag (OOB) error rates are optimistic if `nimpute` is greater than 1.
`perf.type`	Optional character value specifying metric used for predicted value, variable importance (VIMP), and error rate. Reverts to the family default metric if not specified. Values allowed for univariate/multivariate classification are: `perf.type="misclass"` (default), `perf.type="brier"` and `perf.type="gmean"`.
`xvar.wt`	Vector of non-negative weights (does not have to sum to 1) representing the probability of selecting a variable for splitting. Default is uniform weights.
`yvar.wt`	Used for sending in features with custom splitting. For expert use only.
`split.wt`	Vector of non-negative weights used for multiplying the split statistic for a variable. A large value encourages the node to split on a specific variable. Default is uniform weights.
`case.wt`	Vector of non-negative weights (does not have to sum to 1) for sampling cases. Observations with larger weights will be selected with higher probability in the bootstrap (or subsampled) samples. It is generally better to use real weights rather than integers. See the breast data example below illustrating its use for class imbalanced data.
`forest`	Save key forest values? Used for prediction on new data and required by many of the package functions. Turn this off if you are only interested in training a forest.
`var.used`	Return statistics on number of times a variable split? Default is `FALSE`. Possible values are `all.trees` which returns total number of splits of each variable, and `by.tree` which returns a matrix of number a splits for each variable for each tree.
`split.depth`	Records the minimal depth for each variable. Default is `FALSE`. Possible values are `all.trees` which returns a matrix of the average minimal depth for a variable (columns) for a specific case (rows), and `by.tree` which returns a three-dimensional array recording minimal depth for a specific case (first dimension) for a variable (second dimension) for a specific tree (third dimension).
`seed`	Negative integer specifying seed for the random number generator.
`statistics`	Should split statistics be returned? Values can be parsed using `stat.split`.
`...`	Further arguments passed to or from other methods.

Details

Splitting

Splitting rules are specified by the option splitrule.
For all families, pure random splitting can be invoked by setting splitrule="random".
For all families, computational speed can be increased using randomized splitting invoked by the option nsplit. See Improving Computational Speed.

Available splitting rules

splitrule="gini" (default splitrule): Gini index splitting (Breiman et al. 1984, Chapter 4.3).
splitrule="auc": AUC (area under the ROC curve) splitting for both two-class and multiclass setttings. AUC splitting is appropriate for imbalanced data. See imbalanced for more information.
splitrule="entropy": entropy splitting (Breiman et al. 1984, Chapter 2.5, 4.3).

Value

An object of class (rfsrc, grow) with the following components:

`call`	The original call to `rfsrc` for growing the random forest object.
`family`	The family used in the analysis.
`n`	Sample size of the data (depends upon `NA`'s, see `na.action`).
`ntree`	Number of trees grown.
`mtry`	Number of variables randomly selected for splitting at each node.
`nodesize`	Minimum size of terminal nodes.
`nodedepth`	Maximum depth allowed for a tree.
`splitrule`	Splitting rule used.
`nsplit`	Number of randomly selected split points.
`yvar`	y-outcome values.
`yvar.names`	A character vector of the y-outcome names.
`xvar`	Data frame of x-variables.
`xvar.names`	A character vector of the x-variable names.
`xvar.wt`	Vector of non-negative weights for dimension reduction which specify the probability used to select a variable for splitting a node.
`split.wt`	Vector of non-negative weights specifying multiplier by which the split statistic for a covariate is adjusted.
`cause.wt`	Vector of weights used for the composite competing risk splitting rule.
`leaf.count`	Number of terminal nodes for each tree in the forest. Vector of length `ntree`. A value of zero indicates a rejected tree (can occur when imputing missing data). Values of one indicate tree stumps.
`proximity`	Proximity matrix recording the frequency of pairs of data points occur within the same terminal node.
`forest`	If `forest=TRUE`, the forest object is returned. This object is used for prediction with new test data sets and is required for other R-wrappers.
`membership`	Matrix recording terminal node membership where each column records node mebership for a case for a tree (rows).
`splitrule`	Splitting rule used.
`inbag`	Matrix recording inbag membership where each column contains the number of times that a case appears in the bootstrap sample for a tree (rows).
`var.used`	Count of the number of times a variable is used in growing the forest.
`imputed.indv`	Vector of indices for cases with missing values.
`imputed.data`	Data frame of the imputed data. The first column(s) are reserved for the y-outcomes, after which the x-variables are listed.
`split.depth`	Matrix (i,j) or array (i,j,k) recording the minimal depth for variable j for case i, either averaged over the forest, or by tree k.
`node.stats`	Split statistics returned when `statistics=TRUE` which can be parsed using `stat.split`.
`err.rate`	Tree cumulative OOB error rate.
`importance`	Variable importance (VIMP) for each x-variable.
`predicted`	In-bag predicted value.
`predicted.oob`	OOB predicted value.

`class`	In-bag predicted class labels.
`class.oob`	OOB predicted class labels.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


data(tnbc)
########### performance of Random Rank Forest
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
obj

data(tnbc)
########### performance of Random Rank Forest
obj <- rforest(subtype~., data = tnbc[,c(1:10,337)])
obj

Select Decision Rules to Achieve Higher Prediction Accuracy

Description

Select rules from a extrat.rules (rules) object

Usage

select.rules(object, data, data.pair = FALSE)
select.rules(object, data, data.pair = FALSE)

Arguments

`object`	An extracted rule `(rules)` object generated from the `extract.rules` function.
`data`	A validation dataset for selecting rules.
`data.pair`	Is data already converted into binary ranked pairs from the `pair` function?

Value

`rule`	Interpretable selected rules. Note that the performance score displayed is inaccurate based on few samples from the original argument `object`.
`rule.raw`	Rules directly extracted from trees for prediction purpose
`data`	Data used to grow trees from the argument `(object)`.

Author(s)

Ruijie Yin (Maintainer,<[email protected]>), Chen Ye and Min Lu

References

Lu M. Yin R. and Chen X.S. (2023). Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles.

Examples


data(tnbc)
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
objr <- extract.rules(obj)
predict(objr, tnbc[101:110,1:5])$label
objrs <- select.rules(objr,tnbc[110:130,c(1:5,337)])
predict(objrs, tnbc[101:110,1:5])$label

data(tnbc)
obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)])
objr <- extract.rules(obj)
predict(objr, tnbc[101:110,1:5])$label
objrs <- select.rules(objr,tnbc[110:130,c(1:5,337)])
predict(objrs, tnbc[101:110,1:5])$label

Gene expression profiles in triple-negative breast cancer cell

Description

Gene expression profiles in triple-negative breast cancer cells with 215 observations and 337 variables. Gene expression values were randomly chosen from the original dataset. The outcome variable is subtype.

Usage

data(tnbc)
data(tnbc)

Source

Chen, X., Li, J., Gray, W. H., Lehmann, B. D., Bauer, J. A., Shyr, Y., & Pietenpol, J. A. (2012). TNBCtype: a subtyping tool for triple-negative breast cancer. Cancer informatics, 11, CIN-S9983.

Examples

data(tnbc)
data(tnbc)

Package 'ranktreeEnsemble'

Help Index

Extract Interpretable Decision Rules from a Random Forest Model

Description

Usage

Arguments

Value

Author(s)

References

Examples

Variable Importance Index for Each Predictor

Description

Usage

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Value

Author(s)

References

Examples

Transform Continuous Variables into Ranked Binary Pairs

Description

Usage

Arguments

Value

Note

Author(s)

References

Examples

Prediction or Extract Predicted Values for Random Forest, Random Forest Rule or Boosting Models

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Ensemble Models of Rank-Based Trees for Single Sample Classification with Interpretable Rules

Description

Author(s)

References

Examples

Generalized Boosted Modeling via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles

Description

Usage

Arguments

Value

Author(s)

References

Examples

Random Forest via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Select Decision Rules to Achieve Higher Prediction Accuracy

Description

Usage

Arguments

Value

Author(s)

References

Examples

Gene expression profiles in triple-negative breast cancer cell

Description

Usage

Source

Examples