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Last data update: 2014.03.03

R: Modified Classification and Regression with Random Forest

cinbag

R Documentation

Modified Classification and Regression with Random Forest

Description

cinbag implements a modified random forest algorithm (based on the source code from the randomForest package by Andy Liaw and Matthew Wiener and on the original Fortran code by Leo Breiman and Adele Cutler) to return the number of times a row appears in a tree's bag. cinbag returns a randomForest object, e.g., rfobj, with an additional output, a matrix with inbag counts (rows) for each tree (columns). For instance, rfobj$inbagCount is similar to rfobj$inbag, but with inbag counts instead of inbag indicators.

Usage

      cinbag(x, y=NULL,  xtest=NULL, ytest=NULL, ntree=500,
             mtry=if (!is.null(y) && !is.factor(y))
             max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))),
             replace=TRUE, classwt=NULL, cutoff, strata,
             sampsize = if (replace) nrow(x) else ceiling(.632*nrow(x)),
             nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1,
             maxnodes = NULL,
             importance=FALSE, localImp=FALSE, nPerm=1,
             proximity, oob.prox=proximity,
             norm.votes=TRUE, do.trace=FALSE,
             keep.forest=!is.null(y) && is.null(xtest), corr.bias=FALSE,
             keep.inbag=FALSE, ...)

Arguments

`x`	a data frame or a matrix of predictors, or a formula describing the model to be fitted (for the `print` method, an `randomForest` object).
`y`	A response vector. If a factor, classification is assumed, otherwise regression is assumed. If omitted, `randomForest` will run in unsupervised mode.
`xtest`	a data frame or matrix (like `x`) containing predictors for the test set.
`ytest`	response for the test set.
`ntree`	Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
`mtry`	Number of variables randomly sampled as candidates at each split. Note that the default values are different for classification (sqrt(p) where p is number of variables in `x`) and regression (p/3).
`replace`	Should sampling of cases be done with or without replacement?
`classwt`	Priors of the classes. Need not add up to one. Ignored for regression.
`cutoff`	(Classification only) A vector of length equal to number of classes. The ‘winning’ class for an observation is the one with the maximum ratio of proportion of votes to cutoff. Default is 1/k where k is the number of classes (i.e., majority vote wins).
`strata`	A (factor) variable that is used for stratified sampling.
`sampsize`	Size(s) of sample to draw. For classification, if sampsize is a vector of the length the number of strata, then sampling is stratified by strata, and the elements of sampsize indicate the numbers to be drawn from the strata.
`nodesize`	Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown (and thus take less time). Note that the default values are different for classification (1) and regression (5).
`maxnodes`	Maximum number of terminal nodes trees in the forest can have. If not given, trees are grown to the maximum possible (subject to limits by `nodesize`). If set larger than maximum possible, a warning is issued.
`importance`	Should importance of predictors be assessed?
`localImp`	Should casewise importance measure be computed? (Setting this to `TRUE` will override `importance`.)
`nPerm`	Number of times the OOB data are permuted per tree for assessing variable importance. Number larger than 1 gives slightly more stable estimate, but not very effective. Currently only implemented for regression.
`proximity`	Should proximity measure among the rows be calculated?
`oob.prox`	Should proximity be calculated only on “out-of-bag” data?
`norm.votes`	If `TRUE` (default), the final result of votes are expressed as fractions. If `FALSE`, raw vote counts are returned (useful for combining results from different runs). Ignored for regression.
`do.trace`	If set to `TRUE`, give a more verbose output as `randomForest` is run. If set to some integer, then running output is printed for every `do.trace` trees.
`keep.forest`	If set to `FALSE`, the forest will not be retained in the output object. If `xtest` is given, defaults to `FALSE`.
`corr.bias`	perform bias correction for regression? Note: Experimental. Use at your own risk.
`keep.inbag`	Should an `n` by `ntree` matrix be returned that keeps track of which samples are “in-bag” in which trees (but not how many times, if sampling with replacement)
`...`	optional parameters to be passed to the low level function `cinbag.default`.

Value

An object of class randomForest, which is a list with the following components:

`call`	the original call to `randomForest`
`type`	one of `regression`, `classification`, or `unsupervised`.
`predicted`	the predicted values of the input data based on out-of-bag samples.
`importance`	a matrix with `nclass` + 2 (for classification) or two (for regression) columns. For classification, the first `nclass` columns are the class-specific measures computed as mean descrease in accuracy. The `nclass` + 1st column is the mean descrease in accuracy over all classes. The last column is the mean decrease in Gini index. For Regression, the first column is the mean decrease in accuracy and the second the mean decrease in MSE. If `importance=FALSE`, the last measure is still returned as a vector.
`importanceSD`	The “standard errors” of the permutation-based importance measure. For classification, a `p` by `nclass + 1` matrix corresponding to the first `nclass + 1` columns of the importance matrix. For regression, a length `p` vector.
`localImp`	a p by n matrix containing the casewise importance measures, the [i,j] element of which is the importance of i-th variable on the j-th case. `NULL` if `localImp=FALSE`.
`ntree`	number of trees grown.
`mtry`	number of predictors sampled for spliting at each node.
`forest`	(a list that contains the entire forest; `NULL` if `randomForest` is run in unsupervised mode or if `keep.forest=FALSE`.
`err.rate`	(classification only) vector error rates of the prediction on the input data, the i-th element being the (OOB) error rate for all trees up to the i-th.
`confusion`	(classification only) the confusion matrix of the prediction (based on OOB data).
`votes`	(classification only) a matrix with one row for each input data point and one column for each class, giving the fraction or number of (OOB) ‘votes’ from the random forest.
`oob.times`	number of times cases are ‘out-of-bag’ (and thus used in computing OOB error estimate)
`proximity`	if `proximity=TRUE` when `randomForest` is called, a matrix of proximity measures among the input (based on the frequency that pairs of data points are in the same terminal nodes).
`mse`	(regression only) vector of mean square errors: sum of squared residuals divided by `n`.
`rsq`	(regression only) “pseudo R-squared”: 1 - `mse` / Var(y).
`test`	if test set is given (through the `xtest` or additionally `ytest` arguments), this component is a list which contains the corresponding `predicted`, `err.rate`, `confusion`, `votes` (for classification) or `predicted`, `mse` and `rsq` (for regression) for the test set. If `proximity=TRUE`, there is also a component, `proximity`, which contains the proximity among the test set as well as proximity between test and training data.
`inbag`	An indicator (1 or 0) for each training set row and each tree. The indicator is 1 if the training set row is in the tree's bag and is 0 otherwise. Note that this value is not listed in the original `randomForest` function's output, although it is implemented.
`inbagCount`	A count for each training set row and each tree. The count is the number of times the training set row is in the tree's bag. This output is not available in the original `randomForest` package. The purpose of the `cinbag` function is to augment the `randomForest` function so that it returns inbag counts. These counts are necessary for computing and ensembling the trees' empirical cumulative distribution functions.

Note

cinbag's source files call the C functions classRFmod.c and regRFmod.c, which are slightly modified versions of the randomForest's source files classRF.c and regRF.c, respectively.

Author(s)

Yael Grushka-Cockayne, Victor Richmond R. Jose, Kenneth C. Lichtendahl Jr. and Huanghui Zeng, based on the source code from the randomForest package by Andy Liaw and Matthew Wiener and on the original Fortran code by Leo Breiman and Adele Cutler.

References

Breiman L (2001). Random forests. Machine Learning 45 5-32.

Breiman L (2002). Manual on setting up, using, and understanding random forests V3.1. http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf.

Examples

# Load the data
set.seed(201) # Can be removed; useful for replication
data <- as.data.frame(mlbench.friedman1(500, sd=1))
summary(data)

# Prepare data for trimming
train <- data[1:400, ]
test <- data[401:500, ]
xtrain <- train[,-11]  
ytrain <- train[,11]
xtest <- test[,-11]
ytest <- test[,11]
      
# Run cinbag
set.seed(201) # Can be removed; useful for replication
rf <- cinbag(xtrain, ytrain, ntree=500, nodesize=5, mtry=3, keep.inbag=TRUE)
rf$inbag[,1] # First tree's inbag indicators 
rf$inbagCount[,1] # First tree's inbag counts