classifyRNA: classifyRNA

In classSNitch: Classifies RNA Structure Change in Chemical Mapping Data

Description

A function to build a random forest classifier for RNA structure change

Usage

classifyRNA(data=NULL, cutoff=NULL)

Arguments

data	Optional data to build the classifier. Default is pre-loaded data.
cutoff	An optional 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).

Details

This function builds a random forest classifier for RNA structure change using the randomForest package.

Value

A classifyRNA object, based on randomForest object (see randomForest package)

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 columns. 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.

importanceSD

The standard errors of the permutation-based importance measure. A p by nclass + 1 matrix corresponding to the first nclass + 1 columns of the importance matrix.

ntree

Number of trees grown.

mtry

Number of predictors sampled for spliting at each node.

forest

A list that contains the entire forest

err.rate

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

The confusion matrix of the prediction (based on OOB data).

votes

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

A matrix of proximity measures among the input (based on the frequency that pairs of data points are in the same terminal nodes).

Note

Organization of the data file: header=TRUE, tab-delimited .txt file

• "column 1" class label

• "column 2" pattern correlation

• "column 3" dynamic time warping

• "column 4" contiguousness

• "column 5" magnitude correlation

• "column 6" change variance

• "column 7" eSDC

• "column 8" change range

• "column 9" L2 norm

The default data has been gathered from the RNA Mapping Database mutate and map experiments.

Author(s)

Chanin Tolson

References

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18–22 (randomForest package)

RNA Mapping Database

See Also

getFeatures

Examples

#build classifier
rf = classifyRNA()
#get confusion matrix
rf$confusion

classSNitch documentation built on May 2, 2019, 5:51 p.m.