randomforest

本程序是基于linux系统下c++代码

randomForest          package:randomForest          R Documentation

_C_l_a_s_s_i_f_i_c_a_t_i_o_n _a_n_d _R_e_g_r_e_s_s_i_o_n _w_i_t_h _R_a_n_d_o_m _F_o_r_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     'randomForest' implements Breiman's random forest algorithm (based
     on Breiman and Cutler's original Fortran code) for classification
     and regression.  It can also be used in unsupervised mode for
     assessing proximities among data points.

_U_s_a_g_e:

     ## S3 method for class 'formula':
     randomForest(formula, data=NULL, ..., subset, na.action=na.fail)
     ## Default S3 method:
     randomForest(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,
                  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, ...)
     ## S3 method for class 'randomForest':
     print(x, ...)

_A_r_g_u_m_e_n_t_s:

    data: an optional data frame containing the variables in the model.
          By default the variables are taken from the environment which
          'randomForest' is called from.

  subset: an index vector indicating which rows should be used. (NOTE:
          If given, this argument must be named.)

na.action: A function to specify the action to be taken if NAs are
          found.  (NOTE: If given, this argument must be named.)

x, formula: 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).

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
          'randomForest.default'.

_V_a_l_u_e:

     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 error rate for all
          trees up to the i-th.

confusion: (classification only) the confusion matrix of the
          prediction.

   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 `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.

_N_o_t_e:

     The 'forest' structure is slightly different between
     classification and regression.  For details on how the trees are
     stored, see the help page for 'getTree'.

     If 'xtest' is given, prediction of the test set is done ``in
     place'' as the trees are grown.  If 'ytest' is also given, and
     'do.trace' is set to some positive integer, then for every
     'do.trace' trees, the test set error is printed.  Results for the
     test set is returned in the 'test' component of the resulting
     'randomForest' object.  For classification, the 'votes' component
     (for training or test set data) contain the votes the cases
     received for the classes.  If 'norm.votes=TRUE', the fraction is
     given, which can be taken as predicted probabilities for the
     classes.

     For large data sets, especially those with large number of
     variables, calling 'randomForest' via the formula interface is not
     advised: There may be too much overhead in handling the formula.

     The ``local'' (or casewise) variable importance is computed as
     follows:  For classification, it is the increase in percent of
     times a case is OOB and misclassified when the variable is
     permuted.  For regression, it is the average increase in squared
     OOB residuals when the variable is permuted.

_A_u_t_h_o_r(_s):

     Andy Liaw [e-mail] and Matthew Wiener
     [e-mail], based on original Fortran code by Leo
     Breiman and Adele Cutler.

_R_e_f_e_r_e_n_c_e_s:

     Breiman, L. (2001), _Random Forests_, Machine Learning 45(1),
     5-32.

     Breiman, L (2002), ``Manual On Setting Up, Using, And
     Understanding Random Forests V3.1'', <URL:
     http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf>.

_S_e_e _A_l_s_o:

     'predict.randomForest', 'varImpPlot'

_E_x_a_m_p_l_e_s:

     ## Classification:
     ##data(iris)
     set.seed(71)
     iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE,
                             proximity=TRUE)
     print(iris.rf)
     ## Look at variable importance:
     round(importance(iris.rf), 2)
     ## Do MDS on 1 - proximity:
     iris.mds <- cmdscale(1 - iris.rf$proximity, eig=TRUE)
     op <- par(pty="s")
     pairs(cbind(iris[,1:4], iris.mds$points), cex=0.6, gap=0,
           col=c("red", "green", "blue")[as.numeric(iris$Species)],
           main="Iris Data: Predictors and MDS of Proximity Based on RandomForest")
     par(op)
     print(iris.mds$GOF)

     ## The `unsupervised' case:
     set.seed(17)
     iris.urf <- randomForest(iris[, -5])
     MDSplot(iris.urf, iris$Species)

     ## Regression:
     ## data(airquality)
     set.seed(131)
     ozone.rf <- randomForest(Ozone ~ ., data=airquality, mtry=3,
                              importance=TRUE, na.action=na.omit)
     print(ozone.rf)
     ## Show "importance" of variables: higher value mean more important:
     round(importance(ozone.rf), 2)

     ## "x" can be a matrix instead of a data frame:
     set.seed(17)
     x <- matrix(runif(5e2), 100)
     y <- gl(2, 50)
     (myrf <- randomForest(x, y))
     (predict(myrf, x))

     ## "complicated" formula:
     (swiss.rf <- randomForest(sqrt(Fertility) ~ . - Catholic + I(Catholic < 50),
                               data=swiss))
     (predict(swiss.rf, swiss))