randomVarImpsRFplot         package:varSelRF         R Documentation

_P_l_o_t _r_a_n_d_o_m _r_a_n_d_o_m _v_a_r_i_a_b_l_e _i_m_p_o_r_t_a_n_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Plot variable importances from random permutations of class labels
     and the variable importances from the original data set.

_U_s_a_g_e:

     randomVarImpsRFplot(randomImportances, forest,
                         whichImp = "impsUnscaled", nvars = NULL,
                         show.var.names = FALSE, vars.highlight = NULL,
                         main = NULL, screeRandom = TRUE,
                         lwdBlack = 1.5,
                         lwdRed = 2,
                         lwdGreen = 1,
                         cexPoint = 1,
                         overlayTrue = FALSE,
                         xlab = NULL,
                         ylab = NULL, ...)

_A_r_g_u_m_e_n_t_s:

randomImportances: A list with a structure such as the object return by
          'randomVarImpsRF'

  forest: A random forest fitted to the original data. This forest must
          have been fitted with 'importances = TRUE'.

whichImp: The importance measue to use. One (only one) of
          'impsUnscaled', 'impsScaled', 'impsGini', that correspond,
          respectively, to the (unscaled) mean decrease in accuracy,
          the scaled mean decrease in accuracy, and the Gini index. 
          See below and 'randomForest', 'importance' and the references
          for further explanations of the measures of variable
          importance. 

   nvars: If NULL will show the plot for the complete range of
          variables. If an integer, will plot only the most important
          nvars.

show.var.names: If TRUE, show the variable names in the plot. Unless
          you are plotting few variables, it probably won't be of any
          use.

vars.highlight: A vector indicating the variables to highlight in the
          plot with a vertical blue segment. You need to pass here a
          vector of variable names, not variable positions.

    main: The title for the plot.

screeRandom: If TRUE, order all the variable importances (i.e., those
          from both the original and the permuted class labels data
          sets) from largest to smallest before plotting. The plot will
          thus resemble a usual "scree plot". 

lwdBlack: The width of the line to use for the importances from the
          original data set.

  lwdRed: The width of the line to use for the average of the
          importances for the permuted data sets. 

lwdGreen: The width of the line for the importances for the individual
          permuted data sets.

cexPoint: 'cex' argument for the points for the importances from the
          original data set.

overlayTrue: If TRUE, the variable importance from the original data
          set will be plotted last, so you can see it even if buried in
          the middle of many gree lines; can be of help when the plot
          does not allow you to see the black line.

    xlab: The title for the x-axis (see 'xlab').

    ylab: The title for the y-axis (see 'ylab').

     ...: Additional arguments to plot.

_V_a_l_u_e:

     Only used for its side effects of producing plots. In particular,
     you will see lines of three colors: 

   black: 

   green: Connect the variable importances from the data sets with
          permuted class labels; there will be as many lines as
          'numrandom' where used when 'randomVarImpsRF' was called to
          obtain the random importances.

     red: Connects the average of the importances from the permuted
          data sets.

     Additionally, if you used a valid set of values for
     'vars.highlight', these will be shown with a vertical blue
     segment.

_N_o_t_e:

     These plots resemble the scree plots commonly used with principal
     component analysis, and the actual choice of colors was taken from
     the importance spectrum plots of Friedman & Meulman.

_A_u_t_h_o_r(_s):

     Ramon Diaz-Uriarte rdiaz@ligarto.org

_R_e_f_e_r_e_n_c_e_s:

     Breiman, L. (2001) Random forests. _Machine Learning_, *45*, 5-32.

     Diaz-Uriarte, R. , Alvarez de Andres, S. (2005) Variable selection
     from random forests: application to gene expression data. Tech.
     report. <URL:
     http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html>

     Friedman, J., Meulman, J. (2005) Clustering objects on subsets of
     attributes (with discussion). _J. Royal Statistical Society,
     Series B_, *66*, 815-850.

_S_e_e _A_l_s_o:

     'randomForest', 'varSelRF', 'varSelRFBoot', 'varSelImpSpecRF',
     'randomVarImpsRF'

_E_x_a_m_p_l_e_s:

     x <- matrix(rnorm(45 * 30), ncol = 30)
     x[1:20, 1:2] <- x[1:20, 1:2] + 2
     cl <- factor(c(rep("A", 20), rep("B", 25)))  

     rf <- randomForest(x, cl, ntree = 200, importance = TRUE)
     rf.rvi <- randomVarImpsRF(x, cl, 
                               rf, 
                               numrandom = 20, 
                               usingCluster = FALSE) 

     randomVarImpsRFplot(rf.rvi, rf)

