partialplot

本程序是基于linux系统下c++代码

partialPlot           package:randomForest           R Documentation

_P_a_r_t_i_a_l _d_e_p_e_n_d_e_n_c_e _p_l_o_t

_D_e_s_c_r_i_p_t_i_o_n:

     Partial dependence plot gives a graphical depiction of the
     marginal effect of a variable on the class probability
     (classification) or response (regression).

_U_s_a_g_e:

     ## S3 method for class 'randomForest':
     partialPlot(x, pred.data, x.var, which.class,
           w, plot = TRUE, add = FALSE,
           n.pt = min(length(unique(pred.data[, xname])), 51),
           rug = TRUE, xlab=deparse(substitute(x.var)), ylab="",
           main=paste("Partial Dependence on", deparse(substitute(x.var))),
           ...) 

_A_r_g_u_m_e_n_t_s:

       x: an object of class 'randomForest', which contains a 'forest'
          component.

pred.data: a data frame used for contructing the plot, usually the
          training data used to contruct the random forest.

   x.var: name of the variable for which partial dependence is to be
          examined.

which.class: For classification data, the class to focus on (default
          the first class).

       w: weights to be used in averaging; if not supplied, mean is not
          weighted

    plot: whether the plot should be shown on the graphic device.

     add: whether to add to existing plot ('TRUE').

    n.pt: if 'x.var' is continuous, the number of points on the grid
          for evaluating partial dependence.

     rug: whether to draw hash marks at the bottom of the plot
          indicating the deciles of 'x.var'.

    xlab: label for the x-axis.

    ylab: label for the y-axis.

    main: main title for the plot.

     ...: other graphical parameters to be passed on to 'plot' or
          'lines'.

_D_e_t_a_i_l_s:

     The function being plotted is defined as:

          tilde{f}(x) = frac{1}{n} sum_{i=1}^n f(x, x_{iC}),

     where x is the variable for which partial dependence is sought,
     and x_{iC} is the other variables in the data.  The summand is the
     predicted regression function for regression, and logits (i.e.,
     log of fraction of votes) for 'which.class' for classification:

        f(x) = log p_k(x) - frac{1}{K} sum_{j=1}^K log p_j(x),

     where K is the number of classes, k is 'which.class', and p_j is
     the proportion of votes for class j.

_V_a_l_u_e:

     A list with two components: 'x' and 'y', which are the values used
     in the plot.

_N_o_t_e:

     The 'randomForest' object must contain the 'forest' component;
     i.e., created with 'randomForest(..., keep.forest=TRUE)'.

     This function runs quite slow for large data sets.

_A_u_t_h_o_r(_s):

     Andy Liaw [e-mail]

_R_e_f_e_r_e_n_c_e_s:

     Friedman, J. (2001). Greedy function approximation: the gradient
     boosting machine, _Ann. of Stat._

_S_e_e _A_l_s_o:

     'randomForest'

_E_x_a_m_p_l_e_s:

     data(airquality)
     airquality <- na.omit(airquality)
     set.seed(131)
     ozone.rf <- randomForest(Ozone ~ ., airquality)
     partialPlot(ozone.rf, airquality, Temp)

     data(iris)
     set.seed(543)
     iris.rf <- randomForest(Species~., iris)
     partialPlot(iris.rf, iris, Petal.Width, "versicolor")