cdplot               package:graphics               R Documentation

_C_o_n_d_i_t_i_o_n_a_l _D_e_n_s_i_t_y _P_l_o_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes and plots conditional densities describing how the
     conditional distribution of a categorical variable 'y' changes
     over a numerical variable 'x'.

_U_s_a_g_e:

     cdplot(x, ...)

     ## Default S3 method:
     cdplot(x, y,
       plot = TRUE, tol.ylab = 0.05,
       bw = "nrd0", n = 512, from = NULL, to = NULL,
       col = NULL, border = 1, main = "", xlab = NULL, ylab = NULL,
       yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), ...)

     ## S3 method for class 'formula':
     cdplot(formula, data = list(),
       plot = TRUE, tol.ylab = 0.05,
       bw = "nrd0", n = 512, from = NULL, to = NULL,
       col = NULL, border = 1, main = "", xlab = NULL, ylab = NULL,
       yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), ...,
       subset = NULL)

_A_r_g_u_m_e_n_t_s:

       x: an object, the default method expects either a single
          numerical variable.

       y: a '"factor"' interpreted to be the dependent variable

 formula: a '"formula"' of type 'y ~ x' with a single dependent
          '"factor"' and a single numerical explanatory variable.

    data: an optional data frame.

    plot: logical. Should the computed conditional densities be
          plotted?

tol.ylab: convenience tolerance parameter for y-axis annotation. If the
          distance between two labels drops under this threshold, they
          are plotted equidistantly.

bw, n, from, to, ...: arguments passed to 'density'

     col: a vector of fill colors of the same length as 'levels(y)'.
          The default is to call 'gray.colors'.

  border: border color of shaded polygons.

main, xlab, ylab: character strings for annotation

yaxlabels: character vector for annotation of y axis, defaults to
          'levels(y)'.

xlim, ylim: the range of x and y values with sensible defaults.

  subset: an optional vector specifying a subset of observations to be
          used for plotting.

_D_e_t_a_i_l_s:

     'cdplot' computes the conditional densities of 'x' given the
     levels of 'y' weighted by the marginal distribution of 'y'. The
     densities are derived cumulatively over the levels of 'y'.

     This visualization technique is similar to spinograms (see
     'spineplot') and plots P(y | x) against x. The conditional
     probabilities are not derived by descretization (as in the
     spinogram), but using a smoothing approach via 'density'.

     Note, that the estimates of the conditional densities are more
     reliable for  high-density regions of x. Conversely, the are less
     reliable in regions with only few x observations.

_V_a_l_u_e:

     The conditional density functions (cumulative over the levels of
     'y') are returned invisibly.

_A_u_t_h_o_r(_s):

     Achim Zeileis Achim.Zeileis@R-project.org

_R_e_f_e_r_e_n_c_e_s:

     Hofmann, H., Theus, M. (2005), _Interactive graphics for
     visualizing conditional distributions_, Unpublished Manuscript.

_S_e_e _A_l_s_o:

     'spineplot', 'density'

_E_x_a_m_p_l_e_s:

     ## NASA space shuttle o-ring failures
     fail <- factor(c(2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1),
                    levels = 1:2, labels = c("no", "yes"))
     temperature <- c(53, 57, 58, 63, 66, 67, 67, 67, 68, 69, 70, 70, 70, 70, 72, 73, 75,
                      75, 76, 76, 78, 79, 81)

     ## CD plot
     cdplot(fail ~ temperature)
     cdplot(fail ~ temperature, bw = 2)
     cdplot(fail ~ temperature, bw = "SJ")

     ## compare with spinogram
     (spineplot(fail ~ temperature, breaks = 3))

     ## scatter plot with conditional density
     cdens <- cdplot(fail ~ temperature, plot = FALSE)
     plot(I(as.numeric(fail) - 1) ~ jitter(temperature, factor = 2),
          xlab = "Temperature", ylab = "Conditional failure probability")
     lines(53:81, 1 - cdens[[1]](53:81), col = 2)

