spineplot              package:graphics              R Documentation

_S_p_i_n_e _P_l_o_t_s _a_n_d _S_p_i_n_o_g_r_a_m_s

_D_e_s_c_r_i_p_t_i_o_n:

     Spine plots are a special cases of mosaic plots, and can be seen
     as a generalization of stacked (or highlighted) bar plots.
     Analogously, spinograms are an extension of histograms.

_U_s_a_g_e:

     spineplot(x, ...)

     ## Default S3 method:
     spineplot(x, y = NULL,
               breaks = NULL, tol.ylab = 0.05, off = NULL,
               ylevels = NULL, col = NULL,
               main = "", xlab = NULL, ylab = NULL,
               xaxlabels = NULL, yaxlabels = NULL,
               xlim = NULL, ylim = c(0, 1), ...)

     ## S3 method for class 'formula':
     spineplot(formula, data = list(),
               breaks = NULL, tol.ylab = 0.05, off = NULL,
               ylevels = NULL, col = NULL,
               main = "", xlab = NULL, ylab = NULL,
               xaxlabels = 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
          variable (interpreted to be the explanatory variable) or a
          2-way table.  See details.

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

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

    data: an optional data frame.

  breaks: if the explanatory variable is numeric, this controls how it
          is discretized. 'breaks' is passed to 'hist' and can be a
          list of arguments.

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

     off: vertical offset between the bars (in per cent). It is fixed
          to '0' for spinograms and defaults to '2' for spine plots.

 ylevels: a character or numeric vector specifying in which order the
          levels of the dependent variable should be plotted.

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

main, xlab, ylab: character strings for annotation

xaxlabels, yaxlabels: character vectors for annotation of x and y axis.
          Default to 'levels(y)' and 'levels(x)', respectively for the
          spine plot. For 'xaxlabels' in the spinogram, the breaks are
          used.

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

     ...: additional arguments passed to 'rect'.

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

_D_e_t_a_i_l_s:

     'spineplot' creates either a spinogram or a spine plot.  It can be
     called via 'spineplot(x, y)' or 'spineplot(y ~ x)' where 'y' is
     interpreted to be the dependent variable (and has to be
     categorical) and 'x' the explanatory variable.  'x' can be either
     categorical (then a spine plot is created) or numerical (then a
     spinogram is plotted).  Additionally, 'spineplot' can also be
     called with only a single argument which then has to be a 2-way
     table, interpreted to correspond to 'table(x, y)'.

     Both, spine plots and spinograms, are essentially mosaic plots
     with special formatting of spacing and shading.  Conceptually,
     they plot P(y | x) against P(x).  For the spine plot (where both x
     and y are categorical), both quantities are approximated by the
     corresponding empirical relative frequencies.  For the spinogram
     (where x is numerical), x is first discretized (by calling 'hist'
     with 'breaks' argument) and then empirical relative frequencies
     are taken.

     Thus, spine plots can also be seen as a generalization of stacked
     bar plots where not the heights but the widths of the bars
     corresponds to the relative frequencies of 'x'. The heights of the
     bars then correspond to the conditional relative frequencies of
     'y' in every 'x' group. Analogously, spinograms extend stacked
     histograms.

_V_a_l_u_e:

     The table visualized is 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:

     Friendly, M. (1994), Mosaic displays for multi-way contingency
     tables. _Journal of the American Statistical Association_, *89*,
     190-200.

     Hartigan, J.A., and Kleiner, B. (1984), A mosaic of television
     ratings. _The American Statistician_, *38*, 32-35.

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

     Hummel, J. (1996), Linked bar charts: Analysing categorical data
     graphically. _Computational Statistics_, *11*, 23-33.

_S_e_e _A_l_s_o:

     'mosaicplot', 'hist', 'cdplot'

_E_x_a_m_p_l_e_s:

     ## treatment and improvement of patients with rheumatoid arthritis
     treatment <- factor(rep(c(1, 2), c(43, 41)), levels = c(1, 2),
                         labels = c("placebo", "treated"))
     improved <- factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),
                        levels = c(1, 2, 3),
                        labels = c("none", "some", "marked"))

     ## (dependence on a categorical variable)
     (spineplot(improved ~ treatment))

     ## applications and admissions by department at UC Berkeley
     ## (two-way tables)
     (spineplot(margin.table(UCBAdmissions, c(3, 2)),
                main = "Applications at UCB"))
     (spineplot(margin.table(UCBAdmissions, c(3, 1)),
                main = "Admissions at UCB"))

     ## 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 = c(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)

     ## (dependence on a numerical variable)
     (spineplot(fail ~ temperature))
     (spineplot(fail ~ temperature, breaks = 3))
     (spineplot(fail ~ temperature, breaks = quantile(temperature)))

     ## highlighting for failures
     spineplot(fail ~ temperature, ylevels = 2:1)

