gpairs              package:YaleToolkit              R Documentation

_G_e_n_e_r_a_l_i_z_e_d _P_a_i_r_s _P_l_o_t_s

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

     Produces a matrix of plots showing pairwise relationships between
     quantitative and categorical variables in a complex data set.

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

     gpairs(x,
            upper.pars = list(scatter = "points",
                              conditional = "barcode",
                              mosaic = "mosaic"),
            lower.pars = list(scatter = "points",
                              conditional = "boxplot",
                              mosaic = "mosaic"),
            diagonal = "default",
            outer.margins = list(bottom = unit(2, "lines"), 
                                 left = unit(2, "lines"), 
                                 top = unit(2, "lines"), 
                                 right = unit(2, "lines")), 
            xylim = NULL,
            outer.labels = NULL, outer.rot = c(90, 0), gap = 0.05, 
            buffer = 0.02, reorder = NULL, cluster.pars = NULL, 
            stat.pars = NULL, scatter.pars = NULL, 
            bwplot.pars = NULL, stripplot.pars = NULL, barcode.pars=NULL,
            mosaic.pars = NULL, axis.pars = NULL, diag.pars = NULL, 
            whatis = FALSE)

     corrgram(x)

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

       x: a data frame (or matrix the relationships between whose
          columns are to be examined). Any combination of quantitative
          and categorical variables is acceptable.

upper.pars: see 'Details'

lower.pars: see 'Details'

diagonal: by default, the diagonal from the top left to the bottom
          right is used for displaying the variable names (and, in our
          version, the marginal distributions of the variables);
          'diagonal="other"' will place the diagonal running from the
          top right down to the bottom left.

outer.margins: a list of length 4 with units as components named
          bottom, left, top, and right, giving the outer margins; the
          default uses two lines of text.  A vector of length 4 with
          units (ordered properly) will work, as will a vector of
          length 4 with numeric values (interpreted as lines).

   xylim: optionally specify a single range to be used as 'xlim' and
          'ylim' where appropriate.  Note that if this option causes
          cropping, it will fail to work in barcode panels.

outer.labels: the default is 'NULL', for alternating axis labels around
          the perimeter.  If '"all"', all labels are printed, and if
          '"none"' no labels are printed.

outer.rot: a 2-vector (x, y) rotating the top/bottom outer labels 'x'
          degrees and the left/right outer labels 'y' degrees. Only
          works for categorical labels of boxplot and mosaic panels.

     gap: the gap between the tiles; defaulting to 0.05 of the width of
          a tile.

  buffer: the fraction by which to expand the range of quantitative
          variables to provide plots that will not truncate plotting
          symbols. Defaults to 0 percent of range currently.

 reorder: currently only support for the string '"cluster"', which
          reorders the columns according to the output of 'hclust'.
          Note that factors are coerced to numbers by replacing them
          with integers, which implicitly assumes what is probably an
          arbitrary ordering.

cluster.pars: a list with two elements named 'dist.method' and
          'hclust.method'. These are passed respectively to 'dist' and
          'hclust'. 'NULL' is equivalent to 'list(dist.method =
          "euclidean", hclust.method = "complete")'.

stat.pars: 'NULL' is equivalent to 'list(fontsize = 7, signif = 0.05,
          verbose = FALSE, use.color = TRUE, missing = 'missing', just
          = 'centre')'; 'stat.pars\$verbose' can be 'TRUE' (providing 5
          statistics), 'FALSE' (providing 2 statistics), or 'NA'
          (nothing).  The string 'missing' is used in summaries where
          there are missing values; 'fontsize' and 'just' control the
          size and justification of the text summaries (see 'grid.text'
          and 'gpar'.  The 'use.color=FALSE' option provides an
          alternative summary of the strength of the correlation (see
          Green and Hickey (2006)).  This is only used with
          'scatter="stats")' in 'upper.pars' and 'lower.pars'.

scatter.pars: 'NULL' is equivalent to 'list(pch = 1, size = unit(0.25,
          "char"), col = "black", frame.fill = NULL, border.col =
          "black")'.

bwplot.pars: 'NULL', passed to 'bwplot' for producing boxplots.

stripplot.pars: 'NULL' is equivalent to 'list(pch = 1, size = unit(0.5,
          'char'), col = 'black', jitter = FALSE)'.

barcode.pars: 'NULL' is equivalent to 'list(nint = 0, ptsize =
          unit(0.25, "char"), ptpch = 1, bcspace = NULL, use.points =
          FALSE)'.

mosaic.pars: 'NULL'. Currently, only 'shade' and 'gp_labels' are passed
          through to 'strucplot' for producing mosaic tiles.

axis.pars: 'NULL' is equivalent to 'list(n.ticks = 5, fontsize = 9)'.

diag.pars: 'NULL' is equivalent to 'list(fontsize = 9, show.hist =
          TRUE, hist.color = 'black')'.

  whatis: default is 'FALSE'; 'TRUE' returns 'whatis(x)'.

_D_e_t_a_i_l_s:

     In some cases, the graphics device can not be resized after
     production of the plot because of the way rotation of barcodes is
     performed.

     'upper.pars' and 'lower.pars' are lists possibly containing named
     elements ''scatter'', ''conditional'' and ''mosaic''. Each element
     of the list is a string implementing the following options:
     'scatter' = exactly one of '('points', 'lm', 'ci', 'symlm',
     'loess', 'corrgram', 'stats', 'qqplot')';  ''conditional'' =
     exactly one of '('boxplot', 'stripplot',  'barcode')';
     'mosaic='mosaic'' (only option currently implemented).

     'corrgram()' is just a wrapper to 'gpairs()' producing a
     `corrgram' in the style of Michael Friendly.

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

     If 'whatis=TRUE', the value is a data frame containing variable
     names, types, numbers of missing values, numbers of distinct
     values, precisions, maxima and minima.

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

     John W. Emerson, Walton Green

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

     Emerson, John W. (1998) "Mosaic Displays in S-PLUS: A General
     Implementation and a Case Study."  {\it Statistical Computing and
     Graphics Newsletter} Vol. 9,No. 1, 1998.

     Basford, K. E. and J. W. Tukey (1999) {\it Graphical Analysis of
     Multiresponse Data: Illustrated with a Plant Breeding Trial.}

     Friendly, M. (2000). {\it Visualizing Categorical Data.} SAS
     Press.

     Friendly, M., 2002, `Corrgrams: Exploratory displays for
     correlation matrices.' {\it American Statistician} 56(4), 316-324.

     Green, W. A. (2006) Loosening the CLAMP: An exploratory graphical
     approach to the Climate Leaf Analysis Multivariate Program {\it
     Palaeontologia Electronica} 9(2):9A.

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

     'pairs', 'splom', 'mosaicplot', 'strucplot', 'bwplot', 'barcode',
     'stripplot'.

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

     allexamples <- FALSE

     y <- data.frame(A=c(rep("red", 100), rep("blue", 100)),
                     B=c(rnorm(100),round(rnorm(100,5,1),1)), C=runif(200),
                     D=c(rep("big", 150), rep("small", 50)),
                     E=rnorm(200))
     gpairs(y)

     data(iris)
     gpairs(iris)
     if (allexamples) {
       gpairs(iris, upper.pars = list(scatter = 'stats'),
              scatter.pars = list(pch = substr(as.character(iris$Species), 1, 1),
                                  col = as.numeric(iris$Species)),
              stat.pars = list(verbose = FALSE))
       gpairs(iris, lower.pars = list(scatter = 'corrgram'),
              upper.pars = list(conditional = 'boxplot', scatter = 'loess'),
              scatter.pars = list(pch = 20))
     }

     data(Leaves)
     gpairs(Leaves[1:10], lower.pars = list(scatter = 'loess'))
     if (allexamples) {
       gpairs(Leaves[1:10], upper.pars = list(scatter = 'stats'),
              lower.pars = list(scatter = 'corrgram'),
              stat.pars = list(verbose = FALSE), gap = 0)
       corrgram(Leaves[,-33])
     }

     runexample <- FALSE
     if (runexample) {
       data(NewHavenResidential)
       gpairs(NewHavenResidential)
     }

