sjPlot (version 2.5.0)

sjp.gpt: Plot grouped proportional tables

Description

Plot grouped proportional crosstables, where the proportion of each level of x for the highest category in y is plotted, for each subgroup of groups.

Usage

sjp.gpt(x, y, groups, geom.colors = "Set1", geom.size = 2.5,
  shape.fill.color = "#f0f0f0", shapes = c(15, 16, 17, 18, 21, 22, 23, 24,
  25, 7, 8, 9, 10, 12), title = NULL, axis.labels = NULL,
  axis.titles = NULL, legend.title = NULL, legend.labels = NULL,
  wrap.title = 50, wrap.labels = 15, wrap.legend.title = 20,
  wrap.legend.labels = 20, axis.lim = NULL, grid.breaks = NULL,
  show.total = TRUE, annotate.total = TRUE, show.p = TRUE,
  show.n = TRUE, prnt.plot = TRUE)

Arguments

x

Categorical variable, where the proportion of each category in x for the highest category of y will be printed along the x-axis.

y

Categorical or numeric variable. If not a binary variable, y will be recoded into a binary variable, dichtomized at the highest category and all remaining categories.

groups

Grouping variable, which will define the y-axis

geom.colors

user defined color for geoms. See 'Details' in sjp.grpfrq.

geom.size

size resp. width of the geoms (bar width, line thickness or point size, depending on plot type and function). Note that bar and bin widths mostly need smaller values than dot sizes.

shape.fill.color

Optional color vector, fill-color for non-filled shapes

shapes

Numeric vector with shape styles, used to map the different categories of x.

title

character vector, used as plot title. Depending on plot type and function, will be set automatically. If title = "", no title is printed. For effect-plots, may also be a character vector of length > 1, to define titles for each sub-plot or facet.

axis.labels

character vector with labels used as axis labels. Optional argument, since in most cases, axis labels are set automatically.

axis.titles

character vector of length one or two, defining the title(s) for the x-axis and y-axis.

legend.title

character vector, used as title for the plot legend.

legend.labels

character vector with labels for the guide/legend.

wrap.title

numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.

wrap.labels

numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.

wrap.legend.title

numeric, determines how many chars of the legend's title are displayed in one line and when a line break is inserted.

wrap.legend.labels

numeric, determines how many chars of the legend labels are displayed in one line and when a line break is inserted.

axis.lim

Numeric vector of length 2, defining the range of the plot axis. Depending on plot type, may effect either x- or y-axis, or both. For multiple plot outputs (e.g., from type = "eff" or type = "slope" in sjp.glm), axis.lim may also be a list of vectors of length 2, defining axis limits for each plot (only if non-faceted).

grid.breaks

numeric; sets the distance between breaks for the axis, i.e. at every grid.breaks'th position a major grid is being printed.

show.total

Logical, if TRUE, a total summary line for all aggregated groups is added.

annotate.total

Logical, if TRUE and show.total = TRUE, the total-row in the figure will be highlighted with a slightly shaded background.

show.p

Logical, adds significance levels to values, or value and variable labels.

show.n

logical, if TRUE, adds total number of cases for each group or category to the labels.

prnt.plot

logical, if TRUE (default), plots the results as graph. Use FALSE if you don't want to plot any graphs. In either case, the ggplot-object will be returned as value.

Value

(Insisibily) returns the ggplot-object with the complete plot (plot) as well as the data frame that was used for setting up the ggplot-object (df).

Details

The p-values are based on chisq.test of x and y for each groups.

Examples

# NOT RUN {
data(efc)

# the proportion of dependency levels in female
# elderly, for each family carer's relationship
# to elderly
sjp.gpt(efc$e42dep, efc$e16sex, efc$e15relat)

# proportion of educational levels in highest
# dependency category of elderly, for different
# care levels
sjp.gpt(efc$c172code, efc$e42dep, efc$n4pstu)

# }