xy.plot: Function producing enhanced XY plots

In nenaoana/SetMethods: Functions for Set-Theoretic Multi-Method Research and Advanced QCA

Function producing enhanced XY plots

Description

xy.plot produces XY plots and provides values for consistency, Haesebrouck's consistency, coverage, RoN, PRI. Several graphic parameters can be decided by the user.

Usage

xy.plot(x, y, data, 
           labcol = "black",
           main = "XY plot", 
           ylab = "Outcome", 
           xlab = "Condition",
           necessity = FALSE,
           jitter = FALSE, 
           font = "sans",
           fontface = "italic", 
           fontsize = 3,
           labs = rownames(data),
           crisp = FALSE,
           shape = 19,
           consH = FALSE,
           ...)

Arguments

x	vector containing the condition.
y	vector containing the outcome.
data	The dataset used
labcol	color of the dots.
main	an overall title for the plot. The default is "XY plot". See ?title.
ylab	a title for the y-axis. The default is "Outcome". See ?title.
xlab	a title for the x-axis. The default is "Condition". See ?title.
necessity	logical. Indicates if the parameters of fit are calculated for a sufficient or necessary condition. The default is FALSE, therefore it calculates the parameters of fit for sufficiency. To get the parameters of fit for necessary conditions set necessity as TRUE.
jitter	Logical. Should labels be jitter to not overlap?
font	Font of the labels. Accepts "sans", "serif", and "mono" fonts.
fontface	Fontface of the labels. Accepts "plain", "bold", "italic", "bold.italic".
fontsize	Fontsize of the labels.
labs	the vector of case labels. The default is the rownames of the dataset.
crisp	Logical. Should a two-by-two table for crisp sets be returned?
shape	The shape for the markers.
consH	Logical. Should Haesebrouck's consistency be printed?
...	Other internal arguments. Do not specify!

Value

It returns an enhanced XY plot.

Author(s)

Mario Quaranta and Ioana-Elena Oana.

References

Haesebrouck, T. (2015) Pitfalls in QCA's consistency measure. Journal of Comparative Politics 2:65-80.

Ragin, C. C. (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. The Chicago University Press: Chicago and London.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge Univeristy Press: Cambridge.

Examples

# Load the Schneider data:

data(SCHF)

# Plot of condition EMP as necessary for outcome EXPORT with case labels 
# and names for the plot and axes:

xy.plot("EMP", "EXPORT", data=SCHF, necessity = TRUE,
            main = "EMP as necessary for EXPORT", ylab = "EXPORT", xlab = "EMP")

nenaoana/SetMethods documentation built on April 7, 2023, 2:20 p.m.