SetMethods (version 4.0)

xy.plot: 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,
           ...)

Value

It returns an enhanced XY plot.

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!

Author

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")