plot.ssra: Plot ssra

In SSRA: Sakai Sequential Relation Analysis

Description

Function for plotting the ssra object

Usage

## S3 method for class 'ssra'
plot(x, r.crt = NULL, r.sig = TRUE, d.sq = NULL,
  m.sig = TRUE, sig.col = TRUE,
  col = c("red2", "green4", "blue3", "black"),
  pch = c(1, 2, 0, 4), mar = c(3.5, 3.5, 1.5, 1), ...)

Arguments

x	requires the return object from the SSRA function
r.crt	minimal absolute correlation to be judged 'sequential'
r.sig	plot statistically significant correlations
d.sq	minimal effect size Cohen's d to be judged 'sequential'
m.sig	plot statistically significant mean difference
sig.col	significance in different colors
col	color code or name
pch	plotting character
mar	number of lines of margin to be specified on the four sides of the plot
...	further arguments passed to or from other methods

Details

Using this function, all item pairs are plotted on a graph by their correlation coefficients and their mean differences (Cohen's d). This graph is useful for defining (or changing) criteria regarding correlation coefficient and mean difference to judge whether an item pair is 'sequential' or 'equal'.

Author(s)

Takuya Yanagida [e-mail], Keiko Sakai [e-mail]

References

Takeya, M. (1991). A new test theory: Structural analyses for educational information. Tokyo: Waseda University Press.

See Also

SSRA, treegram, scatterplot

Examples

# Example data based on Takeya (1991)

# Sakai Sequential Relation Analysis
# ordering assesed according to the correlation coefficient and mean difference
exdat.ssra <- SSRA(exdat, output = FALSE)
plot(exdat.ssra)

SSRA documentation built on May 2, 2019, 2:14 p.m.