pwrRasch (version 0.1-2)

plot.pwrrasch: Plot Statistical Power Curve

Description

Generic plot function for the pwrrasch object, which plots the statistical power curve relating statistical power to sample size

Usage

# S3 method for pwrrasch
plot(x, plot.sig.level = TRUE, type = c("b", "b"),
  pch = c(19, 17), lty = c(1, 3), lwd = c(1, 1), legend = "topleft",
  bty = "o", ...)

Arguments

x

pwrrasch object.

plot.sig.level

If TRUE, nominal significance level is plotted.

type

Vector indicating type of plot for the statistica power curve and the type 1 risk curve.

pch

Vector indicating plotting symbol for the statistical power curve and the type 1 risk curve.

lty

Vector indicating line type for the statistical power curve and the type 1 risk curve.

lwd

Vector indicating line width for the statistical power curve and the type 1 risk curve.

legend

Location of the legend. If FALSE, legend is omitted.

bty

Type of box to be drawn around the legend.

...

Additional arguments affecting the summary produced.

Author

Takuya Yanagida , Jan Steinfeld

Details

Graphical parameters are:

• type The following values are possible: "p" for points, "l" for lines, "b" for both point and lines

• pch see points

• lty Line types can be specified as an integer (0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash)

• lwd Positive numbers indicating line widths

• legend Either the x and y coordinates to be used to position the legend or keyword from the list "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center"

• bty Allowed values are "o" (draw box around legend) and "n" (do not draw box around legend).

References

Kubinger, K. D., Rasch, D., & Yanagida, T. (2009). On designing data-sampling for Rasch model calibrating an achievement test. Psychology Science Quarterly, 51, 370-384.

Kubinger, K. D., Rasch, D., & Yanagida, T. (2011). A new approach for testing the Rasch model. Educational Research and Evaluation, 17, 321-333.

Examples

if (FALSE) {

# item parameters
ipar2 <- ipar1 <- seq(-3, 3, length.out = 20)
# model differential item function (DIF)
ipar2[10] <- ipar1[11]
ipar2[11] <- ipar1[10]
# simulation for b = 100, 200, 300, 400, 500
simres <- pwr.rasch(seq(100, 500, by = 100), ipar = list(ipar1, ipar2))
plot(simres)
}