irtProb (version 1.2)

graphics: Graphic Functions to Illustrate Response Curves and Parameter Estimation

Description

Graphic functions to illustrate response curves and parameter estimation.

Usage

PCC(theta  = 0, S = 0, C = 0, D = 0, s      = 1/1.702, b = seq(-5, 5, length = 300), c = 0, d = 1, groups = TRUE, ID = "ID", main   = "Person Characteristic Curve", xlab   = "Item Difficulty Parameter (b)", ylab = "P(x = 1)", type   = c("g", "a"))

Arguments

theta

numeric; vector of person proficiency ($\theta$) levels scaled on a normal z score.

S

numeric: positive vector of personal fluctuation parameters ($\sigma$).

C

numeric: positive vector of personal pseudo-guessing parameters ($\chi$, a probability between 0 and 1).

D

numeric: positive vector of personal inattention parameters ($\delta$, a probability between 0 and 1).

s

numeric: vector of item fluctuation parameter or the inverse of item discrimination (s= 1/a).

b

numeric: vector of item discrimination parameter.

c

numeric: vector of item pseudo-guessing parameter.

d

numeric: vector of item inattention parameter.

ID

character: curves identification information displayed ("ID", "ALL", "THETA2 or NULL)

groups

logical: default to TRUE. If TRUE, Lattice xyplot by groups. If FALSE, xyplot with shingles.

main

character: first line of main title.

xlab

character: label of x axis.

ylab

character: label of y axis.

type

character: type of xyplot graphic. One of the following: "p", "l", "h", "b", "o", "s", "S", "r", "a", "g", "smooth".

Value

PCC returns a list:

graphic

trellis object: figures for each subject (group or shingle representation).

probability

data.frame: item snd person parameters, like th eprobability of a correct response.

Examples

## PCC curves grouped on a single figure
 res1 <- PCC(theta=c(-2,-2,-2),S=0, C=c(0.0, 0.1, 0.6), D=0.2,
             b=seq(-5,5,length=3000), ID=NULL, groups=TRUE,
             type=c("g","a"))
 res1
 
## PCC curves shingled on a single figure for each subject
 res2 <- PCC(theta=c(-2,-1,0),S=c(4.0,0.0, 1.0), C=c(0.0, 0.1, 0.6), D=0.2,
             b=seq(-5,5,length=3000), ID=NULL, groups=FALSE,
             type=c("g","a"))
 res2