Plot the simulated response accuracy data

The function simulates data under the Joint Hierarchical Model using a 2-pl normal ogive model for response accuracy and a 3-pl log-normal model for response time, and plots the resulting response accuracy data.

Usage

plot_RA(
  level,
  by.theta = FALSE,
  N = 10000,
  K = 10,
  mu.person = c(0, 0),
  mu.item = c(1, 0, 0.5, 1),
  meanlog.sigma2 = log(0.6),
  cov.m.person = matrix(c(1, 0.4, 0.4, 1), ncol = 2, byrow = TRUE),
  cov.m.item = matrix(c(1, 0, 0, 0, 0, 1, 0, 0.4, 0, 0, 1, 0, 0, 0.4, 0, 1), ncol = 4,
    byrow = TRUE),
  sd.item = c(0.2, 1, 0.2, 0.5),
  sdlog.sigma2 = 0,
  item.pars.m = NULL,
  cor2cov.item = TRUE
)

Arguments

level

String. Either "person" or "item".

by.theta

Logical. Whether to plot as a function of theta.

N

Integer. The sample size.

K

Integer. The test length.

mu.person

Numeric vector. Means of theta and zeta.

mu.item

Numeric vector. Means of alpha, beta, phi, and lambda.

meanlog.sigma2

Numeric. The meanlog of sigma2.

cov.m.person

Matrix. The covariance matrix of theta and zeta.

cov.m.item

Matrix. The covariance matrix of alpha, beta, phi, and lambda.

sd.item

Numeric vector. The standard deviations of alpha, beta, phi, and lambda.

sdlog.sigma2

Numeric. The sdlog of sigma2.

item.pars.m

Matrix (optional). A matrix containing item parameters.

cor2cov.item

Logical. Whether a correlation matrix instead of covariance matrix is supplied.

Value

A ggplot object.

Examples

 if (FALSE) { # \dontrun{
   plot_RA(level = "item",
           by.theta = TRUE,
           mu.item = c(1, 0, 1, 1),
           sd.item = c(.2, .5, .2, .5),
           meanlog.sigma2 = log(.2),
           K = 2)
} # }