Plot Simulated Response Times

Simulates data under the Joint Hierarchical Model and plots the resulting response time distribution. The plot type depends on `level` and `logRT`:

- **Person, seconds**: Density of mean response time per person (seconds), trimmed at the 99th percentile, with quantile reference lines. - **Person, log**: Same density on the log-RT scale (untrimmed). - **Item, seconds**: Per-item density of response times (seconds), trimmed at the 97.5th percentile per item, with the median marked. - **Item, log**: Per-item density on the log-RT scale (untrimmed, fixed y-axis across items).

Data are generated via [sim.jhm.data()] with `scale = FALSE`. Response times are on the log scale in the generative model; the seconds scale is obtained via exponentiation.

Usage

plot_RT(
  design = NULL,
  level = "item",
  logRT = FALSE,
  N = 1000,
  K = 30,
  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 = FALSE
)

Arguments

design: Optional list or `"sspLNIRT.design"` object holding the data-generating design. When supplied, its fields are used for any design-related argument the caller did not pass explicitly (`K`, `mu.person`, `mu.item`, `meanlog.sigma2`, `cov.m.person`, `cov.m.item`, `sd.item`, `sdlog.sigma2`, `item.pars.m`, `cor2cov.item`). Caller- supplied arguments always take precedence. Extra fields in `design` (e.g. `thresh`, `out.par`, `seed`) are ignored, so the object returned in `$design` by [get_sspLNIRT()] or [optim_sample()] can be passed directly. Default `NULL` (use the function defaults / caller arguments).
level: Character. `"person"` for person-level aggregates or `"item"` for item-level curves / distributions.
logRT: Logical. If `TRUE`, response times are shown on the log scale; if `FALSE`, on the seconds scale.
N: Integer. Sample size (number of persons) for the simulated data. Default is 1000.
K: Integer. Test length (number of items). Default is 10.
mu.person: Numeric vector of length 2. Population means of $(\theta, \zeta)$.
mu.item: Numeric vector of length 4. Population means of $(\alpha, \beta, \varphi, \lambda)$.
meanlog.sigma2: Numeric. Mean of the log-normal distribution for $\sigma^2$ (on the log scale).
cov.m.person: 2x2 symmetric matrix. Covariance matrix of $(\theta, \zeta)$.
cov.m.item: 4x4 symmetric matrix. Covariance (or correlation) matrix of $(\alpha, \beta, \varphi, \lambda)$. See `cor2cov.item`.
sd.item: Numeric vector of length 4. Standard deviations of $(\alpha, \beta, \varphi, \lambda)$. Required when `cor2cov.item = TRUE`.
sdlog.sigma2: Numeric. Standard deviation of the log-normal distribution for $\sigma^2$. Default is 0.
item.pars.m: Matrix with 4 columns or `NULL`. If supplied, item parameters are held fixed instead of drawn from the truncated MVN.
cor2cov.item: Logical. If `TRUE`, `cov.m.item` is treated as a correlation matrix and converted using `sd.item`.

Value

A [ggplot2::ggplot] object.

Examples

if (FALSE) { # \dontrun{
plot_RT(level = "person", logRT = TRUE,  N = 500, K = 10)
plot_RT(level = "item",   logRT = TRUE, N = 1000, K = 5,
        mu.item = c(1, 0, 0.4, 1), sd.item = c(0.2, 1, 0.2, 0.5))

# Pass a design object retrieved from get_sspLNIRT() (or optim_sample()):
res <- get_sspLNIRT(thresh = 0.10, out.par = "alpha",
                    K = 30, mu.alpha = 1,
                    meanlog.sigma2 = log(0.6), rho = 0.4)
plot_RT(res$design, level = "item", logRT = FALSE)
} # }

Usage

Arguments

Value

See also

Examples