jn_plot_func: Function to generate a Johnson-Neyman Plot of Conditional...

In rblimp: Integration of 'Blimp' Software into R

Function to generate a Johnson-Neyman Plot of Conditional Slope based on a function to produce the conditional effect.

Description

Generates a Johnson-Neyman Plot using a function to produce the conditional effect

Usage

jn_plot_func(func, xrange, ci = 0.95, ...)

Arguments

func	a function used to compute the conditional effect on moderator.
xrange	a numeric of length two with the min and max of the x-axis
ci	a value between 0 and 1 specifying the credible interval size
...	values passed to internal boundary search algorithm. See Details below.

Details

To change colors use ggplot2's scale system. Both fill and color are used. See ggplot2::aes_colour_fill_alpha for more information about setting a manual set of colors.

For ..., the arguments are passed to the internal boundary search algorithm. This algorithm uses an initial grid search to locate boundaries based on the range and then a binary search to refine the estimates. The following arguments are available:

n_initial

Number of points in the initial coarse grid search used to locate approximate boundary positions. Higher values improve detection of closely-spaced boundaries but increase computation time. Default is 1000.

refine_tol

Tolerance for binary search refinement. The algorithm refines each boundary until the interval width is smaller than this value. Smaller values give higher precision but require more function evaluations. Default is 1e-12.

max_iter

Maximum number of iterations for binary search refinement per boundary. Prevents infinite loops if tolerance cannot be achieved. Default is 100.

adaptive

Logical indicating whether to perform additional refinement in regions where boundaries are detected to be closely spaced. When TRUE, uses a finer grid to resolve boundaries that may be missed by the initial coarse grid. Default is TRUE.

Value

a ggplot2::ggplot plot. The bounding values are saved in the attribute 'bounds'.

See Also

compute_condeff()

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x1 ~ normal(0, 1)',
        'x2 ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x1 + 0.5*x2 + m + 0.2*x1*x2 + 0.3*x2*m + 0.1*x1*m + 0.7*x1*x2*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    'y ~ x1 x2 m x1*x2 x1*m x2*m x1*x2*m',
    mydata,
    center = ~ m,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Get parameter values
params <- m1 |> as.matrix()

# Generate Plot
(
    jn_plot_func(
        compute_condeff(params[,6], params[,9]),
        xrange = c(-3, 3)
    )
    + ggplot2::labs(
        title = 'Johnson-Neyman Plot for `x1` * `x2` Moderated by `x2`',
        subtitle = 'Red area represents 0 within 95% interval',
        y = 'y ~ x1 * x2',
        x = 'm'
    )
    + ggplot2::theme_minimal()
)

rblimp documentation built on May 18, 2026, 9:07 a.m.