Package {int3ract}

Create Johnson-Neyman plots for Bayesian models

Description

Accepts either a raw matrix of posterior draws (rows = iterations, columns = parameters) or a multiSiena object produced by sienaBayes().

Usage

JNK_bayes(
  x,
  theta_1,
  theta_2,
  theta_3 = NULL,
  theta_int_12 = NULL,
  theta_int_13 = NULL,
  theta_int_23 = NULL,
  theta_int_123 = NULL,
  theta_1_vals,
  theta_2_vals,
  theta_3_vals = NULL,
  burn_in = NULL,
  thin = 1,
  thresholds = NULL,
  hyper_only = TRUE,
  round_res = 3,
  noTitle = NULL,
  color_mid = "#EBCC2A",
  color_low = "#3B9AB2",
  color_high = "#F21A00",
  color_values = "grey40",
  color_grid = "black",
  grid_density = 0.01,
  grid_spacing = 0.1,
  save = FALSE,
  folder = NULL
)

Arguments

Value

A list containing tables and plots. For two-way interactions: param_table and plots. For three-way: thetas, standard_errors, p_values, significance, and plots. When hyper_only = FALSE ('multiSiena'), also returns a list of group-level results under random_groups_effects.

Examples

# --- two-way: raw posterior matrix (fast, no extra packages) ---
set.seed(1)
n_iter <- 500
post2 <- cbind(x     = rnorm(n_iter,  0.5, 0.2),
               z     = rnorm(n_iter, -0.3, 0.2),
               `x:z` = rnorm(n_iter,  0.4, 0.2))

jnk_bayes2 <- JNK_bayes(post2,
                        theta_1 = 'x', theta_2 = 'z',
                        theta_1_vals = seq(-2, 2, 1),
                        theta_2_vals = seq(-2, 2, 1))

# --- three-way: raw posterior matrix ---
post3 <- cbind(x       = rnorm(n_iter,  0.5, 0.2),
               z       = rnorm(n_iter, -0.3, 0.2),
               w       = rnorm(n_iter,  0.2, 0.2),
               `x:z`   = rnorm(n_iter,  0.4, 0.2),
               `x:w`   = rnorm(n_iter,  0.1, 0.2),
               `z:w`   = rnorm(n_iter, -0.1, 0.2),
               `x:z:w` = rnorm(n_iter,  0.2, 0.2))

jnk_bayes3 <- JNK_bayes(post3,
                        theta_1 = 'x', theta_2 = 'z', theta_3 = 'w',
                        theta_1_vals = seq(-2, 2, 1),
                        theta_2_vals = seq(-2, 2, 1),
                        theta_3_vals = seq(-2, 2, 1))

# --- two-way: integration with MCMCpack (only if installed) ---

if (requireNamespace("MCMCpack", quietly = TRUE)) {
  set.seed(1402)
  dat   <- data.frame(x = rnorm(100), z = rnorm(100))
  dat$y <- dat$x + 0.5 * dat$x * dat$z - 0.5 * dat$z + rnorm(100, sd = 4)
  mod_bayes2 <- MCMCpack::MCMCregress(y ~ x * z, data = dat,
                                      burnin = 1000, mcmc = 10000,
                                      thin = 1, verbose = 0)
  jnk_bayes2_e <- JNK_bayes(mod_bayes2, theta_1 = 'x', theta_2 = 'z',
                            theta_1_vals = seq(-3, 3, 0.5),
                            theta_2_vals = seq(-3, 3, 0.5))
}

Johnson-Neyman(-Krause) plots for frequentist models

Description

Unified function that accepts lm, glm, sienaFit ('RSiena'), or lmerMod/glmerMod ('lme4') objects, or raw coefficient vectors and covariance matrices.

Usage

JNK_freq(
  x = NULL,
  theta_1,
  theta_2,
  theta_3 = NULL,
  theta_int_12 = NULL,
  theta_int_13 = NULL,
  theta_int_23 = NULL,
  theta_int_123 = NULL,
  theta_1_vals = NULL,
  theta_2_vals = NULL,
  theta_3_vals = NULL,
  covar = NULL,
  coefs = NULL,
  name = NULL,
  group_var = NULL,
  fixed_only = TRUE,
  control_fdr = FALSE,
  alpha = 0.05,
  round_res = 3,
  range_size = NULL,
  sig_color = "seagreen3",
  non_sig_color = "chocolate",
  line_color = "black",
  color_mid = "#EBCC2A",
  color_low = "#3B9AB2",
  color_high = "#F21A00",
  color_values = "grey40",
  color_grid = "black",
  grid_density = 0.01,
  grid_spacing = 0.1,
  crosshatch_non_sig = TRUE,
  save = FALSE,
  folder = NULL
)

Arguments

Value

A list containing tables and plots. For two-way interactions: param_table and plots. For three-way: thetas, standard_errors, p_values, significance, and plots. When fixed_only = FALSE ('lme4'), returns a list with fixed and random_groups elements.

Examples

# --- two-way lm ---
set.seed(1)
dat <- data.frame(y = rnorm(100), x = rnorm(100),
                  z = rnorm(100), w = rnorm(100))
res <- lm(y ~ x * z * w, dat)

x2 <- JNK_freq(res, theta_1 = 'x', theta_2 = 'z',
               range_size = 50)

# --- three-way lm (small grid for speed) ---
x3 <- JNK_freq(res, theta_1 = 'x', theta_2 = 'z', theta_3 = 'w',
               range_size = 10)

# --- generic (covariance + coefficients) ---
x2_gen <- JNK_freq(covar = vcov(res)[c('x','z','x:z'), c('x','z','x:z')],
                   coefs = coef(res)[c('x','z','x:z')],
                   name  = c('x', 'z'),
                   theta_1 = 'x',
                   theta_2 = 'z',
                   theta_1_vals = c(-3, 3),
                   theta_2_vals = c(-3, 3),
                   range_size = 50)