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Plot Johnson-Neyman Data

Source: R/gen_data_jn.R
plot.jn_df.Rd

Plot Johnson-Neyman Data

Usage

# S3 method for class 'jn_df'
plot(
  x,
  scales = "fixed",
  nrow = NULL,
  ncol = NULL,
  font_family = NULL,
  legend.position = c("bottom", "right"),
  slope.symbol = c("b", "beta", "bhat", "betahat"),
  font_size_max = 7,
  x_digits = NULL,
  y_digits = NULL,
  x_scientific = TRUE,
  y_scientific = TRUE,
  ...
)

Arguments

x

An object of class jn_df (the output from gen_data_jn()).

scales

Character vector specifying whether facet scales should be "fixed", "free_y", "free_x", or "free". Default is "fixed".

nrow

Number of rows in the facet grid. Default is NULL.

ncol

Number of columns in the facet grid. Default is NULL.

font_family

Font family name for the plot text. If NULL (default), dynamically checks and uses "Arial", falling back to "Helvetica", then "sans".

legend.position

Character vector specifying the legend position: "bottom" (default) or "right".

slope.symbol

Character vector specifying the symbol to represent the slope in the y-axis label: "b" (italicized b, default), "beta" (italicized Greek beta), "bhat" (italicized b with a hat), or "betahat" (Greek beta with a hat).

x_digits

Integer specifying the number of decimal places for rounding the x-axis tick labels. Default is NULL (no rounding).

y_digits

Integer specifying the number of decimal places for rounding the y-axis tick labels. Default is NULL (no rounding).

x_scientific

Logical flag indicating whether to use clean plotmath scientific notation formatting for the x-axis tick labels. Default is TRUE.

y_scientific

Logical flag indicating whether to use clean plotmath scientific notation formatting for the y-axis tick labels. Default is TRUE.

...

Additional arguments (not used).

Value

A ggplot object.

See also

Other contrast or COPE helpers: check_contrast_orthogonality(), gen_contrast_blank(), gen_contrast_ss(), gen_data_jn(), tidy_es_cope_F(), tidy_es_cope_t()

Examples

# 1. Simple moderator example using real values
fit1 <- lm(salary ~ yrs.since.phd * yrs.service, data = carData::Salaries)
c1 <- gen_contrast_ss(
  fit1,
  x = "yrs.since.phd",
  m = list(yrs.service = "real")
)
df_tidy_cope1 <- tidy_es(multcomp::glht(fit1, linfct = c1))
plot(gen_data_jn(df_tidy_cope1), font_size_max = 12)


# 2. Multi-moderator polynomial example with free y scales and custom layout
fit2 <- lm(
  salary ~ scale(yrs.since.phd, scale = FALSE) * scale(yrs.service, scale = FALSE) +
    I(scale(yrs.since.phd, scale = FALSE)^2) * scale(yrs.service, scale = FALSE),
  data = carData::Salaries
)
c2 <- gen_contrast_ss(fit2, x = "yrs.since.phd", m = list(yrs.since.phd = "real", yrs.service = "sd"))
df_tidy_cope2 <- tidy_es(multcomp::glht(fit2, c2), mcc = "none")
plot(gen_data_jn(df_tidy_cope2), scales = "free_y", nrow = 1, font_size_max = 10)