Display p-values as significance stars or numbers

return_p_value_as_stars() is useful to display p-values in a plot. It inserts into the map_signif_level_values argument of any plotting function in this patchclampplotteR package (which is really the map_signif_level of ggsignif::geom_signif()). map_signif_level_values = FALSE or map_signif_level_values = TRUE will display only stars or only numbers. This function will display stars for values <= 0.05 like usual, but it will display raw numeric values is the p-value is between 0.05 and 0.1. This is for transparency when significance values are close to 0.05 threshold. The upper threshold can be adjusted.

Usage

return_p_value_as_stars(p, upper_threshold = 0.1)

Arguments

p

A numeric value which is the p-value.

upper_threshold

A numeric value describing the upper value cutoff when p-values will no longer be displayed as numeric, but "ns" instead. Defaults to 0.1 so p-values between 0.05 and 0.1 will display as numbers.

Value

A numeric or character value.

Examples

# Simplest use

# Use for `map_signif_level_values`
# in any plotting function in `patchclampplotteR`.

# This will use the default `upper_threshold` value of 0.1.

plot_change_as_connected_lines(
  data = sample_summary_eEPSC_df$summary_data,
  plot_treatment = "Control",
  plot_category = 2,
  included_sexes = "both",
  map_signif_level_values = return_p_value_as_stars,
  post_hormone_interval = "t20to25",
  theme_options = sample_theme_options,
  treatment_colour_theme = sample_treatment_names_and_colours
)


# Change upper_threshold

# To change this value, you must use an anonymous function
# because `map_signif_level` requires a numeric, single argument `p`.

plot_change_as_connected_lines(
  data = sample_summary_eEPSC_df$summary_data,
  plot_treatment = "Control",
  plot_category = 2,
  included_sexes = "both",
  map_signif_level_values = function(p) {
    return_p_value_as_stars(p,
      upper_threshold = 0.2
    )
  },
  post_hormone_interval = "t20to25",
  theme_options = sample_theme_options,
  treatment_colour_theme = sample_treatment_names_and_colours
)