Plot the distribution of an index across decision categories

Draws the diagnostic that sqi_validate treats as its headline: how many samples the index places in each soil-health band. An index that piles everything into the middle cannot inform a decision, and that is visible here in a way it is not in a correlation coefficient.

Usage

plot_sqi_validation(
  x,
  col = c("#d73027", "#fc8d59", "#fee08b", "#d9ef8b", "#1a9850"),
  ...
)

Arguments

x

An sqi_validation object, an sqi_result, or a numeric vector of index values. The latter two are validated on the fly with default settings.

col

Fill colours for the bars, recycled across bands. Defaults to a red-to-green ramp so that "very low" and "very high" read at a glance.

...

Additional graphical parameters passed to barplot.

Value

Invisibly returns the sqi_validation object that was plotted.

See also

sqi_validate, plot_sqi_report

Examples

props <- c("pH", "OM", "N", "P", "K", "CEC", "BD")
result <- compute_sqi_properties(soil_data, properties = props,
                                 id_column = "SampleID")
plot_sqi_validation(result)
#> Warning: 100% of samples fall in the middle bands (threshold 80%). An index that declines to separate samples cannot inform a decision, however well it correlates with anything else. Consider a scoring or aggregation route that discriminates more strongly -- see sqi_stability().