pliman (version 2.1.0)

summary_index: Summary an object index

Description

If more than one index is available, the function performs a Principal Component Analysis and produces a plot showing the contribution of the indexes to the PC1 (see pca()). If an index is declared in index and a cut point in cut_point, the number and proportion of objects with mean value of index bellow and above cut_point are returned. Additionaly, the number and proportion of pixels bellow and above the cutpoint is shown for each object (id).

Usage

summary_index(
  object,
  index = NULL,
  cut_point = NULL,
  select_higher = FALSE,
  plot = TRUE,
  type = "var",
  ...
)

Value

A list with the following elements:

• ids The identification of selected objects.

• between_id A data frame with the following columns

  • n The number of objects.

  • nsel The number of selected objects.

  • prop The proportion of objects selected.

  • mean_index_sel, and mean_index_nsel The mean value of index for the selected and non-selected objects, respectively.

• within_id A data frame with the following columns

  • id The object identification

  • n_less The number of pixels with values lesser than or equal to cut_point.

  • n_greater The number of pixels with values greater than cut_point.

  • less_ratio The proportion of pixels with values lesser than or equal to cut_point.

  • greater_ratio The proportion of pixels with values greater than cut_point.

• pca_res An object computed with pca()

Arguments

object

An object computed with analyze_objects().

index

The index desired, e.g., "B". Note that these value must match the index(es) used in the argument object_index of analyze_objects().

cut_point

The cut point.

select_higher

If FALSE (default) selects the objects with index smaller than the cut_point. Use select_higher = TRUE to select the objects with index higher than cut_point.

plot

Shows the contribution plot when more than one index is available? Defaults to TRUE.

type

The type of plot to produce. Defaults to "var". See more at get_biplot().

...

Further arguments passed on to get_biplot().

Author

Tiago Olivoto

Examples

library(pliman)
soy <- image_pliman("soy_green.jpg")
anal <- analyze_objects(soy, object_index = "G", pixel_level_index = TRUE)
plot_measures(anal, measure = "G")

summary_index(anal, index = "G", cut_point = 0.5)