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DFA scores plot

Source: R/DFA_class.R
dfa_scores_plot.Rd

A scatter plot of the selected DFA components.

Usage

dfa_scores_plot(
  components = c(1, 2),
  points_to_label = "none",
  factor_name,
  ellipse = "all",
  label_filter = character(0),
  label_factor = "rownames",
  label_size = 3.88,
  ...
)

Arguments

components

(numeric) The components selected for plotting. The default is c(1, 2).

points_to_label

(character) Points to label. Allowed values are limited to the following:

  • "none": No samples labels are displayed.

  • "all": The labels for all samples are displayed.

  • "outliers": Labels for for potential outlier samples are displayed.

The default is "none".

factor_name

(character) The name of a sample-meta column to use.

ellipse

(character) Plot ellipses. Allowed values are limited to the following:

  • "all": Hotelling T2 ellipses (p=0.95) are plotted for all groups and all samples.

  • "group": Hotelling T2 ellipses (p=0.95) are plotted for all groups.

  • "none": Ellipses are not included on the plot.

  • "sample": A Hotelling T2 ellipse (p=0.95) is plotted for all samples (ignoring group).

The default is "all".

label_filter

(character) Labels are only plotted for the named groups. If zero-length then all groups are included. The default is character(0).

label_factor

(character) The column name of sample_meta to use for labelling samples on the plot. "rownames" will use the row names from sample_meta. The default is "rownames".

label_size

(numeric) The text size of labels. Note this is not in Font Units. The default is 3.88.

...

Additional slots and values passed to struct_class.

Value

A dfa_scores_plot object. This object has no output slots. See chart_plot in the struct package to plot this chart object.

Details

This object makes use of functionality from the following packages:

  • scales

  • ggplot2

Inheritance

A dfa_scores_plot object inherits the following struct classes:

[dfa_scores_plot] >> [chart] >> [struct_class]

References

Wickham H, Pedersen T, Seidel D (2023). scales: Scale Functions for Visualization. R package version 1.3.0, https://CRAN.R-project.org/package=scales.

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

Examples

M = dfa_scores_plot(
      components = c(1, 2),
      points_to_label = "none",
      factor_name = "V1",
      ellipse = "all",
      label_filter = character(0),
      label_factor = "rownames",
      label_size = 3.88)

D = iris_DatasetExperiment()
M = mean_centre() + DFA(factor_name='Species')
M = model_apply(M,D)
C = dfa_scores_plot(factor_name = 'Species')
chart_plot(C,M[2])
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?
#> Warning: The following aesthetics were dropped during statistical transformation: label.
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?