Convenience stats for row and column matrix factors

These statistical transformations (stats) adapt conventional ggplot2 stats to one or the other matrix factor of a tbl_ord, in lieu of stat_rows() or stat_cols(). They accept the same parameters as their corresponding conventional stats.

Usage

stat_rows_density_2d(
  mapping = NULL,
  data = NULL,
  geom = "density_2d",
  position = "identity",
  ...,
  contour = TRUE,
  contour_var = "density",
  n = 100,
  h = NULL,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_cols_density_2d(
  mapping = NULL,
  data = NULL,
  geom = "density_2d",
  position = "identity",
  ...,
  contour = TRUE,
  contour_var = "density",
  n = 100,
  h = NULL,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_rows_density_2d_filled(
  mapping = NULL,
  data = NULL,
  geom = "density_2d_filled",
  position = "identity",
  ...,
  contour = TRUE,
  contour_var = "density",
  n = 100,
  h = NULL,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_cols_density_2d_filled(
  mapping = NULL,
  data = NULL,
  geom = "density_2d_filled",
  position = "identity",
  ...,
  contour = TRUE,
  contour_var = "density",
  n = 100,
  h = NULL,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_rows_ellipse(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  ...,
  type = "t",
  level = 0.95,
  segments = 51,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_cols_ellipse(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  ...,
  type = "t",
  level = 0.95,
  segments = 51,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_rows_center(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun = NULL,
  fun.center = NULL,
  fun.min = NULL,
  fun.max = NULL,
  fun.ord = NULL,
  fun.args = list()
)

stat_cols_center(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun = NULL,
  fun.center = NULL,
  fun.min = NULL,
  fun.max = NULL,
  fun.ord = NULL,
  fun.args = list()
)

stat_rows_star(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun = NULL,
  fun.center = NULL,
  fun.ord = NULL,
  fun.args = list()
)

stat_cols_star(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  fun.data = NULL,
  fun = NULL,
  fun.center = NULL,
  fun.ord = NULL,
  fun.args = list()
)

stat_rows_chull(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_chull(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_peel(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  num = NULL,
  by = 1L,
  breaks = c(0.5),
  cut = c("above", "below"),
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_peel(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  num = NULL,
  by = 1L,
  breaks = c(0.5),
  cut = c("above", "below"),
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_cone(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  origin = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_cone(
  mapping = NULL,
  data = NULL,
  geom = "path",
  position = "identity",
  origin = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_depth(
  mapping = NULL,
  data = NULL,
  geom = "contour",
  position = "identity",
  contour = TRUE,
  contour_var = "depth",
  notion = "zonoid",
  notion_params = list(),
  n = 100L,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_depth(
  mapping = NULL,
  data = NULL,
  geom = "contour",
  position = "identity",
  contour = TRUE,
  contour_var = "depth",
  notion = "zonoid",
  notion_params = list(),
  n = 100L,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_depth_filled(
  mapping = NULL,
  data = NULL,
  geom = "contour_filled",
  position = "identity",
  contour = TRUE,
  contour_var = "depth",
  notion = "zonoid",
  notion_params = list(),
  n = 100L,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_depth_filled(
  mapping = NULL,
  data = NULL,
  geom = "contour_filled",
  position = "identity",
  contour = TRUE,
  contour_var = "depth",
  notion = "zonoid",
  notion_params = list(),
  n = 100L,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_scale(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  mult = 1
)

stat_cols_scale(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  mult = 1
)

stat_rows_spantree(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  engine = "mlpack",
  method = "euclidean",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_spantree(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  engine = "mlpack",
  method = "euclidean",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_bagplot(
  mapping = NULL,
  data = NULL,
  geom = "bagplot",
  position = "identity",
  fraction = 0.5,
  coef = 3,
  median = TRUE,
  fence = TRUE,
  outliers = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_cols_bagplot(
  mapping = NULL,
  data = NULL,
  geom = "bagplot",
  position = "identity",
  fraction = 0.5,
  coef = 3,
  median = TRUE,
  fence = TRUE,
  outliers = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_rows_rule(
  mapping = NULL,
  data = NULL,
  geom = "rule",
  position = "identity",
  fun.lower = "minpp",
  fun.upper = "maxpp",
  fun.offset = "minabspp",
  fun.args = list(),
  referent = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ref_subset = NULL,
  ref_elements = "active",
  ...
)

stat_cols_rule(
  mapping = NULL,
  data = NULL,
  geom = "rule",
  position = "identity",
  fun.lower = "minpp",
  fun.upper = "maxpp",
  fun.offset = "minabspp",
  fun.args = list(),
  referent = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ref_subset = NULL,
  ref_elements = "active",
  ...
)

stat_rows_projection(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  referent = NULL,
  ref_subset = NULL,
  ref_elements = "active",
  ...,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_cols_projection(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  referent = NULL,
  ref_subset = NULL,
  ref_elements = "active",
  ...,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data for this layer. When using a stat_*() function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following:

• A Geom ggproto subclass, for example GeomPoint.

• A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

• For more information and other ways to specify the geom, see the layer geom documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

• The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

• A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

• For more information and other ways to specify the position, see the layer position documentation.

...

Additional arguments passed to ggplot2::layer().

contour

If TRUE, contour the results of the 2d density estimation.

contour_var

Character string identifying the variable to contour by. Can be one of "density", "ndensity", or "count". See the section on computed variables for details.

n

Number of grid points in each direction.

h

Bandwidth (vector of length two). If NULL, estimated using MASS::bandwidth.nrd().

adjust

A multiplicative bandwidth adjustment to be used if 'h' is 'NULL'. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example, adjust = 1/2 means use half of the default bandwidth.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

type

The type of ellipse. The default "t" assumes a multivariate t-distribution, and "norm" assumes a multivariate normal distribution. "euclid" draws a circle with the radius equal to level, representing the euclidean distance from the center. This ellipse probably won't appear circular unless coord_fixed() is applied.

level

The level at which to draw an ellipse, or, if type="euclid", the radius of the circle to be drawn.

segments

The number of segments to be used in drawing the ellipse.

fun.data

A function that is given the complete data and should return a data frame with variables ymin, y, and ymax.

fun.center

Deprecated alias to fun.

fun.min, fun, fun.max

Alternatively, supply three individual functions that are each passed a vector of values and should return a single number.

fun.ord

Alternatively to the ggplot2::stat_summary_bin() parameters, supply a summary function that takes a matrix as input and returns a named column summary vector. Overridden by fun.data and fun, cannot be used together with fun.min and fun.max.

fun.args

Optional additional arguments passed on to the functions.

num

A positive integer; the number of hulls to peel. Pass Inf for all hulls.

by

A positive integer; with what frequency to include consecutive hulls, pairs with num.

breaks

A numeric vector of fractions (between 0 and 1) of the data to contain in each hull; overridden by num.

cut

Character; one of "above" and "below", indicating whether each hull should contain at least or at most breaks of the data, respectively.

origin

Logical; whether to include the origin with the transformed data. Defaults to FALSE.

notion

Character; the name of the depth function (passed to ddalpha::depth.()).

notion_params

List of additional parameters passed via ... to ddalpha::depth.().

mult

Numeric value used to scale the coordinates.

engine

A single character string specifying the package implementation to use; "mlpack", "vegan", or "ade4".

method

Passed to stats::dist() if engine is "vegan" or "ade4", ignored if "mlpack".

fraction

Fraction of the data to include in the bag.

coef

Scale factor of the fence relative to the bag.

median, fence, outliers

Logical indicators whether to include median, fence, and outliers in the composite output.

fun.lower, fun.upper, fun.offset

Functions used to determine the limits of the rules and the translations of the axes from the projections of referent onto the axes and onto their normal vectors.

referent

The reference data set; see Details.

ref_elements, ref_subset

Analogues of elements and subset applied to referent.

Value

A ggproto layer.

Ordination aesthetics

This statistical transformation is compatible with the convenience function ord_aes().

Some transformations (e.g. stat_center()) commute with projection to the lower (1 or 2)-dimensional biplot space. If they detect aesthetics of the form ..coord[0-9]+, then ..coord1 and ..coord2 are converted to x and y while any remaining are ignored.

Other transformations (e.g. stat_spantree()) yield different results in a lower-dimensional biplot when they are computed before versus after projection. If the stat layer detects these aesthetics, then the transformation is performed before projection, and the results in the first two dimensions are returned as x and y.

A small number of transformations (stat_rule()) are incompatible with ordination aesthetics but will accept ord_aes() without warning.

See also

Other biplot layers: biplot-geoms, stat_rows()

Examples

iris_pca <- ordinate(iris, prcomp, cols = seq(4), scale. = TRUE)
# NB: Non-standard aesthetics are handled as in version > 3.5.1; see:
# https://github.com/tidyverse/ggplot2/issues/6191
# This prevents `scale_color_discrete(aesthetics = ...)` from synching them.
ggbiplot(iris_pca) +
  stat_rows_bagplot(
    aes(fill = Species),
    median_gp = list(color = sync()),
    fence_gp = list(linewidth = 0.25),
    outlier_gp = list(shape = "asterisk")
  ) +
  scale_color_discrete(name = "Species", aesthetics = c("color", "fill")) +
  geom_cols_vector(aes(label = name))

ggbiplot(iris_pca) +
  stat_rows_bagplot(
    aes(fill = Species, color = Species),
    median_gp = list(color = sync()),
    fence_gp = list(linewidth = 0.25),
    outlier_gp = list(shape = "asterisk")
  ) +
  geom_cols_vector(aes(label = name))


# scaled PCA of Anderson iris measurements
iris[, -5] %>%
  princomp(cor = TRUE) %>%
  as_tbl_ord() %>%
  mutate_rows(species = iris$Species) %>%
  print() -> iris_pca
#> # A tbl_ord of class 'princomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: Comp.1, Comp.2, ..., Comp.4
#> # 
#> # Rows (principal): [ 150 x 4 | 1 ]
#>   Comp.1 Comp.2  Comp.3 ... |   species
#>                             |   <fct>  
#> 1  -2.26  0.480  0.128      | 1 setosa 
#> 2  -2.08 -0.674  0.235      | 2 setosa 
#> 3  -2.36 -0.342 -0.0442 ... | 3 setosa 
#> 4  -2.30 -0.597 -0.0913     | 4 setosa 
#> 5  -2.39  0.647 -0.0157     | 5 setosa 
#> # ℹ 145 more rows     | # ℹ 145 more rows
#> 
#> # 
#> # Columns (standard): [ 4 x 4 | 0 ]
#>   Comp.1 Comp.2 Comp.3 ... | 
#>                            | 
#> 1  0.521 0.377   0.720     | 
#> 2 -0.269 0.923  -0.244 ... | 
#> 3  0.580 0.0245 -0.142     | 
#> 4  0.565 0.0669 -0.634     | 
# row-principal biplot with depth median-based stars
iris_pca %>%
  ggbiplot(aes(color = species)) +
  theme_bw() +
  scale_color_brewer(type = "qual", palette = 2) +
  stat_rows_star(alpha = .5, fun.ord = "depth_median") +
  geom_rows_point(alpha = .5) +
  stat_rows_center(fun.ord = "depth_median", size = 4, shape = 1L) +
  ggtitle(
    "Row-principal PCA biplot of Anderson iris measurements",
    "Segments connect each observation to its within-species depth median"
  )


# correspondence analysis of combined female and male hair and eye color data
HairEyeColor %>%
  rowSums(dims = 2L) %>%
  MASS::corresp(nf = 2L) %>%
  as_tbl_ord() %>%
  augment_ord() %>%
  print() -> hec_ca
#> # A tbl_ord of class 'correspondence': (4 x 2) x (4 x 2)'
#> # 2 coordinates: Can1 and Can2
#> # 
#> # Rows (standard): [ 4 x 2 | 1 ]
#>     Can1   Can2 |   name 
#>                 |   <chr>
#> 1 -1.10   1.44  | 1 Black
#> 2 -0.324 -0.219 | 2 Brown
#> 3 -0.283 -2.14  | 3 Red  
#> 4  1.83   0.467 | 4 Blond
#> # 
#> # Columns (standard): [ 4 x 2 | 1 ]
#>     Can1   Can2 |   name 
#>                 |   <chr>
#> 1 -1.08   0.592 | 1 Brown
#> 2  1.20   0.556 | 2 Blue 
#> 3 -0.465 -1.12  | 3 Hazel
#> 4  0.354 -2.27  | 4 Green
# inertia across artificial coordinates (all singular values < 1)
get_inertia(hec_ca)
#>       Can1       Can2 
#> 0.20877265 0.02222661 
# in row-principal biplot, row coordinates are weighted averages of columns
# (and vice-versa)
hec_ca %>%
  confer_inertia("rows") %>%
  ggbiplot(aes(color = .matrix, fill = .matrix, shape = .matrix)) +
  theme_bw() +
  stat_cols_chull(alpha = .1) +
  geom_cols_point() +
  geom_rows_point() +
  ggtitle("Row-principal CA of hair & eye color")


# centered principal components analysis of U.S. personal expenditure data
USPersonalExpenditure %>%
  prcomp() %>%
  as_tbl_ord() %>%
  augment_ord() %>%
  # allow radiating text to exceed plotting window
  ggbiplot(aes(label = name), clip = "off",
           sec.axes = "cols", scale.factor = 50) +
  geom_rows_label(size = 3) +
  # omit labels in the conical hull without the origin
  geom_cols_vector(vector_labels = FALSE) +
  stat_cols_cone(linetype = "dotted") +
  geom_cols_vector(stat = "cone", vector_labels = TRUE, color = "transparent") +
  ggtitle(
    "U.S. Personal Expenditure data, 1940-1960",
    "Row-principal biplot of centered PCA"
  )

# compute row-principal components of scaled iris measurements
iris[, -5] %>%
  prcomp(scale = TRUE) %>%
  as_tbl_ord() %>%
  mutate_rows(species = iris$Species) %>%
  print() -> iris_pca
#> # A tbl_ord of class 'prcomp': (150 x 4) x (4 x 4)'
#> # 4 coordinates: PC1, PC2, ..., PC4
#> # 
#> # Rows (principal): [ 150 x 4 | 1 ]
#>     PC1    PC2     PC3 ... |   species
#>                            |   <fct>  
#> 1 -2.26 -0.478  0.127      | 1 setosa 
#> 2 -2.07  0.672  0.234      | 2 setosa 
#> 3 -2.36  0.341 -0.0441 ... | 3 setosa 
#> 4 -2.29  0.595 -0.0910     | 4 setosa 
#> 5 -2.38 -0.645 -0.0157     | 5 setosa 
#> # ℹ 145 more rows     | # ℹ 145 more rows
#> 
#> # 
#> # Columns (standard): [ 4 x 4 | 0 ]
#>      PC1     PC2    PC3 ... | 
#>                             | 
#> 1  0.521 -0.377   0.720     | 
#> 2 -0.269 -0.923  -0.244 ... | 
#> 3  0.580 -0.0245 -0.142     | 
#> 4  0.565 -0.0669 -0.634     | 
# row-principal biplot with centroids and confidence elliptical disks
iris_pca %>%
  ggbiplot(aes(color = species)) +
  theme_bw() +
  geom_rows_point() +
  geom_polygon(
    aes(fill = species),
    color = NA, alpha = .25, stat = "rows_ellipse"
  ) +
  geom_cols_vector(color = "#444444") +
  scale_color_brewer(
    type = "qual", palette = 2,
    aesthetics = c("color", "fill")
  ) +
  ggtitle(
    "Row-principal PCA biplot of Anderson iris measurements",
    "Overlaid with 95% confidence disks"
  )


# hull peeling with breaks below
judge_pca <- ordinate(USJudgeRatings, princomp, cols = -c(1, 12))
ggbiplot(judge_pca, axis.type = "predictive") +
  geom_cols_axis() +
  geom_rows_point(elements = "score") +
  stat_rows_peel(
    aes(alpha = after_stat(hull)), color = "black", elements = "score",
    breaks = c(.9, .5, .1), cut = "below"
  )
#> Warning: Using alpha for a discrete variable is not advised.


# hull peeling by groups
iris_pca <- ordinate(iris, cols = 1:4, model = prcomp)
ggbiplot(iris_pca) +
  geom_rows_point(aes(color = Species), shape = "circle open") +
  stat_rows_peel(
    aes(fill = Species, alpha = after_stat(hull)),
    num = 3
  )
#> Warning: Using alpha for a discrete variable is not advised.


# unscaled PCA
iris_pca <- ordinate(iris, cols = 1:4, model = prcomp)
# biplot canvas
iris_biplot <- 
  iris_pca %>%
  ggbiplot(aes(color = Species, label = name), axis.type = "predictive") +
  geom_rows_point() +
  geom_cols_axis(aes(center = center))
# print select cases
top_cases <- c(1, 51, 101)
iris[top_cases, ]
#>     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#> 1            5.1         3.5          1.4         0.2     setosa
#> 51           7.0         3.2          4.7         1.4 versicolor
#> 101          6.3         3.3          6.0         2.5  virginica
# subset variables
length_vars <- c(1, 3)
iris[, length_vars] %>%
  aggregate(by = iris[, "Species", drop = FALSE], FUN = mean)
#>      Species Sepal.Length Petal.Length
#> 1     setosa        5.006        1.462
#> 2 versicolor        5.936        4.260
#> 3  virginica        6.588        5.552

# project all cases onto all axes
iris_biplot + stat_rows_projection()

# project all cases onto select axes
iris_biplot + stat_rows_projection(ref_subset = length_vars)
#> `subset` will be applied after data are restricted to active elements.
#> This message is displayed once per session.


# project select cases onto all axes
iris_biplot + stat_rows_projection(subset = top_cases)

# project select cases onto select axes
iris_biplot + stat_rows_projection(subset = top_cases, ref_subset = length_vars)


# project select cases onto manually provided axes
iris_cols <- as.data.frame(get_cols(iris_pca))[c(1, 2), ]
iris_biplot + stat_rows_projection(subset = top_cases, referent = iris_cols)


# project selected cases onto selected axes in full-dimensional space
iris_pca %>%
  ggbiplot(ord_aes(iris_pca, color = Species, label = name),
           axis.type = "predictive") +
  geom_rows_point() +
  geom_cols_axis(aes(center = center)) +
  stat_rows_projection(subset = top_cases, ref_subset = length_vars)


# default (standardized) linear discriminant analysis
glass_lda <- MASS::lda(Site ~ SiO2 + Al2O3 + FeO + MgO + CaO, glass)
# bestow 'tbl_ord' class & augment observation, centroid, and variable fields
as_tbl_ord(glass_lda) %>%
  augment_ord() %>%
  print() -> glass_lda
#> # A tbl_ord of class 'lda': (72 x 3) x (5 x 3)'
#> # 3 coordinates: LD1, LD2, LD3
#> # 
#> # Rows (principal): [ 72 x 3 | 5 ]
#>     LD1    LD2      LD3 |   name         prior counts grouping
#>                         |   <chr>        <dbl>  <int> <chr>   
#> 1  1.82 -1.21  -0.672   | 1 Apollonia    0.132      9 Apollon…
#> 2 -5.62  0.364 -0.00398 | 2 Banias       0.265     18 Banias  
#> 3  2.47  1.63   0.0833  | 3 Bet Eli'ez…  0.397     27 Bet Eli…
#> 4  1.31 -2.84   0.277   | 4 Dor          0.206     14 Dor     
#> 5  2.99  2.03  -1.22    | 5 1           NA         NA Bet Eli…
#> # ℹ 67 more rows | # ℹ 67 more rows
#> # ℹ 1 more variable: .element <chr>
#> # 
#> # Columns (standard): [ 5 x 3 | 2 ]
#>        LD1    LD2    LD3 |   name  .element
#>                          |   <chr> <chr>   
#> 1  0.00681 0.618   0.468 | 1 SiO2  active  
#> 2  2.05    1.04   -0.660 | 2 Al2O3 active  
#> 3 -1.93    0.165   2.44  | 3 FeO   active  
#> 4 -1.76    1.82   -0.599 | 4 MgO   active  
#> 5 -0.275   0.0942  1.42  | 5 CaO   active  
# row-standard biplot
glass_lda %>%
  confer_inertia(1) %>%
  ggbiplot(aes(shape = grouping)) +
  theme_bw() + theme_biplot() +
  geom_rows_point(size = 4) +
  geom_rows_point(elements = "score") +
  stat_cols_rule(
    aes(label = name), color = "#888888", num = 8L,
    ref_elements = "score", fun.offset = function(x) minabspp(x, p = .1),
    text.size = 2.5, label_dodge = .04
  ) +
  scale_shape_manual(values = c(2L, 3L, 0L, 5L)) +
  ggtitle(
    "LDA of Freestone glass measurements",
    "Row-standard biplot of standardized LDA"
  )

# contribution LDA of sites on measurements
glass_lda <-
  lda_ord(Site ~ SiO2 + Al2O3 + FeO + MgO + CaO, glass,
          axes.scale = "contribution")
# bestow 'tbl_ord' class & augment observation, centroid, and variable fields
as_tbl_ord(glass_lda) %>%
  augment_ord() %>%
  print() -> glass_lda
#> # A tbl_ord of class 'lda_ord': (72 x 3) x (5 x 3)'
#> # 3 coordinates: LD1, LD2, LD3
#> # 
#> # Rows (principal): [ 72 x 3 | 5 ]
#>     LD1    LD2      LD3 |   name         prior counts grouping
#>                         |   <chr>        <dbl>  <int> <chr>   
#> 1  1.82 -1.21  -0.672   | 1 Apollonia    0.132      9 Apollon…
#> 2 -5.62  0.364 -0.00398 | 2 Banias       0.265     18 Banias  
#> 3  2.47  1.63   0.0833  | 3 Bet Eli'ez…  0.397     27 Bet Eli…
#> 4  1.31 -2.84   0.277   | 4 Dor          0.206     14 Dor     
#> 5  2.99  2.03  -1.22    | 5 1           NA         NA Bet Eli…
#> # ℹ 67 more rows | # ℹ 67 more rows
#> # ℹ 1 more variable: .element <chr>
#> # 
#> # Columns (standard): [ 5 x 3 | 2 ]
#>       LD1    LD2     LD3 |   name  .element
#>                          |   <chr> <chr>   
#> 1  0.166   0.871  0.160  | 1 SiO2  active  
#> 2  0.663   0.164  0.0442 | 2 Al2O3 active  
#> 3 -0.142   0.112  0.192  | 3 FeO   active  
#> 4 -0.680   0.367 -0.0694 | 4 MgO   active  
#> 5 -0.0804 -0.151  0.941  | 5 CaO   active  
# symmetric biplot
glass_lda %>%
  confer_inertia(.5) %>%
  ggbiplot(aes(shape = grouping)) +
  theme_bw() + theme_biplot() +
  geom_rows_point() +
  stat_rows_density_2d(elements = "score", alpha = .5, color = "#444444") +
  stat_cols_rule(
    aes(label = name), geom = "axis", color = "#888888", num = 8L,
    ref_elements = "active", fun.offset = function(x) minabspp(x, p = .1),
    label_dodge = 0.04, text.size = 2.5, text_dodge = .025
  ) +
  scale_shape_manual(values = c(16L, 17L, 15L, 18L)) +
  ggtitle(
    "LDA of Freestone glass measurements",
    "Symmetric biplot of contribution LDA"
  )


if (FALSE) { # \dontrun{
# classical multidimensional scaling of road distances between European cities
euro_mds <- ordinate(eurodist, cmdscale_ord, k = 11)
# monoplot of city locations
euro_plot <- euro_mds %>%
  negate_ord("PCo2") %>%
  ggbiplot() +
  geom_cols_text(aes(label = name), size = 3)
print(euro_plot)
# biplot with minimal spanning tree based on plotting window distances
euro_plot +
  stat_cols_spantree(
    engine = "mlpack",
    alpha = .5, linetype = "dotted"
  )
# biplot with minimal spanning tree based on full-dimensional distances
euro_plot +
  stat_cols_spantree(
    ord_aes(euro_mds), engine = "mlpack",
    alpha = .5, linetype = "dotted"
  )
} # }