This vignette covers regression biplots constructed with the newer
bipl5 workflow:
init_biplot(...) |> scale_mds(type = "regress", Z = ...)The mathematics mirror the current wrap_bipl5.regress()
documentation, but the examples use init_biplot() and
scale_mds() because this is now the most direct way to
build a regression biplot inside the package.
Regression biplots differ from PCA biplots in one crucial way: the
sample map is supplied from outside. The display coordinates in
Z are treated as fixed, and the variable axes are then
fitted to that map by least squares.
Building a regression biplot
As in the PCA vignette, we keep an extra categorical column in the
original data so we can later demonstrate format_samples()
with a second sample stratification.
iris2 <- iris
iris2$Band <- factor(
rep(c("class1", "class2", "class3", "class4"), length.out = nrow(iris2))
)
Z <- prcomp(iris2[, 1:4], scale. = TRUE)$x[, 1:2]
reg_spec <- init_biplot(iris2, scale = TRUE)
bp_reg <- reg_spec |>
scale_mds(
type = "regress",
Z = Z,
group_aes = iris2$Species,
show_group_means = TRUE
)After scale_mds(type = "regress", ...), the object
contains one fixed display only:
bp_reg
#> bipl5_biplot [REG]
#> └── mdsDisplay_12 [Dim 1 & 2] <bipl5_mdsDisplay>
#> ├── Data <bipl5_data>
#> │ ├── sample_coordinates [150 x 2]
#> │ ├── axes_coordinates [4 axes]
#> │ └── translated_axes_coordinates
#> ├── trace_data [34 traces]
#> └── annotations [72 items]That single display is always stored as mdsDisplay_12
and labelled Dim 1 & 2, because the geometry comes from
the supplied two-column map instead of an internally generated sequence
of principal components.
plot(bp_reg)Formatting samples
format_samples() works for regression biplots in the
same way that it works for PCA biplots: the fitted regression geometry
is left untouched, and only the sample-trace block is rebuilt.
First we colour the samples by species:
bp_reg_species <- bp_reg |>
format_samples(
stratify = "col",
by = Species,
col = c("tomato", "steelblue", "darkgreen")
)Then we add a second categorical stratification by plotting symbol:
bp_reg_dual <- bp_reg_species |>
format_samples(
stratify = "symbol",
by = Band,
pch = c(15, 16, 17, 18)
)The resulting widget now has separate sample legend sections for
Species and Band, while the hidden observation
traces are the observed Species x Band combinations.
plot(bp_reg_dual)How regression biplots differ from PCA biplots
Regression biplots are intentionally simpler in the current package:
- they have one fixed
mdsDisplayonly -
append_mdsDisplay()andremove_mdsDisplay()are not supported - they do not carry the PCA
fit_measuresbranch - there is no PCA-style right-hand fit panel
- the fitted variable axes are tied to the supplied map
Z
The main quality label in the widget still reports the overall regression-biplot quality and its ordered dimension-specific contributions, but the multi-display PCA fit machinery is absent by design.
Mathematical background
This section preserves the long-form regression-biplot documentation in a vignette-style narrative.
Let \(\mathbf{X} \in \mathbb{R}^{n \times p}\) denote the processed data matrix after centring and optional scaling, and let \(\mathbf{Z} \in \mathbb{R}^{n \times 2}\) denote the externally supplied display coordinates. In a regression biplot the sample map is taken as given, and the variable axes are fitted to that map by least squares.
The fitted model is
\[ \mathbf{X} = \mathbf{Z}\mathbf{H}^{\top} + \mathbf{E}, \]
with
\[ \mathbf{H}^{\top} = (\mathbf{Z}^{\top}\mathbf{Z})^{-1}\mathbf{Z}^{\top}\mathbf{X} \]
when \(\mathbf{Z}\) has full column rank. Equivalently,
\[ \widehat{\mathbf{X}} = \mathbf{Z}\mathbf{H}^{\top} = \mathbf{Z}(\mathbf{Z}^{\top}\mathbf{Z})^{-1}\mathbf{Z}^{\top}\mathbf{X} = \mathbf{P}_Z \mathbf{X}, \]
where \(\mathbf{P}_Z\) is the orthogonal projector onto the column space of \(\mathbf{Z}\).
If \(\mathbf{h}_{(j)}\) is the fitted direction for variable \(j\), then the predicted value for sample \(i\) on that variable is
\[ \widehat{x}_{ij} = \mathbf{z}_i^{\top}\mathbf{h}_{(j)}. \]
The point on the calibrated axis corresponding to marker value \(\mu\) is
\[ \mathbf{p}_{\mu j} = \frac{\mu}{\mathbf{h}_{(j)}^{\top}\mathbf{h}_{(j)}}\mathbf{h}_{(j)}. \]
So, just as in the PCA case, direct readings from the axis coincide with the fitted values from the display.
Because \(\widehat{\mathbf{X}} = \mathbf{P}_Z \mathbf{X}\) is an orthogonal projection, the regression biplot admits a natural variable-side predictivity measure. For variable \(j\),
\[ \phi_j = \frac{\|\widehat{\mathbf{x}}_{(j)}\|^2} {\|\mathbf{x}_{(j)}\|^2} = 1 - \frac{\|\mathbf{x}_{(j)} - \widehat{\mathbf{x}}_{(j)}\|^2} {\|\mathbf{x}_{(j)}\|^2}. \]
This is the proportion of variable \(j\)’s sum of squares reproduced by the display. It is also the ordinary multiple-regression \(R^2\) obtained by regressing that variable on the supplied coordinates in \(\mathbf{Z}\).
The overall regression-biplot quality is
\[ R^2_{\mathrm{disp}} = \frac{\|\widehat{\mathbf{X}}\|_F^2}{\|\mathbf{X}\|_F^2} = 1 - \frac{\|\mathbf{X} - \widehat{\mathbf{X}}\|_F^2}{\|\mathbf{X}\|_F^2}. \]
Because the column-wise decomposition is orthogonal, the same quantity can be written as a weighted average of the variable predictivities:
\[ R^2_{\mathrm{disp}} = \sum_{j = 1}^p w_j \phi_j, \qquad w_j = \frac{\|\mathbf{x}_{(j)}\|^2}{\|\mathbf{X}\|_F^2}. \]
If the processed variables all have equal sums of squares, the overall quality is simply the average of the variable predictivities.
To separate the quality into contributions from the two displayed dimensions, the package uses an ordered orthogonalization of the supplied coordinates. Let
\[ \mathbf{u}_1 = \mathbf{z}_1, \qquad \mathbf{q}_1 = \frac{\mathbf{u}_1}{\|\mathbf{u}_1\|}, \]
and then let
\[ \mathbf{u}_2 = \mathbf{z}_2 - \mathbf{q}_1 \mathbf{q}_1^{\top}\mathbf{z}_2, \qquad \mathbf{q}_2 = \frac{\mathbf{u}_2}{\|\mathbf{u}_2\|}. \]
Equivalently, this is the QR orthogonalization of \(\mathbf{Z}\) while preserving the supplied column order. The display projector can then be written as
\[ \mathbf{P}_Z = \mathbf{Q}\mathbf{Q}^{\top}, \qquad \mathbf{Q} = [\mathbf{q}_1 \ \mathbf{q}_2]. \]
Hence
\[ \widehat{\mathbf{X}} = \mathbf{q}_1 \mathbf{q}_1^{\top}\mathbf{X} + \mathbf{q}_2 \mathbf{q}_2^{\top}\mathbf{X}, \]
and the two pieces are orthogonal. This yields the ordered contributions
\[ R_1^2 = \frac{\|\mathbf{q}_1 \mathbf{q}_1^{\top}\mathbf{X}\|_F^2}{\|\mathbf{X}\|_F^2}, \qquad R_{2 \mid 1}^2 = \frac{\|\mathbf{q}_2 \mathbf{q}_2^{\top}\mathbf{X}\|_F^2}{\|\mathbf{X}\|_F^2}, \]
with
\[ R^2_{\mathrm{disp}} = R_1^2 + R_{2 \mid 1}^2. \]
This decomposition is ordered. If the original columns of \(\mathbf{Z}\) are not orthogonal, then \(R_{2 \mid 1}^2\) is the additional contribution of the second supplied dimension after removing overlap with the first. It should not be interpreted as the contribution of the raw second column in isolation.
The same idea gives an ordered decomposition of each variable predictivity:
\[ \phi_j = \phi_{j1} + \phi_{j, 2 \mid 1}. \]
This is a useful way to understand which part of the supplied map is doing the predictive work for each variable.
The documentation also preserves the distinction between predictivity and direct-reading diagnostics. If \(s_j\) is the standard deviation used in preprocessing, the pointwise direct-reading error is
\[ \delta_{ij} = \frac{|x_{ij} - \widehat{x}_{ij}|}{s_j}, \]
and the mean direct-reading error for axis \(j\) is
\[ \bar{\delta}_j = \frac{1}{n}\sum_{i = 1}^n \delta_{ij}. \]
These are display-specific diagnostics. They measure how accurately a user can read values directly from the currently drawn calibrated axis. They are not the same thing as the variance-accounted-for quantities \(\phi_j\) and \(R^2_{\mathrm{disp}}\).
One final distinction from PCA is especially important: a regression biplot does not, in general, satisfy the sample-side orthogonality that would justify PCA-style sample predictivities. So the principled sum-of-squares fit measures here are the variable predictivities, the overall display quality, and the ordered dimension-specific contributions derived from the supplied map.
References
Alves, M. R. (2012). Evaluation of the predictive power of biplot axes to automate the construction and layout of biplots based on the accuracy of direct readings from common outputs of multivariate analyses: application to principal component analysis. Journal of Chemometrics, 26(5), 180-190.
Gabriel, K. R. (1971). The biplot graphical display of matrices with application to principal component analysis. Biometrika, 58(3), 453-467.
Gower, J. C. and Hand, D. J. (1996). Biplots. London: Chapman and Hall.
Gower, J. C., Lubbe, S. and le Roux, N. J. (2011). Understanding Biplots. Chichester: Wiley.
Greenacre, M. (2010). Biplots in Practice. Bilbao: BBVA Foundation.
la Grange, A., le Roux, N. and Gardner-Lubbe, S. (2009). BiplotGUI: Interactive Biplots in R. Journal of Statistical Software, 30(12), 1-37.