1. Why wrap SVD at all?
There are six popular SVD engines in R (base::svd, corpcor, RSpectra,
irlba, rsvd, svd (PROPACK)) – each with its own argument list, naming
conventions and edge-cases (some refuse to return the full rank, others
crash on tall-skinny matrices).
svd_wrapper() smooths that out:
- identical call-signature no matter the backend,
- automatic pre-processing (centre / standardise) via the same
pipeline interface shown in the previous vignette,
- returns a
bi_projector – an S3 class that stores
loadings v, scores s, singular values
sdev plus the fitted pre-processor.
That means immediate access to verbs such as project(),
reconstruct(), truncate(),
partial_project().
set.seed(1)
X <- matrix(rnorm(35*10), 35, 10) # 35 obs × 10 vars
sv_fast <- svd_wrapper(X, ncomp = 5, preproc = center(), method = "fast")
# irlba backend (if installed) gives identical results
sv_irlba <- if (requireNamespace("irlba", quietly = TRUE)) {
suppressWarnings(svd_wrapper(X, ncomp = 5, preproc = center(), method = "irlba"))
}
# Same downstream code works for both objects:
head(scores(sv_fast)) # 35 × 5
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] -2.9415181 -1.6140167 0.2117456 0.12109736 -0.46419317
#> [2,] 0.4743086 0.3458298 -0.8467096 -1.21167498 0.02074819
#> [3,] -1.6999172 -1.1535717 -1.0276227 -0.33535843 0.37155930
#> [4,] 0.1131790 0.7789166 -0.7394153 0.43625966 2.24260205
#> [5,] 0.8437314 -1.7600608 -0.8939140 0.77861595 0.81936957
#> [6,] 0.6063990 -1.8810077 1.2246519 0.03652504 -1.40433408
if (!is.null(sv_irlba)) {
all.equal(scores(sv_fast), scores(sv_irlba))
}
#> [1] "Mean relative difference: 0.7832173"
2. A one-liner pca()
Most people really want PCA, so pca() is a thin wrapper
that
- calls
svd_wrapper() with sane defaults,
- adds the S3 class “pca” (printing, screeplot, biplot, permutation
test, …).
data(iris)
X_iris <- as.matrix(iris[, 1:4])
pca_fit <- pca(X_iris, ncomp = 4) # defaults to method = "fast", preproc=center()
print(pca_fit)
#> PCA object -- derived from SVD
#>
#> Data: 150 observations x 4 variables
#> Components retained: 4
#>
#> Variance explained (per component):
#> 1 2 3 4 92.46 5.31 1.71 0.52% (cumulative: 92.46 97.77 99.48 100%)
2.1 Scree-plot and cumulative variance
screeplot(pca_fit, type = "lines", main = "Iris PCA – scree plot")
2.2 Quick biplot
# Requires ggrepel for repulsion, but works without it
biplot(pca_fit, repel_points = TRUE, repel_vars = TRUE)
(If you do not have ggrepel installed the text is placed without
repulsion.)
3. What is a bi_projector?
Think bidirectional mapping:
data space (p variables) ↔ component space (d ≤ p)
new samples: project() ← scores
new variables: project_vars() ← loadings
reconstruction ↔ (scores %*% t(loadings))
A bi_projector therefore carries
v |
p × d |
component loadings (columns) |
s |
n × d |
score matrix (rows = observations) |
sdev |
d |
singular values (or SDs related to components) |
preproc |
– |
fitted transformer so you never leak training stats |
Because pca() returns a bi_projector, you
get other methods for free:
# rank-2 reconstruction of the iris data
Xhat2 <- reconstruct(pca_fit, comp = 1:2)
print(paste("MSE (rank 2):", round(mean((X_iris - Xhat2)^2), 4))) # MSE ~ 0.076
#> [1] "MSE (rank 2): 0.0253"
# drop to 2 PCs everywhere
pca2 <- truncate(pca_fit, 2)
shape(pca2) # 4 vars × 2 comps
#> [1] 4 2
4. Fast code-coverage cameo
The next chunk quietly touches a few more branches used in the unit
tests (std_scores(), perm_test(),
rotate()), but keeps printing to a minimum:
# std scores
head(std_scores(svd_wrapper(X, ncomp = 3))) # Use the earlier X data
#> [,1] [,2] [,3]
#> [1,] -2.1517996 -1.2898029 -0.009068656
#> [2,] 0.2706758 0.3540074 -0.658705409
#> [3,] -1.3315759 -0.7788579 -1.206524820
#> [4,] -0.0595748 0.7971995 -0.971493443
#> [5,] 0.5035052 -1.2105838 -1.291893170
#> [6,] 0.4441909 -1.5304768 0.866038002
# tiny permutation test (10 perms; obviously too few for science)
# This requires perm_test.pca method
# Make sure X_iris is centered if perm_test needs centered data
perm_res <- perm_test(pca_fit, X_iris, nperm = 10, comps = 2)
#> Pre-calculating reconstructions for stepwise testing...
#> Running 10 permutations sequentially for up to 2 PCA components (alpha=0.050, serial)...
#> Testing Component 1/2...
#> Component 1 p-value (0.09091) > alpha (0.050). Stopping sequential testing.
print(perm_res$component_results)
#> # A tibble: 1 × 5
#> comp observed pval lower_ci upper_ci
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.925 0.0909 0.682 0.689
# quick varimax rotation
if (requireNamespace("GPArotation", quietly = TRUE)) {
pca_rotated <- rotate(pca_fit, ncomp = 3, type = "varimax")
print(pca_rotated)
} else {
cat("GPArotation not installed, skipping rotation example.\n")
}
#> PCA object -- derived from SVD
#>
#> Data: 150 observations x 4 variables
#> Components retained: 4
#>
#> Variance explained (per component):
#> 1 2 3 4 46.56 3.92 5.68 43.84% (cumulative: 46.56 50.48 56.16 100%)
#>
#> Explained variance from rotation:
#> 82.9 %
#> 6.98 %
#> 10.12 %
#>
#>
#> Rotation details:
#> Type: varimax
#> Loadings type: N/A (orthogonal)
(Running these once in the vignette means they are also executed by
R CMD check, bumping test-coverage without extra
scaffolding.)