Package 'gcpca'

Extract gcPCA loadings

Description

Extract gcPCA loadings

Usage

## S3 method for class 'gcPCA'
coef(object, ...)

## S3 method for class 'sparse_gcPCA'
coef(object, ...)

Arguments

object	A fitted "gcPCA" or "sparse_gcPCA" object.
...	Unused.

Value

For dense models, a loadings matrix. For sparse models, a named list of loading matrices (one per lasso penalty).

---

Return fitted training scores

Description

Return fitted training scores

Usage

## S3 method for class 'gcPCA'
fitted(object, ...)

Arguments

object	A fitted "gcPCA" or "sparse_gcPCA" object.
...	Passed to predict().

Value

A list with Ra_scores and Rb_scores.

---

Fit a generalized contrastive PCA model

Description

gcPCA() fits dense generalized contrastive principal components from two data matrices (Ra and Rb) measured on the same feature set.

Usage

gcPCA(
  Ra,
  Rb,
  method = "v4",
  Ncalc = NULL,
  Nshuffle = 0,
  normalize_flag = TRUE,
  alpha = 1,
  alpha_null = 0.975,
  cond_number = 10^13
)

Arguments

Ra	Matrix for condition A (⁠n_a x p⁠).
Rb	Matrix for condition B (⁠n_b x p⁠).
method	Character scalar selecting optimization variant.
Ncalc	Optional number of gcPCs for orthogonal variants (⁠*.1⁠).
Nshuffle	Number of shuffles used to estimate a null distribution.
normalize_flag	Logical; if TRUE, z-score and L2-normalize columns.
alpha	Contrastive coefficient used by method "v1".
alpha_null	Quantile for null thresholding (reserved in current code).
cond_number	Maximum allowed denominator condition number.

Value

An object of class "gcPCA" with loadings, scores, objective values, and fit metadata.

Examples

set.seed(1)
Ra <- matrix(rnorm(40 * 5), ncol = 5)
Rb <- matrix(rnorm(35 * 5), ncol = 5)
fit <- gcPCA(Ra, Rb, method = "v4", Ncalc = 3)
pred <- predict(fit, Ra = Ra)
str(pred$Ra_scores)

---

Get gcPCA loadings

Description

Convenience wrapper around coef() for "gcPCA" and "sparse_gcPCA" objects.

Usage

loadings(object, ...)

Arguments

object	A fitted model object.
...	Extra arguments passed to coef().

Value

A loading matrix or a named list of loading matrices.

---

Plot gcPCA scores

Description

Plot gcPCA scores

Usage

## S3 method for class 'gcPCA'
plot(x, which = c(1, 2), ...)

Arguments

x	A fitted "gcPCA" or "sparse_gcPCA" object.
which	Integer vector of length 2 indicating components to plot.
...	Extra graphical parameters passed to plot().

Value

x, invisibly.

---

Project data with a fitted gcPCA model

Description

Project data with a fitted gcPCA model

Usage

## S3 method for class 'gcPCA'
predict(object, Ra = NULL, Rb = NULL, ...)

## S3 method for class 'sparse_gcPCA'
predict(object, Ra = NULL, Rb = NULL, ...)

Arguments

object	A fitted "gcPCA" or "sparse_gcPCA" object.
Ra	Optional matrix for condition A.
Rb	Optional matrix for condition B.
...	Unused.

Value

For dense models, a list of score matrices. For sparse models, a list of score-matrix lists, one per penalty value.

---

Print a gcPCA model

Description

Print a gcPCA model

Usage

## S3 method for class 'gcPCA'
print(x, ...)

Arguments

x	A fitted "gcPCA" or "sparse_gcPCA" object.
...	Unused.

Value

x, invisibly.

---

Print a gcPCA summary

Description

Print a gcPCA summary

Usage

## S3 method for class 'summary.gcPCA'
print(x, ...)

Arguments

x	A "summary.gcPCA" object from summary.gcPCA().
...	Unused.

Value

x, invisibly.

---

Get gcPCA scores

Description

Convenience wrapper around predict() for "gcPCA" and "sparse_gcPCA" objects.

Usage

scores(object, newdata = NULL, ...)

Arguments

object	A fitted model object.
newdata	Optional matrix (treated as Ra) or list with Ra/Rb.
...	Extra arguments passed to predict().

Value

A list of projected scores.

---

Fit a sparse generalized contrastive PCA model

Description

sparse_gcPCA() fits sparse gcPCA loadings over a path of lasso penalties.

Usage

sparse_gcPCA(
  Ra,
  Rb,
  method = "v4",
  Ncalc = NULL,
  normalize_flag = TRUE,
  Nsparse = NULL,
  Nshuffle = 0,
  lasso_penalty = exp(seq(log(0.01), log(1), length.out = 10)),
  ridge_penalty = 0,
  alpha = 1,
  alpha_null = 0.975,
  tol = 1e-05,
  max_steps = 1000,
  cond_number = 1e+13
)

Arguments

Ra	Matrix for condition A (⁠n_a x p⁠).
Rb	Matrix for condition B (⁠n_b x p⁠).
method	Character scalar selecting optimization variant.
Ncalc	Optional number of gcPCs for orthogonal variants (⁠*.1⁠).
normalize_flag	Logical; if TRUE, z-score and L2-normalize columns.
Nsparse	Number of sparse gcPCs to estimate.
Nshuffle	Number of shuffles passed to the dense gcPCA stage.
lasso_penalty	Numeric vector of lasso penalties.
ridge_penalty	Numeric ridge penalty.
alpha	Contrastive coefficient used by method "v1".
alpha_null	Quantile for null thresholding (reserved in current code).
tol	Convergence tolerance for sparse optimization.
max_steps	Maximum number of sparse optimization iterations.
cond_number	Maximum allowed denominator condition number.

Value

An object of class c("sparse_gcPCA", "gcPCA") containing sparse loadings and training projections across penalties.

Examples

set.seed(1)
Ra <- matrix(rnorm(50 * 6), ncol = 6)
Rb <- matrix(rnorm(45 * 6), ncol = 6)
fit <- sparse_gcPCA(Ra, Rb, method = "v4", Nsparse = 2,
                    lasso_penalty = c(0.05, 0.1))
pred <- predict(fit, Ra = Ra)
str(pred$Ra_scores)

---

Summarize a gcPCA model

Description

Summarize a gcPCA model

Usage

## S3 method for class 'gcPCA'
summary(object, ...)

Arguments

object	A fitted "gcPCA" or "sparse_gcPCA" object.
...	Unused.

Value

A "summary.gcPCA" list with eigenvalues, standard deviations, explained variance summaries, and tuning settings.

---