Package 'RmlxStats'

Title: MLX-Accelerated Statistical Models
Description: Fast statistical routines on Apple Silicon using the Rmlx package.
Authors: David Hugh-Jones [aut, cre], R Core Team [ctb], Trevor Hastie [ctb], Jerome Friedman [ctb], Rob Tibshirani [ctb], Balasubramanian Narasimhan [ctb], Kenneth Tay [ctb], Noah Simon [ctb], James Yang [ctb], Achim Zeileis [ctb]
Maintainer: David Hugh-Jones <[email protected]>
License: GPL (>= 2)
Version: 0.3.0
Built: 2026-05-27 11:02:04 UTC
Source: https://github.com/hughjonesd/RmlxStats

Help Index

Re-export generics

Description

These generics are re-exported from the generics package for convenience.

Usage

tidy(x, ...)

glance(x, ...)

augment(x, ...)

Arguments

x, ...

Passed to the generic.

MLX-friendly binomial family

Description

Construct a binomial GLM family whose core link and deviance helpers are implemented in R so they work with MLX arrays as well as base R vectors. This avoids calling into compiled C routines that only handle base types.

Usage

mlxs_binomial(link = "logit")

Arguments

link

a specification for the model link function. This can be a name/expression, a literal character string, a length-one character vector, or an object of class "link-glm" (such as generated by make.link) provided it is not specified via one of the standard names given next.

The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log, identity and cloglog (complementary log-log); the Gamma family the links inverse, identity and log; the poisson family the links log, identity, and sqrt; and the inverse.gaussian family the links 1/mu^2, inverse, identity and log.

The quasi family accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt, and the function power can be used to create a power link function.

Details

Currently the logit, log, cloglog, and cauchit links are supported. For other link specifications, fall back to stats::binomial().

Value

A family object compatible with stats::glm() and mlxs_glm().

Bootstrap MLX arrays along the first dimension

Description

mlxs_boot() resamples observations from one or more MLX arrays, calls a user-supplied function on each resampled batch, and returns the collected results. Every argument supplied via ... must share the same size in its first dimension (number of observations). Arguments that do not need resampling should be captured in the environment of fun instead of being passed through ....

Usage

mlxs_boot(fun, ..., B = 200L, seed = NULL, progress = FALSE, compile = FALSE)

Arguments

fun

Function called on each bootstrap draw. It must accept the same named arguments as supplied through ....
...

Arrays, matrices, or vectors that should be resampled along the first dimension before being passed to fun.
B

Number of bootstrap iterations.
seed

Optional integer seed for reproducibility.
progress

Logical; if TRUE, show a text progress bar.
compile

Logical; compile fun once via Rmlx::mlx_compile() before entering the resampling loop. Defaults to FALSE.

Value

A list with elements samples (the raw results from fun), B, and seed.

Cross-validated MLX elastic net regression

Description

Cross-validation wrapper around mlxs_glmnet() that mirrors the core glmnet::cv.glmnet() workflow for the families currently supported by mlxs_glmnet().

Usage

mlxs_cv_glmnet(
  x,
  y,
  weights = NULL,
  offset = NULL,
  lambda = NULL,
  type.measure = c("default", "mse", "deviance", "class", "mae", "auc", "C"),
  nfolds = 10,
  foldid = NULL,
  alignment = c("lambda", "fraction"),
  grouped = TRUE,
  keep = FALSE,
  parallel = FALSE,
  gamma = c(0, 0.25, 0.5, 0.75, 1),
  relax = FALSE,
  trace.it = 0,
  family = mlxs_gaussian(),
  ...
)

Arguments

x

Numeric matrix of predictors (observations in rows).
y

Numeric response vector.
weights

Optional observation weights. Currently unsupported.
offset

Optional offset. Currently unsupported.
lambda

Optional decreasing lambda sequence. If NULL, the full-data fit chooses the path and the same path is reused inside each fold.
type.measure

Loss used to score the holdout predictions.
nfolds

Number of folds.
foldid

Optional integer vector giving the fold assignment for each observation.
alignment

Alignment mode. Only "lambda" is currently supported.
grouped

Should cross-validation be aggregated fold-by-fold? Only TRUE is currently supported.
keep

Should out-of-fold predictions be stored?
parallel

Logical. Parallel refits are currently unsupported.
gamma, relax

Relaxed fits are currently unsupported.
trace.it

Progress tracing. Currently unsupported.
family

MLX-aware family object, e.g. mlxs_gaussian() or mlxs_binomial().
...

Additional arguments passed to mlxs_glmnet(), such as alpha, nlambda, lambda_min_ratio, standardize, intercept, maxit, and tol.

Details

The full-data fit defines a master lambda path. Each fold is then refit on the same lambda values and scored on its holdout set.

Current limitations relative to glmnet::cv.glmnet():

  • only Gaussian and binomial families are supported

  • weights, offset, alignment != "lambda", grouped = FALSE, parallel = TRUE, relax = TRUE, and non-zero trace.it are not implemented

  • type.measure = "auc" and type.measure = "C" are not implemented

Value

An object of class mlxs_cv_glmnet.

MLX-friendly Gaussian family

Description

MLX-friendly Gaussian family

Usage

mlxs_gaussian(link = "identity")

Arguments

link

a specification for the model link function. This can be a name/expression, a literal character string, a length-one character vector, or an object of class "link-glm" (such as generated by make.link) provided it is not specified via one of the standard names given next.

The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log, identity and cloglog (complementary log-log); the Gamma family the links inverse, identity and log; the poisson family the links log, identity, and sqrt; and the inverse.gaussian family the links 1/mu^2, inverse, identity and log.

The quasi family accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt, and the function power can be used to create a power link function.

Value

A family object compatible with mlxs_glm().

MLX-backed generalized linear model

Description

Fit generalized linear models using iterative reweighted least squares (IRLS) with MLX providing the heavy lifting for weighted least squares solves. Final convergence is done at double precision on the cpu.

Usage

mlxs_glm(
  formula,
  family = mlxs_gaussian(),
  data,
  subset,
  weights,
  na.action = stats::na.exclude,
  start = NULL,
  control = list(),
  ...
)

Arguments

formula

Model formula.
family

A mlxs family object (e.g., mlxs_gaussian(), mlxs_binomial(), mlxs_poisson()). You can use "gaussian" etc.
data

Optional data frame, tibble, or environment containing the variables in the model.
subset

Optional expression for subsetting observations.
weights

Optional non-negative observation weights.
na.action

How to handle missing values.
start

Starting values for the parameters in the linear predictor.
control

Optional list of control parameters passed to mlxs_glm_control(). Control parameters can include epsilon, epsilon_f64, maxit, trace, and rank_tol.
...

Additional arguments passed to the family function when family is supplied as a function or string.

Value

An object of class c("mlxs_glm", "mlxs_model") containing elements similar to the result of stats::glm(). Unlike stats::glm(), rank-deficient model matrices are rejected rather than fit with aliased coefficients.

Examples

fit <- mlxs_glm(mpg ~ cyl + disp, family = mlxs_gaussian(), data = mtcars)
coef(fit)

Control parameters

Description

Control parameters

Usage

mlxs_glm_control(
  epsilon = 1e-08,
  epsilon_f64 = 1e-06,
  maxit = 25,
  trace = FALSE,
  rank_tol = NULL
)

Arguments

epsilon

Convergence tolerance parameter, interpreted as in stats::glm.control(). Iterations converge when abs(deviance - deviance_old)/(abs(deviance) + 0.1) < epsilon.
epsilon_f64

Move operations to float64 on the cpu when convergence is this close (using the same expression as above). Doing this allows more precision but slows computation.
maxit

Maximum number of IWLS iterations.
trace

Logical: trace each iteration?
rank_tol

Optional relative tolerance used to detect rank-deficient systems. NULL uses the package default, which varies by dtype and is 1e-6 for float32 matrices. Set to FALSE to skip rank checks entirely. Note that higher numbers indicate lower tolerance.

Value

A list with default values filled in.

MLX-backed elastic net regression

Description

Fit lasso or elastic-net penalised regression paths using MLX arrays for the heavy linear algebra. Dense Gaussian and binomial paths stay on the MLX backend throughout the iterative updates, with repeated chunk updates traced through Rmlx::mlx_compile() to reduce host overhead.

Usage

mlxs_glmnet(
  x,
  y,
  family = mlxs_gaussian(),
  alpha = 1,
  lambda = NULL,
  nlambda = 100,
  lambda_min_ratio = 1e-04,
  standardize = TRUE,
  intercept = TRUE,
  use_strong_rules = TRUE,
  maxit = 1000,
  tol = 1e-06
)

Arguments

x

Numeric R or mlx matrix of predictors.
y

Numeric R or mlx response vector.
family

MLX-aware family object, e.g. mlxs_gaussian() or mlxs_binomial().
alpha

Elastic-net mixing parameter (1 = lasso, currently alpha must be strictly positive).
lambda

Optional decreasing sequence of penalty values. If NULL, a sequence of length nlambda is generated from lambda_max down to lambda_max * lambda_min_ratio.
nlambda

Length of automatically generated lambda path.
lambda_min_ratio

Smallest lambda as a fraction of lambda_max.
standardize

Should columns of x be scaled before fitting?
intercept

Should an intercept be fit?
use_strong_rules

Retained for API compatibility. The dense MLX solver keeps all coefficients on device, so this flag currently does not change the computation.
maxit

Maximum proximal-gradient iterations per lambda value.
tol

Convergence tolerance on the coefficient updates.

Value

An object of class mlxs_glmnet containing the fitted coefficient path, intercepts, lambda sequence, and scaling information. The beta and a0 components are stored as MLX arrays so downstream methods can keep path computations on the MLX backend. Use coef() or predict() for ordinary base-R matrix outputs.

Note

glmnet::glmnet() is faster on smaller problems. Very roughly as of April 2026, mlxs_glmnet() gets competitive at n x p = 5,000,000 or greater.

MLX-backed linear regression

Description

Fit a linear model via QR decomposition using MLX arrays on Apple Silicon devices. The interface mirrors stats::lm() for the common arguments.

Usage

mlxs_lm(
  formula,
  data,
  subset,
  weights,
  na.action = stats::na.exclude,
  rank_tol = NULL
)

Arguments

formula

Model formula.
data

Optional data frame, tibble, or environment containing the variables in the model.
subset

Optional expression for subsetting observations.
weights

Optional non-negative observation weights.
na.action

How to handle missing values.
rank_tol

Optional relative tolerance used to detect rank-deficient systems. NULL uses the package default, which varies by dtype and is 1e-6 for float32 matrices. Set to FALSE to skip rank checks entirely. Note that higher numbers indicate lower tolerance.

Value

An object of class c("mlxs_lm", "mlxs_model") containing components similar to an "lm" fit, along with MLX intermediates stored in the mlx element. Note that MLX currently operates in single precision, so fitted values and diagnostics may differ from stats::lm() at around the 1e-6 level. Unlike stats::lm(), rank-deficient model matrices are rejected rather than fit with aliased coefficients.

Examples

fit <- mlxs_lm(mpg ~ cyl + disp, data = mtcars)
coef(fit)

Fit an MLX linear model from design matrices

Description

mlxs_lm_fit() powers mlxs_lm() by wrapping the QR-based solver that runs entirely on MLX arrays.

Usage

mlxs_lm_fit(x, y, weights = NULL, rank_tol = NULL)

Arguments

x

MLX design matrix (or object coercible via Rmlx::as_mlx()) whose rows represent observations and columns represent predictors.
y

MLX column vector (or object coercible via Rmlx::as_mlx()) holding the response values.
weights

Optional MLX column vector or numeric vector of non-negative observation weights. When supplied, weighted least squares are fit via the standard square-root weighting.
rank_tol

Optional relative tolerance used to detect rank-deficient systems. NULL uses the package default, which varies by dtype and is 1e-6 for float32 matrices. Set to FALSE to skip rank checks entirely. Note that higher numbers indicate lower tolerance.

Details

Inputs that are not already MLX objects are converted with Rmlx::as_mlx() or Rmlx::mlx_matrix() so callers can provide base-R matrices or vectors. Weighted fits are performed by applying the standard square-root weight transform before solving the QR system.

Value

A list with components coefficients, fitted.values, residuals, effects, and qr, mirroring the corresponding pieces of stats::lm(). Array-valued components remain MLX matrices to keep downstream GPU pipelines in device memory.

Examples

x <- Rmlx::as_mlx(cbind(1, as.matrix(mtcars[c("cyl", "disp")])))
y <- Rmlx::mlx_matrix(mtcars$mpg, ncol = 1)
fit <- mlxs_lm_fit(x, y)
drop(as.matrix(fit$coefficients))

MLX-friendly Poisson family

Description

MLX-friendly Poisson family

Usage

mlxs_poisson(link = "log")

Arguments

link

a specification for the model link function. This can be a name/expression, a literal character string, a length-one character vector, or an object of class "link-glm" (such as generated by make.link) provided it is not specified via one of the standard names given next.

The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log, identity and cloglog (complementary log-log); the Gamma family the links inverse, identity and log; the poisson family the links log, identity, and sqrt; and the inverse.gaussian family the links 1/mu^2, inverse, identity and log.

The quasi family accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt, and the function power can be used to create a power link function.

Value

A family object compatible with mlxs_glm().

MLX-backed principal components analysis

Description

Perform principal components analysis with MLX arrays, keeping the centred and scaled data on device throughout the decomposition.

Usage

mlxs_prcomp(
  x,
  retx = TRUE,
  center = TRUE,
  scale. = FALSE,
  tol = NULL,
  rank. = NULL,
  oversample = NULL,
  n_iter = 2L,
  seed = 1L,
  ...
)

Arguments

x

Numeric matrix-like object or MLX array with observations in rows.
retx

Should the rotated scores be returned?
center, scale.

Passed to base::scale(). User-supplied vectors are supported.
tol

Optional tolerance for omitting components with small standard deviations, relative to the leading component.
rank.

Optional maximal rank. If smaller than min(n, p), the fit uses the randomized truncated PCA path.
oversample

Oversampling added to the randomized subspace dimension. If NULL, randomized fits use min(rank., max(10, ceiling(rank. / 2))). Ignored for exact fits.
n_iter

Number of randomized power iterations. Ignored for exact fits.
seed

Seed used for the randomized projection basis. Ignored for exact fits.
...

Additional arguments are rejected for compatibility with stats::prcomp().

Details

The interface follows stats::prcomp() closely. Full-rank fits use an exact decomposition. When rank. is supplied and smaller than min(nrow(x), ncol(x)), a randomized truncated PCA path is used instead.

The randomized path first sketches a slightly larger subspace than the requested rank, then compresses back down to the requested components. The oversample parameter controls how much extra space is used in that sketch: larger values make it less likely that the random sketch misses part of the leading principal subspace. The n_iter parameter applies additional power iterations, which improve accuracy when the singular values decay slowly but require extra passes over the matrix.

By default, oversample is chosen as min(rank., max(10, ceiling(rank. / 2))), which keeps the usual constant-size oversampling for small target ranks while allowing more slack for larger truncated fits. This follows common randomized SVD guidance to start with modest oversampling, often around 5 to 10, and to increase oversampling before increasing the number of power iterations.

Value

An object of class c("mlxs_prcomp", "prcomp").

References

Halko, N., Martinsson, P.-G., and Tropp, J. A. (2011). Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. SIAM Review, 53(2), 217-288.

Musco, C. and Musco, C. (2015). Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition. NeurIPS 2015.

MLX-friendly quasibinomial family

Description

MLX-friendly quasibinomial family

Usage

mlxs_quasibinomial(link = "logit")

Arguments

link

a specification for the model link function. This can be a name/expression, a literal character string, a length-one character vector, or an object of class "link-glm" (such as generated by make.link) provided it is not specified via one of the standard names given next.

The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log, identity and cloglog (complementary log-log); the Gamma family the links inverse, identity and log; the poisson family the links log, identity, and sqrt; and the inverse.gaussian family the links 1/mu^2, inverse, identity and log.

The quasi family accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt, and the function power can be used to create a power link function.

Value

A family object compatible with mlxs_glm().

MLX-friendly quasipoisson family

Description

MLX-friendly quasipoisson family

Usage

mlxs_quasipoisson(link = "log")

Arguments

link

a specification for the model link function. This can be a name/expression, a literal character string, a length-one character vector, or an object of class "link-glm" (such as generated by make.link) provided it is not specified via one of the standard names given next.

The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log, identity and cloglog (complementary log-log); the Gamma family the links inverse, identity and log; the poisson family the links log, identity, and sqrt; and the inverse.gaussian family the links 1/mu^2, inverse, identity and log.

The quasi family accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt, and the function power can be used to create a power link function.

Value

A family object compatible with mlxs_glm().

mlxs_glm method utilities

Description

Support functions that provide a familiar S3 surface for mlxs_glm fits by delegating to equivalent base glm behaviour where helpful.

Usage

## S3 method for class 'mlxs_glm'
weights(object, type = c("prior", "working"), ...)

## S3 method for class 'mlxs_glm'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  se.fit = FALSE,
  ...
)

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

## S3 method for class 'mlxs_glm'
residuals(object, type = c("deviance", "pearson", "working", "response"), ...)

## S3 method for class 'mlxs_glm'
vcov(object, ...)

## S3 method for class 'mlxs_glm'
confint(
  object,
  parm,
  level = 0.95,
  ...,
  bootstrap = FALSE,
  bootstrap_args = list(B = 200L, seed = NULL, progress = FALSE, bootstrap_type = "case")
)

## S3 method for class 'mlxs_glm'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'mlxs_glm'
summary(
  object,
  bootstrap = FALSE,
  bootstrap_args = list(B = 200L, seed = NULL, progress = FALSE, bootstrap_type = "case"),
  confint = FALSE,
  level = 0.95,
  ...
)

## S3 method for class 'summary.mlxs_glm'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'mlxs_glm'
anova(object, ...)

## S3 method for class 'mlxs_glm'
model.frame(formula, ...)

## S3 method for class 'mlxs_glm'
model.matrix(object, ...)

## S3 method for class 'mlxs_glm'
terms(x, ...)

## S3 method for class 'mlxs_glm'
nobs(object, ...)

## S3 method for class 'mlxs_glm'
tidy(x, ...)

## S3 method for class 'mlxs_glm'
glance(x, ...)

## S3 method for class 'mlxs_glm'
augment(
  x,
  data = x$model,
  newdata = NULL,
  type.predict = c("response", "link"),
  type.residuals = c("response", "deviance"),
  se_fit = FALSE,
  output = c("data.frame", "mlx"),
  ...
)

## S3 method for class 'mlxs_glm'
estfun(x, ..., output = c("matrix", "mlx"))

## S3 method for class 'mlxs_glm'
hatvalues(model, ..., output = c("matrix", "mlx"))

## S3 method for class 'mlxs_glm'
bread(x, ...)

Arguments

object, model

An mlxs_glm model fit.
type

Character string indicating the scale of the prediction or residuals to return.
...

Additional arguments passed to underlying methods.
newdata

Optional data frame used for prediction.
se.fit

Logical. Should standard errors of the fit be returned when supported?
parm

Parameter specification for confidence intervals.
level

Confidence level for intervals.
bootstrap

Logical; should bootstrap standard errors or confidence intervals be computed?
bootstrap_args

List of bootstrap configuration options. See mlxs_boot().
x

An mlxs_glm model fit (for methods with a leading x argument).
digits

Number of significant digits to print for summaries.
confint

Logical; should confidence intervals be included in the summary object?
formula, data

Optional formula and data overrides used by augment.mlxs_glm().
type.predict, type.residuals

Character strings controlling the scale of fitted values and residuals returned by augment.mlxs_glm().
se_fit

Logical; standard-error analogue for augment.
output

Character string; return format ("data.frame" or "mlx").

mlxs_lm method utilities

Description

These helpers provide the familiar S3 surface for mlxs_lm fits.

Usage

## S3 method for class 'mlxs_lm'
predict(object, newdata = NULL, ...)

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

## S3 method for class 'mlxs_lm'
residuals(object, ...)

## S3 method for class 'mlxs_lm'
vcov(object, ...)

## S3 method for class 'mlxs_lm'
confint(
  object,
  parm,
  level = 0.95,
  ...,
  bootstrap = FALSE,
  bootstrap_args = list(B = 200L, seed = NULL, progress = FALSE, bootstrap_type = "case")
)

## S3 method for class 'mlxs_lm'
anova(object, ...)

## S3 method for class 'mlxs_anova'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

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

## S3 method for class 'mlxs_anova'
tidy(x, ...)

## S3 method for class 'mlxs_lm'
summary(
  object,
  bootstrap = FALSE,
  bootstrap_args = list(B = 200L, seed = NULL, progress = FALSE, bootstrap_type = "case"),
  confint = FALSE,
  level = 0.95,
  ...
)

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

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

## S3 method for class 'mlxs_lm'
model.frame(formula, ...)

## S3 method for class 'mlxs_lm'
model.matrix(object, ...)

## S3 method for class 'mlxs_lm'
terms(x, ...)

## S3 method for class 'mlxs_lm'
nobs(object, ...)

## S3 method for class 'mlxs_lm'
tidy(x, ...)

## S3 method for class 'mlxs_lm'
glance(x, ...)

## S3 method for class 'mlxs_lm'
augment(
  x,
  data = model.frame(x),
  newdata = NULL,
  se_fit = FALSE,
  output = c("data.frame", "mlx"),
  ...
)

## S3 method for class 'mlxs_lm'
estfun(x, ..., output = c("matrix", "mlx"))

## S3 method for class 'mlxs_lm'
hatvalues(model, ..., output = c("matrix", "mlx"))

## S3 method for class 'mlxs_lm'
bread(x, ...)

Arguments

object, model

An mlxs_lm model fit.
newdata

Optional data frame for prediction.
...

Additional arguments passed to underlying methods.
parm

Parameter specification for confidence intervals.
level

Confidence level for intervals.
bootstrap

Logical; should bootstrap standard errors or confidence intervals be computed?
bootstrap_args

List of bootstrap configuration options. See mlxs_boot().
x

An mlxs_lm model fit (for methods with a leading x argument).
row.names

Optional row names for data frame conversion.
optional

Logical; passed to as.data.frame.
confint

Logical; should confidence intervals be included in the summary object?
formula

An mlxs_lm object used in place of formula for model.frame.
data

Optional data frame for augment.
se_fit

Logical; should standard errors of fit be included?
output

Character string; return format ("data.frame", "matrix", "vector" or "mlx"). To make methods from other packages work, the usual default is to return a base R object.

Shared mlxs model methods

Description

Methods for behavior shared by mlxs_lm and mlxs_glm through their mlxs_model superclass.

Usage

## S3 method for class 'mlxs_model'
update(object, ..., evaluate = TRUE)

## S3 method for class 'mlxs_model'
coef(object, ..., output = c("vector", "mlx"))

Arguments

object

An mlxs_model fit.
...

Additional arguments passed to underlying methods.
evaluate

Logical; evaluate the updated call?

PCA methods for mlxs_prcomp

Description

predict.mlxs_prcomp() returns MLX scores. summary() and plot() only materialize standard deviations for base-style output; print() and biplot() materialize rotations and scores as needed for display.

Usage

## S3 method for class 'mlxs_prcomp'
predict(object, newdata, ...)

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

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

## S3 method for class 'mlxs_prcomp'
plot(x, main = deparse1(substitute(x)), ...)

## S3 method for class 'mlxs_prcomp'
biplot(x, ...)

## S3 method for class 'mlxs_prcomp'
nobs(object, ...)

## S3 method for class 'mlxs_prcomp'
tidy(x, ...)

## S3 method for class 'mlxs_prcomp'
augment(x, data = NULL, newdata = NULL, output = c("data.frame", "mlx"), ...)

Arguments

object, x

A fitted mlxs_prcomp object.
newdata

Optional new observations to project.
...

Passed through to the corresponding base method.
data

Optional original data to append PCA scores to in augment.mlxs_prcomp().
output

Output format for augment.mlxs_prcomp(): either a data frame with appended score columns or the MLX score matrix directly.

Value

Method-specific output. predict.mlxs_prcomp() returns an MLX matrix. augment.mlxs_prcomp() returns either a data frame or MLX matrix.