Fit linear models with array-shaped response

Wraps many_lm to accept a response Y with more than two dimensions (e.g. a 3-D array). The trailing dimensions are collapsed into columns for fitting and optionally restored in the output.

Usage

array_lm(
  X,
  Y,
  weights = NULL,
  intercept = FALSE,
  include_fitted = FALSE,
  include_residuals = FALSE,
  cache = TRUE,
  method = c("normal", "qr"),
  restore_array = TRUE
)

Arguments

X

Numeric matrix or adgeMatrix of predictors, shape [n, p].

Y

Numeric array or matrix of responses. The first dimension must equal n (observations). Additional dimensions are treated as independent response variables.

weights

Optional numeric vector of length n with non-negative observation weights.

intercept

Logical. When TRUE, a column of ones is prepended to X before fitting.

include_fitted

Logical. When TRUE, fitted values are stored in the returned object.

include_residuals

Logical. When TRUE, residuals are stored in the returned object.

cache

Logical. When TRUE, the design-matrix factorization is cached for reuse.

method

Solver: "normal" or "qr".

restore_array

Logical. When TRUE (default), rss, sigma2, fitted values, and residuals are reshaped to match the original trailing dimensions of Y.

Value

An object of class "am_array_lm_fit", a named list containing the same fields as many_lm plus:

response_dims

Integer vector of trailing dimensions of Y.

rss

Array or vector of residual sums of squares.

sigma2

Array or vector of residual variances.

See also

many_lm, lm_fit

Examples

X <- matrix(rnorm(50), nrow = 10)
Y <- array(rnorm(10 * 3 * 4), dim = c(10, 3, 4))
fit <- array_lm(X, Y)
dim(fit$sigma2)
#> NULL