lm_eclairs: Fit linear model after decorrelating

In decorrelate: Decorrelation Projection Scalable to High Dimensional Data

Fit linear model after decorrelating

Description

Fit linear model after applying decorrelation projection to response and predictors.

Usage

lm_eclairs(
  formula,
  data,
  ecl,
  subset,
  weights,
  na.action,
  method = "qr",
  model = TRUE,
  x = FALSE,
  y = FALSE,
  qr = TRUE,
  singular.ok = TRUE,
  contrasts = NULL,
  offset,
  ...
)

Arguments

formula	an object of class 'formula' (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	a matrix or data.frame containing the variables in the model
ecl	estimate of covariance/correlation matrix from eclairs storing U, d_1^2, \lambda and \nu
subset	same as for lm
weights	same as for lm
na.action	same as for lm
method	same as for lm
model	same as for lm
x	same as for lm
y	same as for lm
qr	same as for lm
singular.ok	same as for lm
contrasts	same as for lm
offset	same as for lm
...	same as for lm

Details

This function fit a linear regression to the transformed response, and transformed design matrix. Note that the design matrix, not just the data.frame of variables is transformed so that 1) factors are transformed and 2) the intercept term is transformed.

Value

Object of class lm returned by function lm

Examples

library(Rfast)

n <- 800 # number of samples
p <- 200 # number of features

# create correlation matrix
Sigma <- autocorr.mat(p, .9)

# draw data from correlation matrix Sigma
Y <- rmvnorm(n, rep(0, p), sigma = Sigma * 5.1, seed = 1)

# eclairs decomposition
ecl <- eclairs(Y)

# simulate covariates
data <- data.frame(matrnorm(p, 2, seed = 1))
colnames(data) <- paste0("v", 1:2)

# simulate response
y <- rnorm(p)

# fit linear model on transformed data
lm_eclairs(y ~ v1 + v2, data, ecl)

decorrelate documentation built on Aug. 8, 2025, 7:55 p.m.