decorrelate (version 0.1.6.4)

lm_eclairs: 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,
  ...
)

Value

Object of class lm returned by function lm

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.

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)