coef.grpSLOPE | R Documentation |

Extract the regression coefficients from a `grpSLOPE`

object, either on the
scale of the normalized design matrix (i.e., columns centered and scaled to unit norm),
or on the original scale.

## S3 method for class 'grpSLOPE' coef(object, scaled = TRUE, ...)

`object` |
A |

`scaled` |
Should the coefficients be returned for the normalized version of the design matrix? |

`...` |
Potentially further arguments passed to and from methods |

If `scaled`

is set to `TRUE`

, then the coefficients are returned for the
normalized version of the design matrix, which is the scale on which they were computed.
If `scaled`

is set to `FALSE`

, then the coefficients are transformed to
correspond to the original (unaltered) design matrix.
In case that `scaled = FALSE`

, an estimate for the intercept term is returned with
the other coefficients. In case that `scaled = TRUE`

, the estimate of the intercept
is always equal to zero, and is not explicitly provided.

A named vector of regression coefficients where the names signify the group that each entry belongs to

set.seed(1) A <- matrix(rnorm(100^2), 100, 100) grp <- rep(rep(letters[1:20]), each=5) b <- c(rep(1, 20), rep(0, 80)) y <- A %*% b + rnorm(10) result <- grpSLOPE(X=A, y=y, group=grp, fdr=0.1) head(coef(result), 8) # a_1 a_2 a_3 a_4 a_5 b_1 b_2 b_3 # 7.942177 7.979269 8.667013 8.514861 10.026664 8.963364 10.037355 10.448692 head(coef(result, scaled = FALSE), 8) # (Intercept) a_1 a_2 a_3 a_4 a_5 b_1 b_2 # -0.4418113 0.8886878 0.8372108 0.8422089 0.8629597 0.8615827 0.9323849 0.9333445

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