lars.lsa {pathwayPCA} | R Documentation

## Least Angle Regression and LASSO Regression

### Description

These are all variants of LASSO, and provide the entire sequence of coefficients and fits, starting from zero to the least squares fit.

### Usage

    lars.lsa(Sigma0, b0, n, type = c("lar", "lasso"), max.steps = NULL,
      eps = .Machine$double.eps, adaptive = TRUE, para = NULL)

### Arguments

`Sigma0` | A Grammian / covariance matrix of pathway predictors.
`b0` | An eigenvector of `Sigma0`.
`n` | The sample size.
`type` | Option between `"lar"` and `"lasso"`. Defaults to `"lasso"`.
`max.steps` | How many steps should the LAR or LASSO algorithms take? Defaults to 8 times the pathway dimension.
`eps` | What should we consider to be numerically 0? Defaults to the machine's default error limit for doubles (`.Machine$double.eps`).
`adaptive` | Ignore.
`para` | Ignore.

### Details

LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the `"lasso"` option, it computes the complete LASSO solution simultaneously for all values of the shrinkage parameter in the same computational cost as a least squares fit. This function is adapted from the `lars` function in the `lars` package to apply to covariance or Grammian pathway design matrices.

### Value

An object of class `"lars"`.

### See Also

https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf

### Examples

      # DO NOT CALL THIS FUNCTION DIRECTLY.
      # Use AESPCA_pVals() instead

    ## Not run:
      X_mat <- as.matrix(colonSurv_df[, 5:50])
      X_mat <- scale(X_mat)

      XtX <- t(X_mat) %*% X_mat
      A_mat <- svd(XtX)$v

      lars.lsa(
        Sigma0 = XtX,
        b0 = A_mat[1, ] * sign(A_mat[1, 1]),
        n = ncol(X_mat)
      )

    ## End(Not run)

[Package pathwayPCA version 1.4.0 Index]