Description Usage Arguments Value References Examples

This function is called internally by `lars.c`

to compute the transformed versions of the X, Y, and constraint
matrix data, as shown in the PaC paper.

1 | ```
transformed.ineq(x, y, C.full, b, lambda, beta0, eps = 10^-8)
``` |

`x` |
independent variable matrix of data to be used in calculating PaC coefficient paths |

`y` |
response vector of data to be used in calculating PaC coefficient paths |

`C.full` |
complete constraint matrix C (with inequality constraints of the form |

`b` |
constraint vector b |

`lambda` |
value of lambda |

`beta0` |
initial guess for beta coefficient vector |

`eps` |
value close to zero used to verify SVD decomposition. Default is 10^-8 |

`x`

transformed x data to be used in the PaC algorithm

`y`

transformed y data to be used in the PaC algorithm

`Y_star`

transformed Y* value to be used in the PaC algorithm

`a2`

index of A used in the calculation of beta2 (the non-zero coefficients)

`beta1`

beta1 values

`beta2`

beta2 values

`C`

constraint matrix

`C2`

subset of constraint matrix corresponding to non-zero coefficients

`active.beta`

index of non-zero coefficient values

`beta2.index`

index of non-zero coefficient values

Gareth M. James, Courtney Paulson, and Paat Rusmevichientong (JASA, 2019) "Penalized and Constrained Optimization." (Full text available at http://www-bcf.usc.edu/~gareth/research/PAC.pdf)

1 2 3 4 5 6 7 | ```
random_data = generate.data(n = 500, p = 20, m = 10)
transform_fit = transformed.ineq(random_data$x, random_data$y,
random_data$C.full, random_data$b, lambda = 0.01, beta0 = rep(0,20))
dim(transform_fit$x)
head(transform_fit$y)
dim(transform_fit$C)
transform_fit$active.beta
``` |

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