Description Usage Arguments Details Value Author(s) References See Also Examples

The function "camel.cmr" implements calibrated multivariate regression using jointly sparse regularization.

1 2 |

`Y` |
The |

`X` |
The |

`lambda` |
A sequence of decresing positive value to control the regularization. Typical usage is to leave the input |

`nlambda` |
The number of values used in |

`prec` |
Stopping criterion. The default value is 1e-3. |

`max.ite` |
The iteration limit. The default value is 1e3. |

`mu` |
The smoothing parameter. The default value is 0.01. |

`verbose` |
Tracing information is disabled if |

Calibrated multivariate regression adjusts the regularization with respect to the noise level of each task. Thus it achieves improved statistical performance and the tuning insensitiveness.

An object with S3 class `"camel.cmr"`

is returned:

`beta` |
A list of matrice of regression estimates where each entry corresponds to a regularization parameter. |

`intercept` |
The value of intercepts corresponding to regularization parameters. |

`Y` |
The value of |

`X` |
The value of |

`lambda` |
The sequence of regularization parameters |

`nlambda` |
The number of values used in |

`sparsity` |
The sparsity levels of the solution path. |

`ite` |
A list of vectors where ite[[1]] is the number of external iteration and ite[[2]] is the number of internal iteration with the i-th entry corresponding to the i-th regularization parameter. |

`verbose` |
The |

Xingguo Li, Tuo Zhao, and Han Liu

Maintainer: Xingguo Li <xingguo.leo@gmail.com>

1. L. Han, L. Wang, and T. Zhao. Multivariate Regression with Calibration. *http://arxiv.org/abs/1305.2238*, 2013.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Generate the design matrix and regression coefficient vector
n = 200
d = 400
m = 13
Sigma = matrix(0.5,d,d)
diag(Sigma) = 1
X = mvrnorm(n,rep(0,d),Sigma)
B = matrix(0,d,m)
B[1,] = 3
B[2,] = 2
B[4,] = 1.5
W = matrix(rnorm(n*m,0,1),n,m)
sig = sqrt(2)
D = sig*diag(2^(c(0:-12)/4))
Z = W%*%D
Y = X%*%B + Z
out = camel.cmr(X, Y)
``` |

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