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

Fit solution paths for sparse linear SVM regularized by lasso or elastic-net over a grid of values for the regularization parameter lambda.

1 2 3 4 5 |

`X` |
Input matrix. |

`y` |
Output vector. Currently the function only supports binary output and converts the output into +1/-1 coding internally. |

`alpha` |
The elastic-net mixing parameter that controls the relative contribution
from the lasso and the ridge penalty. It must be a number between 0 and 1. |

`gamma` |
The tuning parameter for huberization smoothing of hinge loss. Default is 0.1. |

`nlambda` |
The number of lambda values. Default is 100. |

`lambda.min` |
The smallest value for lambda, as a fraction of lambda.max, the data derived entry value. Default is 0.01 if the number of observations is larger than the number of variables and 0.05 otherwise. |

`lambda` |
A user-specified sequence of lambda values. Typical usage is to leave
blank and have the program automatically compute a |

`preprocess` |
Preprocessing technique to be applied to the input. Either
"standardize" (default), "rescale" or "none" (see |

`screen` |
Screening rule to be applied at each |

`max.iter` |
Maximum number of iterations. Default is 1000. |

`eps` |
Convergence threshold. The algorithms continue until the maximum change in the
objective after any coefficient update is less than |

`dfmax` |
Upper bound for the number of nonzero coefficients. The algorithm exits and
returns a partial path if |

`penalty.factor` |
A numeric vector of length equal to the number of variables. Each
component multiplies |

`message` |
If set to TRUE, sparseSVM will inform the user of its progress. This argument is kept for debugging. Default is FALSE. |

The sequence of models indexed by the regularization parameter `lambda`

is fitted
using a semismooth Newton coordinate descent algorithm. The objective function is defined
to be

*∑ hingeLoss(y_i (x_i' w + b))/n + λ*penalty(w).*

where

*hingeLoss(t) = max(0, 1-t)*

and the intercept `b`

is unpenalized.

The program supports different types of preprocessing techniques. They are applied to
each column of the input matrix `X`

. Let x be a column of `X`

. For
`preprocess = "standardize"`

, the formula is

*x' = (x-mean(x))/sd(x);*

for `preprocess = "rescale"`

,

*x' = (x-min(x))/(max(x)-min(x)).*

The models are fit with preprocessed input, then the coefficients are transformed back to the original scale via some algebra.

The function returns an object of S3 class `"sparseSVM"`

, which is a list containing:

`call` |
The call that produced this object. |

`weights` |
The fitted matrix of coefficients. The number of rows is equal to the number
of coefficients, and the number of columns is equal to |

`iter` |
A vector of length |

`saturated` |
A logical flag for whether the number of nonzero coefficients has reached |

`lambda` |
The sequence of regularization parameter values in the path. |

`alpha` |
Same as above. |

`gamma` |
Same as above. |

`penalty.factor` |
Same as above. |

`levels` |
Levels of the output class labels. |

Congrui Yi and Yaohui Zeng

Maintainer: Congrui Yi <eric.ycr@gmail.com>

1 2 3 4 5 6 7 8 9 |

```
Loading required package: parallel
(Intercept) V1 V2 V3 V4 V5
-0.4729766 0.4685178 0.3527636 0.6406139 -0.8139670 -0.1796772
V6 V7 V8 V9 V10 V11
-0.4643762 -0.3854431 -0.3612029 0.0000000 -0.6262678 0.0000000
V12 V13 V14 V15 V16 V17
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V18 V19 V20 V21 V22 V23
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V24 V25 V26 V27 V28 V29
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V30 V31 V32 V33 V34 V35
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V36 V37 V38 V39 V40 V41
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V42 V43 V44 V45 V46 V47
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V48 V49 V50 V51 V52 V53
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V54 V55 V56 V57 V58 V59
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V60 V61 V62 V63 V64 V65
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V66 V67 V68 V69 V70 V71
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V72 V73 V74 V75 V76 V77
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V78 V79 V80 V81 V82 V83
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V84 V85 V86 V87 V88 V89
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V90 V91 V92 V93 V94 V95
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
V96 V97 V98 V99 V100
0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
0.2 0.1
[1,] 1 -1
[2,] 1 1
[3,] 1 1
[4,] 1 1
[5,] 1 1
```

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