Description Usage Arguments Value References Examples

This function is primarily used for reproducibility. It will generate a data set of a given size with a given number of constraints for testing function code.

1 2 | ```
generate.data(n = 1000, p = 10, m = 5, cov.mat = NULL, s = 5,
sigma = 1, glasso = F, err = 0)
``` |

`n` |
number of rows in randomly-generated data set (default is 1000) |

`p` |
number of variables in randomly-generated data set (default is 10) |

`m` |
number of constraints in randomly-generated constraint matrix (default is 5) |

`cov.mat` |
a covariance matrix applied in the generation of data to impose a correlation structure. Default is NULL (no correlation) |

`s` |
number of true non-zero elements in coefficient vector beta1 (default is 5) |

`sigma` |
standard deviation of noise in response (default is 1, indicating standard normal) |

`glasso` |
should the generalized Lasso be used (TRUE) or standard Lasso (FALSE). Default is FALSE |

`err` |
error to be introduced in random generation of coefficient values. Default is no error (err = 0) |

`x`

generated `x`

data

`y`

generated response `y`

vector

`C.full`

generated full constraint matrix (with constraints of the form `C.full`

*`beta`

=`b`

)

`b`

generated constraint vector `b`

`b.run`

if error was included, the error-adjusted value of `b`

`beta`

the complete beta vector, including generated `beta1`

and `beta2`

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 | ```
random_data = generate.data(n = 500, p = 20, m = 10)
dim(random_data$x)
head(random_data$y)
dim(random_data$C.full)
random_data$beta
``` |

```
Loading required package: penalized
Loading required package: survival
Welcome to penalized. For extended examples, see vignette("penalized").
[1] 500 20
[1] 2.3602319 -0.7045475 2.3177571 6.4863212 5.3387137 2.6740237
[1] 10 20
[1] 1.701332775 1.578918822 1.569004900 1.645264048 1.058658151
[6] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[11] -0.874289078 1.430112024 0.218669916 -1.001107376 -0.175887054
[16] 1.366756156 0.803341774 -1.345645786 -0.898999992 -0.001670896
```

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