Description Usage Arguments Value Author(s) References Examples

View source: R/data.generation.R

Generates a matrix of dimensions `dim(U)`

with
induced correlations. `D`

variables are randomly selected as
discriminatory. If `num.groups = 2`

then discrimination is
induced by adding and subtracting values derived from the level of
of discrimination, `l`

, for the classes respectively.
Multi-class datasets have a few further levels of randomization. For
each variable, a random number of the groups are selected as
discriminating while the remaining groups are not altered.
For each discriminatory group, a unique change is provided by
randomly assigning addition or subtraction of the discrimination factor.
For example, if 3 groups are selected and two groups are assigned
as addition and the third subtraction, the second addition is
multiplied by its number of replicates. E.g. (1,1,-1) -> (1,2,-1).
These values are randomized and then multiplied by the respective
discrimination factor. The resulting values are then added/subtracted
from the respective groups. A noise matrix is applied to the final
matrix to perturb 'perfect' discrimination.

1 | ```
create.discr.matrix(V, D = 20, l = 1.5, num.groups = 2, k = 4)
``` |

`V` |
Numeric matrix |

`D` |
Number of discriminatory variables induced. Default |

`l` |
Level of discrimination, higher = greater separation.
Default |

`num.groups` |
Number of groups in the dataset |

`k` |
Correlation Perturbation - The higher k, the more the data
is perturbed. Default |

List of the following elements

`discr.mat` |
Matrix of dimension |

`features` |
Vector of features that were induced to be discriminatory. |

Charles E. Determan Jr.

Wongravee, K., Lloyd, G R., Hall, J., Holmboe, M. E., &
Schaefer, M. L. (2009). *Monte-Carlo methods for determining optimal
number of significant variables. Application to mouse urinary profiles.*
Metabolomics, 5(4), 387-406. http://dx.doi.org/10.1007/s11306-009-0164-4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
``` |

```
solo last variable
```

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