This function calculates MI and BCMI between a set of discrete variables held as columns in a matrix. It also performs jackknife bias correction and provides a z-score for the hypothesis of no association. Also included are the *.pw functions that calculate MI between two vectors only. The *njk functions do not perform the jackknife and are therefore faster.

1 2 3 4 |

`dmat` |
The data matrix. Each row is an observation and each column is a variable of interest. Should contain categorical data, all types of data will be coerced via factors to integers. |

`disc1` |
A vector for the pairwise version |

`disc2` |
A vector for the pairwise version |

The results of dmi() are in many ways similar to a correlation matrix,
with each row and column index corresponding to a given variable.
dminjk() and dminjk.pw() just returns the MI values without performing the
jackknife. The number of processor cores used can be changed by
setting the environment variable "OMP_NUM_THREADS" *before* starting R.

Returns a list of 3 matrices each of size `ncol(dmat)`

by
`ncol(dmat)`

`mi` |
The raw MI estimates. |

`bcmi` |
Jackknife bias corrected MI estimates (BCMI). These are each MI value minus the corresponding jackknife estimate of bias. |

`zvalues` |
Z-scores for each hypothesis that the corresponding bcmi value is zero. These have poor statistical properties but can be useful as a rough measure of the strength of association. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data(cars)
# Discretise the data first
d <- cut(cars$dist, breaks = 10)
s <- cut(cars$speed, breaks = 10)
# Discrete MI values
dmi.pw(s, d)
# For comparison, analysed as continuous data:
cmi.pw(cars$dist, cars$speed)
# Exploring a group of categorical variables
dat <- mtcars[, c("cyl","vs","am","gear","carb")]
discresults <- dmi(dat)
discresults
# Plot the relative magnitude of the BCMI values
diag(discresults$bcmi) <- NA
mp(discresults$bcmi)
``` |

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