Description Usage Arguments Details Value Examples

This function calculates MI and BCMI between a set of continuous variables and a set of discrete variables (variables in columns). 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 |

`cts` |
The data matrix. Each row is an observation and each column is a variable of interest. Should be numerical data. (For the pairwise functions this should be a vector.) |

`disc` |
Matrix of discrete data, each row is an observation and each column is a variable. Will be coerced to integers. (For the pairwise functions this should be a vector.) |

`level` |
The number of levels used for plug-in bandwidth estimation (see the documentation for the KernSmooth package.) |

`na.rm` |
Remove missing values if TRUE. This is required for the bandwidth calculation. |

`h` |
A (double) vector of smoothing bandwidths, one for each variable. If missing this will be calculated using the dpik() function from the KernSmooth package. |

`...` |
Additional options passed to dpik() if necessary. |

mminjk() and mminjk.pw() return just the MI values without performing the
jackknife. mmi.pw() and mminjk.pw() only require one bandwidth for the
continuous variable. 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(cts) by ncol(disc). Each row index represents a continuous variable and each column index a discrete variable.

`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 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ```
##################################################
# A dataset with discrete and continuous variables
cts <- state.x77
disc <- data.frame(state.division,state.region)
summary(cts)
table(disc)
m1 <- mmi(cts, disc)
lapply(m1, round, 2)
# Division gives more information about the continuous variables than region.
# Here is one where both division and region show a strong association:
boxplot(cts[,6] ~ disc[,1])
boxplot(cts[,6] ~ disc[,2])
# In this case the states need to be divided into regions before a clear
# association can be seen:
boxplot(cts[,1] ~ disc[,1])
boxplot(cts[,1] ~ disc[,2])
# Look at associations within the continuous variables:
pairs(cts, col = state.region)
c1 <- cmi(cts)
lapply(c1, round, 2)
##################################################
# A pairwise comparison
# Note that the ANOVA homoskedasticity assumption is not satisfied here.
boxplot(InsectSprays[,1] ~ InsectSprays[,2])
mmi.pw(InsectSprays[,1], InsectSprays[,2])
##################################################
# Another pairwise comparison
boxplot(morley[,3] ~ morley[,1])
m2 <- mmi.pw(morley[,3], morley[,1])
m2
##################################################
# See the vignette for large-scale examples.
``` |

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