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

`chances`

estimates Bernoulli parameters (=chances) from a binary matrix and associated class labels.

1 |

`X` |
data matrix (columns correspond to variables, rows to samples). |

`L` |
factor containing the class labels, one for each sample (row). |

`lambda.freqs` |
shrinkage parameter for class frequencies (if not specified it is estimated). |

`verbose` |
report shrinkage intensity and other information. |

The class-specific chances are estimated using the empirical means over the 0s and 1s in each class. For estimating the pooled mean the class-specific means are weighted using the
estimated class frequencies. Class frequencies are estimated using `freqs.shrink`

.

`chances`

returns a list with the following components:

`samples`

: the samples in each class,

`regularization`

: the shrinkage intensity used to estimate the class frequencies,

`freqs`

: the estimated class frequencies,

`means`

: the estimated chances (parameters of Bernoulli distribution, expectations of 1s) for each variable conditional on class, as well as the marginal changes (pooled means).

Sebastian Gibb and Korbinian Strimmer (http://strimmerlab.org).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# load binda library
library("binda")
# example binary matrix with 6 variables (in columns) and 4 samples (in rows)
Xb = matrix(c(1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 0,
1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 1, 1), nrow=4, byrow=TRUE)
colnames(Xb) = paste0("V", 1:ncol(Xb))
# Test for binary matrix
is.binaryMatrix(Xb) # TRUE
L = factor(c("Treatment", "Treatment", "Control", "Control") )
chances(Xb, L)
``` |

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