The summary statistics stored here are used by the tools for copy number estimation.

1 2 3 4 5 |

`object` |
An object of class |

`...` |
Ignored |

An array with dimension R x A x G x C, or R x G x C.

R: number of markers A: number of alleles (2) G: number of biallelic genotypes (3) C: number of batches

`Ns`

returns an array of genotype frequencies stratified by
batch. Dimension R x G x C.

`corr`

returns an array of within-genotype correlations
(log2-scale) stratified by batch. Dimension R x G x C.

`medians`

returns an array of the within-genotype medians
(intensity-scale) stratified by batch and allele. Dimension R x A x G
x C.

`mads`

returns an array of the within-genotype median absolute
deviations (intensity-scale) stratified by batch and allele. Dimension
is the same as for `medians`

.

`tau2`

returns an array of the squared within-genotype median
absolute deviation on the log-scale. Only the mads for AA and BB
genotypes are stored. Dimension is R x A x G x C, where G is AA or
BB. Note that the mad for allele A/B for subjects with genotype BB/AA
is a robust estimate of the background variance, whereas the the mad
for allele A/B for subjects with genotype AA/BB is a robust estimate
of the variance for copy number greater than 0 (we assume that on the
log-scale the variance is rougly constant for CA, CB > 0).

1 2 3 4 5 6 | ```
data(cnSetExample)
Ns(cnSetExample)[1:5, , ]
corr(cnSetExample)[1:5, , ]
meds <- medians(cnSetExample)
mads(cnSetExample)[1:5, , ,]
tau2(cnSetExample)[1:5, , ,]
``` |

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