aIc.scale {aIc} | R Documentation |

`aIc.scale`

calculates the scaling invariance of a sample in
a dataset for a given correction. This compares the distances of samples
of the full dataset and a scaled version of the dataset.
This is expected to be true if the transform is behaving rationally in
compositional datasets.`aIc.scale`

calculates the scaling invariance of a sample in
a dataset for a given correction. This compares the distances of samples
of the full dataset and a scaled version of the dataset.
This is expected to be true if the transform is behaving rationally in
compositional datasets.

```
aIc.scale(
data,
norm.method = "prop",
zero.remove = 0.95,
zero.method = "prior",
distance = "euclidian",
log = FALSE,
group = NULL
)
```

`data` |
can be any dataframe or matrix with samples by column |

`norm.method` |
can be prop, clr, iqlr, lvha, RLE, TMM, TMMwsp |

`zero.remove` |
is a value. Filter data to remove features that are 0 across at least that proportion of samples: default 0.95 |

`zero.method` |
can be any of NULL, prior, GBM or CZM. NULL will not impute or change 0 values, GBM (preferred) and CZM are from the zCompositions R package, and prior will simply add 0.5 to all counts. |

`distance` |
can be euclidian, bray, or jaccard. euclidian on log-ratio transformed data is the same as the Aitchison distance. default=euclidian |

`log` |
is a logical. log transform the RLE or TMM outputs, default=FALSE |

`group` |
is a vector containing group information. Required for clr, RLE, TMM, lvha, and iqlr based normalizations. |

Returns a list with the overlap between distances in the full and
scaled composition in `ol`

(expect 0), a yes/no binary decision in
`is.scale`

and the table of distances for the whole and scaled composition
in `dist.all`

and `dist.scale`

, a plot showing a histogram of the resulting
overlap in distances in `plot`

, and the plot and axis
labels in `main`

`xlab`

and `ylab`

Greg Gloor

```
data(selex)
group = c(rep('N', 7), rep('S', 7))
x <- aIc.scale(selex, group=group, norm.method='clr', zero.method='prior')
plot(x$plot, main=x$main, ylab=x$ylab, xlab=x$xlab)
```

[Package *aIc* version 1.0 Index]