estimate_stability: Estimate Stability
In microbiome: Microbiome Analytics

Description Usage Arguments Details Value Author(s) Examples

Quantify intermediate stability with respect to a given reference point.

1	estimate_stability(df, reference.point = NULL, method = "lm", spl.list)

`df`	Combined input data vector (samples x variables) and metadata data.frame (samples x features) with the 'data', 'subject' and 'time' field for each sample
`reference.point`	Optional. Calculate stability of the data w.r.t. this point. By default the intermediate range is used (min + (max - min)/2)
`method`	'lm' (linear model) or 'correlation'; the linear model takes time into account as a covariate
`spl`	split object to speed up

Decomposes each column in x into differences between consecutive time points. For each variable and time point we calculate for the data values: (i) the distance from reference point; (ii) distance from the data value at the consecutive time point. The 'correlation' method calculates correlation between these two variables. Negative correlations indicate that values closer to reference point tend to have larger shifts in the consecutive time point. The 'lm' method takes the time lag between the consecutive time points into account as this may affect the comparison and is not taken into account by the straightforward correlation. Here the coefficients of the following linear model are used to assess stability: abs(change) ~ time + abs(start.reference.distance). Samples with missing data, and subjects with less than two time point are excluded.

A list with following elements: stability: estimated stability data: processed data set used in calculations

Leo Lahti leo.lahti@iki.fi

# df <- data.frame(list(
#           subject=rep(paste('subject', 1:50, sep='-'), each=2), 
#           time=rep(1:2, 50),
#           data=rnorm(100)))
#s <- estimate_stability_single(df, reference.point=NULL, method='lm')

microbiome documentation built on Nov. 8, 2020, 5:08 p.m.