MRcoefs: Table of top-ranked features from fitZig or fitFeatureModel
In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Description Usage Arguments Value See Also Examples

Extract a table of the top-ranked features from a linear model fit. This function will be updated soon to provide better flexibility similar to limma's topTable.

MRcoefs(
  obj,
  by = 2,
  coef = NULL,
  number = 10,
  taxa = obj@taxa,
  uniqueNames = FALSE,
  adjustMethod = "fdr",
  alpha = 0.1,
  group = 0,
  eff = 0,
  numberEff = FALSE,
  counts = 0,
  file = NULL
)

`obj`	Output of fitFeatureModel or fitZig.
`by`	Column number or column name specifying which coefficient or contrast of the linear model is of interest.
`coef`	Column number(s) or column name(s) specifying which coefficient or contrast of the linear model to display.
`number`	The number of bacterial features to pick out.
`taxa`	Taxa list.
`uniqueNames`	Number the various taxa.
`adjustMethod`	Method to adjust p-values by. Default is "FDR". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See `p.adjust` for more details. Additionally, options using independent hypothesis weighting (IHW) are available. See `MRihw` for more details.
`alpha`	Value for p-value significance threshold when running IHW. The default is set to 0.1
`group`	One of five choices, 0,1,2,3,4. 0: the sort is ordered by a decreasing absolute value coefficient fit. 1: the sort is ordered by the raw coefficient fit in decreasing order. 2: the sort is ordered by the raw coefficient fit in increasing order. 3: the sort is ordered by the p-value of the coefficient fit in increasing order. 4: no sorting.
`eff`	Filter features to have at least a "eff" quantile or number of effective samples.
`numberEff`	Boolean, whether eff should represent quantile (default/FALSE) or number.
`counts`	Filter features to have at least 'counts' counts.
`file`	Name of output file, including location, to save the table.

Table of the top-ranked features determined by the linear fit's coefficient.

fitZig fitFeatureModel MRtable MRfulltable

data(lungData)
k = grep("Extraction.Control",pData(lungData)$SampleType)
lungTrim = lungData[,-k]
lungTrim=filterData(lungTrim,present=30)
lungTrim=cumNorm(lungTrim,p=0.5)
smokingStatus = pData(lungTrim)$SmokingStatus
mod = model.matrix(~smokingStatus)
fit = fitZig(obj = lungTrim,mod=mod)
head(MRcoefs(fit))
####
fit = fitFeatureModel(obj = lungTrim,mod=mod)
head(MRcoefs(fit))