completeFeatures: Determine the Number of Completely Observed Features
In miRcomp: Tools to assess and compare miRNA expression estimatation methods

Description Usage Arguments Value Author(s) Examples

This function determines the number of features that are good quality and non-NA across all samples using a given quality threshold.

1 2	completeFeatures(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, label1=NULL, label2=NULL)

`object1`	a list containing two elements: ct (the expression estiamtes) and qc (quality scores)
`qcThreshold1`	a numeric threshold corresponding to object1$qc below which values are considered low quality.
`object2`	an optional second list of the same format as object1, used to compare two methods.
`qcThreshold2`	a numeric threshold corresponding to object2$qc below which values are considered low quality.
`label1`	optional label corresponding to object 1 to be used in plotting.
`label2`	optional label corresponding to object 2 to be used in plotting.

The function generates a table of the number of complete, partial, and absent features.

Matthew N. McCall

  data(lifetech)
  completeFeatures(object1=lifetech,qcThreshold1=1.25)
  data(qpcRdefault)
  completeFeatures(object1=lifetech,qcThreshold1=1.25,
           object2=qpcRdefault,qcThreshold2=0.99)