precision: Assess the precision of the expression estimates
In miRcomp: Tools to assess and compare miRNA expression estimatation methods

Description Usage Arguments Value Author(s) Examples

This function assesses the within-replicate precision for each feature.

precision(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL,
          commonFeatures=TRUE, statistic=c("sd","cv"),
          scale=c("none","log","log10"), bins=3,
          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.
`commonFeatures`	if TRUE and object2 is non-NULL, only high quality non-NA features in common between both objects are used.
`statistic`	whether to compute the standard deviation (sd) or coefficient of variation (cv).
`scale`	optional scaling of the values. This can help with visualizing the distributions.
`bins`	the number of bins to divide the data into.
`label1`	optional label corresponding to object 1 to be used in plotting.
`label2`	optional label corresponding to object 2 to be used in plotting.

A boxplot of either the standard deviation or coefficient of variation stratified by expression is produced. The values plotted in each box of the boxplot are returned.

Matthew N. McCall

  data(lifetech)
  tmp1 <- precision(object1=lifetech,qcThreshold1=1.25)
  data(qpcRdefault)
  tmp2 <- precision(object1=lifetech,qcThreshold1=1.25,
           object2=qpcRdefault,qcThreshold2=0.99)