qpcrImpute: Impute Non-detects in qPCR data
In valery-sherina/nondetects2-Val: Non-detects in qPCR data

Description Usage Arguments Value Author(s) Examples

This function models the missing data mechanism and uses an EM algorithm to impute the non-detect values in qPCR data.

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qpcrImpute(object, dj=NULL, pyfit=NULL, groupVars=NULL, tol=1, iterMax=100, 
          outform=c("Single","Param","Multy"), formula=NULL, numsam=5, 
          vary_fit=TRUE, vary_model=TRUE, add_noise=TRUE)

`object`	a qPCRset
`dj`	normalization values. If NULL, features with "control" in featureType(object) are used to normalize the data. If no control features are found, the data are not normalized.
`pyfit`	initial estimate of the relationship between the probability of a non-detect and average expression. If NULL, this relationship is estimated from the data.
`groupVars`	which columns in pData(object) should be used to determine replicate samples. If NULL, all columns are used.
`tol`	likelihood convergence criterion of the EM algorithm.
`iterMax`	maximimum number of iterations of the EM algorithm.
`outform`	the form of the output requested.If "Single" performes a single imputation of missing values. If "Param" returnes estimated model parameters: mean and variance. If "Multy" performes a multiple imputation of missing values, and creats multiple data sets with imputed values.
`formula`	specifies the model.
`numsam`	number of the datasets to be created if outform="Multy". The default value is 5.
`vary_fit`	if outform="Multy", includes the model uncertainty due to the logit of the probability of being missing. The default value is "TRUE".
`vary_model`	if outform="Multy", includes the model uncertainty due to the estimating mean of the data. The default value is "TRUE".
`add_noise`	if outform="Multy", introduses the variance component due to the random noise. The default value is "TRUE".