make.Cq.data: This Function will create the main Data object for all...
In LucasFVoges/DoubleqpcR: DMAS-qPCR data analysis

make.Cq.data

R Documentation

This Function will create the main Data object for all analysis. Used after an input is created with read.CqTable and all optional calculation data validation is complete.

Description

Outliers can be removed. To check the results one can use the table.Cq() function before.

Usage

make.Cq.data(
  add = FALSE,
  target = "Genotype A",
  CqType = c("TP", "SD"),
  outliers = TRUE,
  outliers.method = "Grubbs",
  alpha = 0.05,
  outlier.range = 3,
  silent = FALSE
)

Arguments

`add`	This toggle wil add the samples, if an data.cq is already existing in global scope.
`target`	the target genotype "genotype A".
`CqType`	this is the Cq value columns from the input.cq that should be used.
`outliers`	logical if outliers are to be deleted from the output
`outliers.method`	If a "Dixon" or "Grubbs" test should be used.
`alpha`	alpha for outlier testing (0.05 = 95% significance)
`outlier.range`	For Grubbs: input ignored, set to 6. For Dixon: This is only important for samples with 3 or less values. In this case the range of data (e.g. Range c(1,1.4,1.3) = 0.4) need to be at least outlier.range if an outlier test should happen. Normally outlier test for 3 or less values is not recommended. But this helps to get rid of clear outliers e.g. (2,2,30). My advice is to check the data also manually.
`silent`	If status of outlier detection and processing is printed.

Details

If an data.Cq object already exists, it will be overwritten when add = FALSE. Otherwise samples will be added. Or overwritten! It is not jet possible to add more values in a sample...