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.

Source: R/makeCqData.R

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

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.

Value

returns a list of samples with cq values (data.cq)

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...