This function will show the Cq values from the samples. You need to have input.cq "read.cqTable" or build it manually with Cq values present.
table.Cq(
sample = NA,
target = "Genotype A",
CqType = c("TP", "SD"),
outliers = TRUE,
outliers.method = "Grubbs",
alpha = 0.05,
outlier.range = 3,
decimals = 3,
format = "kable",
silent = FALSE
)specify the sample from input.cq
the target genotype "genotype A".
wich Cq values should be used. This can be a vector!
logical if outliers are to be deleted from the output
If a "Dixon" or "Grubbs" test should be used.
alpha for outlier testing (0.05 = 95% significance)
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 shoud 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.
decimals for the resulting table (ignored for format = "data")
How the table will be formated. possible are "kable" and "DT" (work in progress) or "data" for pure dataframe, "fulldata" for a dataframe with difference, mean, and sd. Or any other input will give just the table.
should status be printed? (mostly for outlier detection)
returns a table with the delta Cq values with mean and standard deviation.