table.Cq | R Documentation |
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
)
sample |
specify the sample from input.cq |
target |
the target genotype "genotype A". |
CqType |
wich Cq values should be used. This can be a vector! |
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 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 |
decimals for the resulting table (ignored for format = "data") |
format |
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. |
silent |
should status be printed? (mostly for outlier detection) |
returns a table with the delta Cq values with mean and standard deviation.
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