Performs a 4-PL regression of the standard values and converts the O.D. into concentration values.
elisa_analyse(.data, std_key = "^STD", dec = ".", var_in = "value", var_out = "estimate", .drop = FALSE) elisa_analyze(.data, std_key = "^STD", dec = ".", var_in = "value", var_out = "estimate", .drop = FALSE)
| .data | dataframe containing at least the value and id columns (with O.D. values and sample identifiers). |
|---|---|
| std_key | a character string specifying the common starting pattern of standard point ids (default = "^STD"). |
| dec | a character string used as a decimal separator for the encoded standard concentration values. |
| var_in | a character string used for the OD values (default = "value") |
| var_out | a character string used to name the output columns (default = "estimate") |
| .drop | Should input columns be dropped? (default = `FALSE`) |
| value | a character string specifying the column containing the O.D. values (default = "value"). |
A dataframe including the calculated concentrations, standard deviation and wether the value is in the range of the standard curve.
A complete example on how to perform an analysis can be found at https://github.com/koncina/elisar.
# NOT RUN { library(elisar) # Import file e <- read_plate("od_measure.xls") elisa_analyse(e) elisa_analyse(e, .drop = TRUE) # }