The qpcR Package - NexTag Supports Open Source Initiatives

6 calibArgumentsdatacyc.colf.colgroupsthreshmethoddilpredfctplotconf.levopt.minopt.plota dataframe containing the qPCR data.the column containing the cycle numbers. Defaults to the first column.the columns containing the raw fluorescence qPCR data.a factor defining replicates of the qPCR runs.the fluorescence value from which the threshold cycles are defined.the method for defining the threshold cycles. Either ’var’ for a user-defined valueor ’cpD2’ (overrides ’var’) for estimation from the second derivative maximumof the first run.a vector with the concentration (or dilution) values corresponding to the qPCRruns.a vector of threshold cycles whose concentrations are to be predicted.the model used for the threshold cycle estimation. Any sigmoidal model in the’drc’ package. Defaults to ’l5()’.logical. Defaults to TRUE. If FALSE, the values are returned.the p-value for the confidence interval. Defaults to 0.05, can be omitted withNULL.numeric. The minimal value for the regression optimization. Must be tweakedby the user.logical. If TRUE, the Fluorescence vs. AIC plot is displayed (with the best valuehighlighted).ValueA list with the following components:ctr.squaredthe calculated threshold cycles.The r-square of the linear fit.adj.r.squaredThe adjusted r-square of the linear fit.AICEffpredthresh.optAIC.optthe AIC value of the linear fit.the qPCR efficiency as calculated from the slope of the dilution curve.the predicted values for the data given in ’pred’.the optimal threshold value.the minimal AIC of the regression optimization.Author(s)Andrej-Nikolai Spiess