modlist {qpcR} | R Documentation |
Simple function to create a list of nonlinear models from the columns of a qPCR dataframe.
Very handy if following functions should be applied to different qPCR models, i.e. by sapply
.
modlist(x, cols = NULL, fct = l4(), opt = FALSE, norm = FALSE, backsub = NULL, ...)
x |
a dataframe containing the qPCR data. |
cols |
the columns (runs) to be analyzed. If NULL , all runs will be considered. |
fct |
the function used for building the model, using the function lists from the 'drc' package. |
opt |
logical. Should model optimization take place? If TRUE , model selection is applied. |
norm |
logical. Should the raw data be normalized to within [0, 1] before model fitting? |
backsub |
background subtraction. If NULL , not applied. Otherwise, a numeric sequence such as 1:10 . See 'Details'. |
... |
other parameters to be passed to mchoice . |
For a more detailed description of the functions see 'l4()' and 'pcrbatch'.
A list with each item containing the model from each column. A 'names' attribute containing the column name is attached to each model.
Andrej-Nikolai Spiess
### calculate efficiencies for each run in ### the 'reps' data ### subtract background using the first 8 cycles ml <- modlist(reps, fct = l5(), backsub = 1:8) effs <- sapply(ml, function(x) efficiency(x)$eff) print(effs) ### 'crossing points' for the first 3 runs ### using best model from Akaike weights and normalization ml <- modlist(reps, 2:4, fct = l4(), opt = TRUE, norm = TRUE, crit = "weights") cps <- sapply(ml, function(x) efficiency(x)$cpD2) print(cps)