modlist {qpcR} | R Documentation |
Essential 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, cyc = 1, fluo = NULL, model = l4, opt = FALSE, norm = FALSE, backsub = NULL, opt.method = "LM", nls.method = "port", sig.level = 0.05, crit = "ftest", ...)
x |
a dataframe containing the qPCR data. |
cyc |
the column containing the cycle data. Defaults to first column. |
fluo |
the column(s) (runs) to be analyzed. If NULL , all runs will be considered. |
model |
the model to be used. |
opt |
logical. Should model selection be applied? |
norm |
logical. Should the raw data be normalized within [0; 1] before model fitting? |
backsub |
background subtraction. If NULL , not applied. Otherwise, a numeric sequence such as 1:10 . See 'Details' in pcrbatch . |
opt.method |
see pcrfit . |
nls.method |
see pcrfit . |
sig.level |
see mselect . |
crit |
see mselect . |
... |
other parameters to be passed to pcrfit or mselect . |
In case of unsuccessful model fitting, the run is skipped and the next run is analyzed.
A list with each item containing the model from each column. A 'names' item containing the column name is attached to each model.
Andrej-Nikolai Spiess
pcrbatch
for batch analysis using different methods.
## calculate efficiencies for each run in ## the 'reps' data ## subtract background using the first 8 cycles ml <- modlist(reps, model = l5, backsub = 1:8) sapply(ml, function(x) efficiency(x, plot = FALSE)$eff) ## 'crossing points' for the first 3 runs (normalized) ## and using best model from Akaike weights ml <- modlist(reps, 1, 2:4, model = l5, opt = TRUE, norm = TRUE, crit = "weights" ) sapply(ml, function(x) efficiency(x, plot = FALSE)$cpD2)