qpcR (version 1.1-8)

modlist: Create nonlinear models from a dataframe and coerce them into a list

Description

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.

Usage

modlist(x, cols = NULL, fct = l4(), opt = FALSE, norm = FALSE, 
          backsub = NULL, ...)

Arguments

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.

Value

• A list with each item containing the model from each column. A 'names' attribute containing the column name is attached to each model.

Details

For a more detailed description of the functions see 'l4()' and 'pcrbatch'.

Examples

### 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)