qpcR (version 1.3-2)

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

Description

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.

Usage

modlist(x, cyc = 1, fluo = NULL, model = l4, remove = FALSE, opt = FALSE, 
        norm = FALSE, backsub = NULL, opt.method =  "all", 
        nls.method = "all", sig.level = 0.05, crit = "ftest", 
        verbose = TRUE, ...)

Arguments

x

a dataframe containing the qPCR data or a single qPCR run of class 'pcrfit'.

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.

remove

logical. If TRUE, runs that failed to be fitted are not included in the output. See 'Details'.

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.

verbose

logical. If TRUE, fitting and tagging results will be displayed in the console.

...

other parameters to be passed to pcrfit or mselect.

Value

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

Details

In case of unsuccessful model fitting and if remove = FALSE (default), the original data is included in the output, albeit with no fitting information. This is useful since using plot.pcrfit on the 'modlist' shows the non-fitted runs. If remove = TRUE, the non-fitted runs are automatically removed and will thus not be displayed. See plot.pcrfit for examples.

See Also

pcrbatch for batch analysis using different methods.

Examples

## 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)

## convert a single run to a 'modlist'
m <- pcrfit(reps, 1, 2, l5)
ml <- modlist(m)