qpcR (version 1.3-0)

pcrfit: Function for qPCR model fitting

Description

This is the main workhorse function of the qpcR package that fits one of the available models to qPCR data using nonlinear least-squares fitting from nls, with sensible starting parameters obtained from either nls.lm (default) or optim.

Usage

pcrfit(data, cyc = 1, fluo, model = l4, do.optim = TRUE,  
       opt.method = "LM", nls.method = "port", 
       start = NULL, robust = FALSE, control = nls.control(), 
       weights = NULL, ...)

Arguments

data

the name of the dataframe containing the qPCR runs.

cyc

the column containing the cycle data. Defaults to 1.

fluo

the column containing the raw fluorescence data of the run.

model

the model to be used for the analysis. Defaults to l4.

do.optim

if FALSE, refinement of starting values by nls.lm or optim will be skipped.

opt.method

one of the available refinement methods. See 'Details'.

nls.method

one of the available methods in nls. Default is "port", which works quite well.

start

a vector of starting values that can be supplied externally.

robust

logical. If TRUE, robust nonlinear regression is used. See 'Details'.

control

an optional list of control settings for nls or qpcR:::rnls.

weights

a vector with same length as data containing the weights for the nonlinear fit.

...

other parameters to be passed to optim, genoud or nls.

Value

• A model of class 'nls' and 'pcrfit' with the following items attached:
• DATA
  the initial data used for fitting.
• MODEL
  the model used for fitting.
• call2
  the call to pcrfit.
• parMat
  the trace of the starting values for each applied method. Can be used to track problems.
• opt.method
  the parameter opt.method.

encoding

latin1

Details

The fitting procedure works as follows (hopefully ensuring maximum safeness against convergence errors): 1) Approximate starting values are acquired from model$ssfct. 2) Starting values are refined by any of the methods available in optim or the Levenberg-Marquardt algorithm (nls.lm). The opt.methods can be combined to tweak the robustness, whereby the starting parameters are passed to each succeeding method, i.e. rep("Nelder", 5) will do 5 successive Nelder-Mead optimisations or c("LM", "Nelder") will pass the starting values from "LM" to "Nelder". Levenberg-Marquardt ("LM") is very fast and reliable in many scenarios and is thus the default. 3) One of the possible methods from nls is then applied with the starting values obtained from 2). This function is to be used at the single run level. Otherwise use pcrbatch or modlist. The output from the optim methods is checked by ensuring all eigenvalues from the hessian are positive, otherwise a notice will occur. If robust = TRUE, robust nonlinear fitting will be used. To do this, the internal function qpcR:::rnls is called which is a modification of the nlrob function of the 'robustbase' package. Modifications were done such that all available generic functions for objects of class 'nls' can be used on the output of qpcR:::rnls, such as predict, confint etc.

References

Bioassay analysis using R. Ritz C & Streibig JC. J Stat Soft (2005), 12: 1-22. A Method for the Solution of Certain Problems in Least Squares. K. Levenberg. Quart Appl Math (1944), 2: 164-168. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. D. Marquardt. SIAM J Appl Math (19639), 11: 431-441.

Examples

## simple l4 fit of F1.1 of the 'reps' dataset
pcrfit(reps, 1, 2, l4) 

## using BFGS and Nelder from 'optim'
pcrfit(reps, 1, 2, l5, opt.method = c("BFGS", "Nelder"))

## skip 'optim' method and supply
## own starting values
pcrfit(reps, 1, 2, l4, do.optim = FALSE, start = c(-5, -0.05, 11, 16)) 

## make a robust model
pcrfit(reps, 1, 2, l4, robust = TRUE)