qpcR: pcrsim – R documentation

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pcrsim

Simulation of sigmoidal qPCR data with goodness-of-fit analysis

Description

Simulated sigmoidal qPCR curves are generated from an initial model to which some user-defined homoscedastic or heteroscedastic noise is added. One or more models can then be fit to this random data and goodness-of-fit (GOF) measures are calculated for each of the models. This is essentially a Monte-Carlo approach testing for the best model in dependence to some noise structure in sigmodal models.

Usage

pcrsim(object, nsim = 100, error = 0.02, 
       errfun = function(y) 1, plot = TRUE,
       fitmodel = NULL, select = FALSE, 
       statfun = function(y) mean(y, na.rm = TRUE),
       PRESS = FALSE, ...)

Arguments

`object`	an object of class 'pcrfit.
`nsim`	the number of simulated curves.
`error`	the gaussian error used for the simulation. See 'Details'.
`errfun`	an optional function for the error distribution. See 'Details'.
`plot`	should the simulated and fitted curves be displayed?
`fitmodel`	a model or model list to test against the initial model.
`select`	if `TRUE`, a matrix is returned with the best model in respect to each of the GOF measures.
`statfun`	a function to be finally applied to all collected GOF measures, default is the average.
`PRESS`	logical. If set to `TRUE`, the computationally expensive `PRESS` statistic will be calculated.
`...`	other parameters to be passed on to `plot` or `pcrfit`.

Details

The value defined under error is just the standard deviation added plainly to each y value from the initial model, thus generating a dataset with homoscedastic error. With aid of errfun, the distribution of the error along the y values can be altered and be used to generate heteroscedastic error along the curve, i.e. as a function of the magnitude.

Example:
errfun = function(y) 1
same variance for all y, as is.

errfun = function(y) y
variance as a function of the y-magnitude.

errfun = function(y) 1/y
variance as an inverse function of the y-magnitude.

For the effect, see 'Examples'.

Value

A list containing the following items:

`cyc`	same as in 'arguments'.
`fluoMat`	a matrix with the simulated qPCR data in columns.
`coefList`	a list with the coefficients from the fits for each model, as subitems.
`gofList`	a list with the GOF measures for each model, as subitems.
`statList`	a list with the GOF measures summarized by `statfun` for each model, as subitems.
`modelMat`	if `select = TRUE`, a matrix with the best model for each GOF measure and each simulation.

Author(s)

Andrej-Nikolai Spiess

Examples

## Generate initial model.
m1 <- pcrfit(reps, 1, 2, l4)

## Simulate homoscedastic error
## and test l4 and l5 on data.
res1 <- pcrsim(m1, error = 0.2, nsim = 20, 
               fitmodel = list(l4, l5))

## Not run: 
## Use heteroscedastic noise typical for 
## qPCR: more noise at lower fluorescence.
res2 <- pcrsim(m1, error = 0.01, errfun = function(y) 1/y,
              nsim = 20, fitmodel = list(l4, l5, l6))

## Get 95% confidence interval for 
## the models GOF in question (l4, l5, l6).
res3 <- pcrsim(m1, error = 0.2, nsim = 20, fitmodel = list(l4, l5, l6),
              statfun = function(y) quantile(y, c(0.025, 0.975)))
res3$statList  

## Count the selection of the 'true' model (l4)
## for each of the GOF measures,
## use PRESS statistic => SLOW!
## BIC wins!!
res4 <- pcrsim(m1, error = 0.05, nsim = 10, fitmodel = list(l4, l5, l6),
               select = TRUE, PRESS = TRUE)
apply(res4$modelMat, 2, function(x) sum(x == 1))

## End(Not run)

qpcR

Modelling and Analysis of Real-Time PCR Data

v1.4-1

GPL (>= 2)

Authors

Andrej-Nikolai Spiess <a.spiess@uke.uni-hamburg.de>

Initial release

2018-05-29