qpcr_nlme_formula: 5-parameter logistic mixed model analysis of qpcr data
In daniel-gerhard/qpcrnlme: Nonlinear mixed model analysis of RT-PCR data

Description Usage Arguments Value Author(s) See Also Examples

Fits a nonlinear mixed model to qpcr fluorescence data, allowing for one factorial ANOVA designs with several levels for a single treatment factor at multiple genes. Marginal c(t) values and corresponding delta delta c(t) values are calculated.

1 2	qpcr_nlme_formula(response, cycle, gene, trtformula, brep, well, data, newdata, cutoff, nGQ=5, verbose=TRUE)

`response`	Name of a column in data, denoting a numeric response vector with fluorescence observations.
`cycle`	Name of a column in data, denoting a numeric response vector with cycle numbers.
`gene`	Name of a column in data, denoting a factor coding for different genes.
`trtformula`	One-sided formula to model treatment effects
`brep`	Name of a column in data, denoting a factor coding for biological replications.
`well`	Name of a column in data, denoting a factor coding for technical replications.
`data`	A data.frame object.
`newdata`	A data.frame object with covariate values at which cycle thresholds are estimated.
`cutoff`	cutoff value t to define c(t) cycle thresholds.
`nGQ`	Number of nodes and weights for Gaussian Quadrature
`verbose`	Some text output during calculation...

An object of class ddct.

Daniel Gerhard <gerhard@biostat.uni-hannover.de>

nlme

## Not run: 
data(pt6c)
qpcr_nlme_formula(response="Fluorescence", cycle="Cycle", gene="Target", 
  trtformula=~ poly(Time, 4, raw=TRUE), brep="Content", well="Well", data=pt6c, 
  newdata=data.frame(Time=seq(1,8, length=5)), cutoff=100, nGQ=5, verbose=TRUE)

## End(Not run)