compute.fit: Provided a standard curve, predict concentration from Ct
In NCI-CGR/cgrtelomeres: Automated Processing of Telomere qPCR Data

Description Usage Arguments Details Value See Also Examples

compute.fit takes a fit transformed exponential regression model and uses its coefficients to predict best fit values for provided Cts, given boundaries on valid estimates defined by the standards.

1	compute.fit(fit.model, predictors, standard.limits)

`fit.model`	Object of class `lm` from which regression coefficients are extracted.
`standard.limits`	Numeric vector of length 2, presumably the output of `range`
`cycle.thresholds`	Numeric vector of Ct values to convert to best fit concentrations.

The transformation applied to the data assumes that regression has been performed on log2-transformed predictors (concentrations) and outcomes (Cts).

Numeric vector of best fit concentrations, with out of bounds values set to NA.

create.analysis() for the standard curve calculation; lm() for linear regression.

fixed.concentrations <- c(4, 1.6, 0.64, 0.256, 0.1024, 0.041)
experimental.cts <- runif(20, 1, 38)
fit.model <- lm(log2(experimental.cts[1:6]) ~ log2(fixed.concentrations))
fit.values <- compute.fit(fit.model, experimental.cts, fixed.concentrations)