lm.coefs: Compute linear model coefficients

In chipPCR: Toolkit of Helper Functions to Pre-Process Amplification Data

Description

Computes linear model using the robust linear regression.

Usage

lm.coefs(x, y, method.reg)

Arguments

x	a vector of ordinate values.
y	a vector of abscissa values.
method.reg	defines the method ("rfit", "lmrob", "rq", "least") for the linear regression.

Details

lm.coefs is a convenient wrapper around few functions performing normal (least squares) and robust linear regression. If the robust linear regression is impossible, lm.coefs will give a warning and perform linear regression using the least squares method. This function can be used to calculate the background of an amplification curve. The coefficients of the analysis can be used for a trend based correction of the entire data set.

Value

A data frame with one column and two rows representing coefficients of the linear model.

Author(s)

Stefan Roediger, Michal Burdukiewicz

See Also

rq, rfit, lm, lmrob

Examples

plot(VIMCFX96_69[, 1], VIMCFX96_69[, 2], type = "l", xlab = "Cycle", 
     ylab = "Fluorescence")
rect(1,0,10,5000)
method <- c("lmrob", "rq", "least", "rfit")
for (i in 1:4) {
  tmp <- lm.coefs(VIMCFX96_69[1:10, 1], VIMCFX96_69[1:10, 2], 
		  method.reg = method[i])
  abline(a = tmp[1, 1], b = tmp[2, 1], col = i + 1, lwd = 1.5)
}
legend(2, 3000, c("Data", "lmrob", "rq", "least", "rfit"), lty = 1, col = 1:5, 
       cex = 1.5)

chipPCR documentation built on March 5, 2021, 9:06 a.m.