regression.delta.Cq: This function will build a regression model on the delta Cq...
In LucasFVoges/DoubleqpcR: DMAS-qPCR data analysis

regression.delta.Cq

R Documentation

This function will build a regression model on the delta Cq values. Plot and cross validation is included.

Description

The model and prediction with investR package are reported. A CrossValidation for the model is possible with caret. The cross validation here is a basic wrapper for caret package. If more detailed CV is wanted, the analysis should be made separately with caret.

Usage

regression.delta.Cq(
  CqType = "SD",
  linSqrtTrans = FALSE,
  method = "c",
  fit = "linear",
  rawPolynomials = FALSE,
  cv = FALSE,
  cv.seed = sample(1:100, 1),
  cv.method = "LGOCV",
  cv.p = 0.5,
  cvComplete = FALSE,
  plot = TRUE,
  cv.plot = FALSE
)

Arguments

`CqType`	this is the Cq value type that should be used.
`linSqrtTrans`	Will transform the values to linearise the values! this is basically a shifted square root representation. The parameter fit should be linar as well.
`method`	method for generating the delta Cq values (see delta.Cq.data())
`fit`	model for lm() function. "linear", "poly3", "poly4".
`cv`	should a cross validation be made? With caret!
`cv.seed`	seed for cv
`cv.method`	The method for cv from caret package
`cv.p`	percentage of training data (0 to 1)
`cvComplete`	should a cross validation be made with omitting one concentration completely? This will run separately from caret cv.
`plot`	plot the data with plotfit() method from investR with std. settings (for more options use plotfit() separately)
`cv.plot`	plot the data with plotfit() for the cvComplete cross validation.