normalise: Perform a normalisation (calibration) procedure on the...

In AFLP: Analysis of AFLP data

Description

Calculates normalised fluorescence values by taking into account the differences between replicates nested in specimens, plates and markers. This is based on a linear mixed model. The form of the model is automatically chosen based on the number of replicates, specimens, plates and marker. The same form is apply on each primer combination separately.

Usage

  normalise(data, output = c("screen", "tex", "none"),
    path = NULL, device = "pdf", SpecimenEffect = FALSE,
    level = 0.99,
    transformation = c("log", "logit", "none"))

Arguments

data	An AFLP object
output	Which output is required. "screen" put QQ-plots of the random effects, QQ-plots of the residuals and possible outliers on the screen. "tex" givens the same information but saves the QQ-plots to files and report LaTeX code to include the information in a LaTeX document.
path	the path where the figures are saved. Only used if output = "tex". Defaults to NULL, which is the working directory.
device	the device to which the figures are saved. See ggsave for the available devices. Only used if output = "tex". Defaults to "pdf".
SpecimenEffect	Add a random effect of the specimens to the model. Defaults to FALSE.
level	The level of the prediction intervals. Used to determine possible outliers in the QQ-plots. Defaults to 0.99.
transformation	Which transformation to use on the raw fluorescence data. Valid choises are "log", "logit" and "none". Defaults to "log". "log" implies the use of log(), hence no zero fluorescences are allowed. With "logit", the raw fluorescence is first devided by the smallest power of 2, which is still larger than the largest raw fluorescence. Then a logit transformation is applied (log(p/(1 - p))).

Value

• data The altered data object that was put into the function. Outliers in the outliers slot are removed from the Fluorescence slot. The Normalised and Score columns from the Fluorescence slot are overwritten. Normalised holds the new normalised values. Score with be fill with NA.

• outliers An AFLP.outlier object with all possible outliers detected by this procedure.

Author(s)

Thierry Onkelinx [e-mail], Paul Quataert

See Also

classify, ggsave

Examples

data(Tilia)
 nOutput <- normalise(Tilia, output = "none")

AFLP documentation built on May 2, 2019, 6:13 p.m.