Function to compute the AOV

Function to compute the analysis of variance of ILS data, taking into account the laboratories and material factors.

Usage

lab.aov(x, ...)

# S3 method for default
lab.aov(
  x,
  var.index = 1,
  replicate.index = 2,
  material.index = 3,
  laboratory.index = 4,
  data.name = NULL,
  level = 0.95,
  plot = FALSE,
  pages = 0,
  ...
)

# S3 method for lab.qcdata
lab.aov(x, level = 0.95, plot = FALSE, pages = 0, ...)

Arguments

x

Object lab.qcdata.

...

Arguments passed to or from methods.

var.index

Scalar with the column number corresponding to the observed variable (the critical to quality variable). Alternatively, a string with the name of a quality variable can be provided.

replicate.index

Scalar with the column number corresponding to the index each replicate.

material.index

Scalar corresponding to the replicated number.

laboratory.index

Scalar that defines the index number of each laboratory.

data.name

String specifying the name of the variable which appears on the plots. If name is not provided, it is retrieved from the object.

level

Requested confidence level (0.95 by default)

plot

If TRUE, confidence intervals are plot.

pages

By default 0, it indicates the number of pages over which to spread the output. For example, if pages=1, all terms will be plotted on one page with the layout performed automatically. If pages=0, one plot will be displayed by each tested material.

References

WHothorn T., Bretz, F., and Westfall, P. (2008), Simultaneous inference in general parametric models. Biometrical Journal, 50(3):346-363.

Heyden, Y., Smeyers-Verbeke, J. (2007), Set-up and evaluation of interlaboratory studies. J. Chromatogr. A, 1158:158-167.

Examples

if (FALSE) {
library(ILS)
data(Glucose)
Glucose.qcdata <- lab.qcdata(Glucose)
str(Glucose.qcdata)
lab.aov(Glucose.qcdata,level = 0.95, plot = TRUE, pages = 1)
}