lab.aov {ILS} | R Documentation |
Function to compute the AOV
Description
Function to compute the analysis of variance of ILS data, taking into account the laboratories and material factors.
Usage
lab.aov(x, ...)
## Default S3 method:
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 class 'lab.qcdata'
lab.aov(x, level = 0.95, plot = FALSE, pages = 0, ...)
Arguments
x |
An object of class |
... |
Other arguments passed to or from methods. |
var.index |
A scalar with the column number corresponding to the observed variable (the critical to quality variable). Alternativelly can be a string with the name of the quality variable. |
replicate.index |
A scalar with the column number corresponding to the index each replicate. |
material.index |
A scalar corresponding to the replicated number. |
laboratory.index |
A scalar that defines the index number of each laboratory. |
data.name |
A string specifying the name of the variable which appears on the plots. If name is not provided, it is taken from the object given as data. |
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
## Not run:
library(ILS)
data(Glucose)
Glucose.qcdata <- lab.qcdata(Glucose)
str(Glucose.qcdata)
lab.aov(Glucose.qcdata,level = 0.95, plot = TRUE, pages = 1)
## End(Not run)