mandel.k {metRology}

Calculate Mandel's k statistics for replicate observations

Description

mandel.k calculates Mandel's k statistics for replicate observations. Mandel's k an indicator of precision compared to the pooled standard deviation across all groups.

Usage

	mandel.k(x, g = NULL, m = NULL, na.rm = T, rowname = NULL, 
			method=c("classical", "robust"), n = NA, ...)

	## Default S3 method:
mandel.k(x, g = NULL, m = NULL, na.rm = T, rowname = NULL, 
			method=c("classical", "robust"), n = NA, ...)

	## S3 method for class 'ilab'
mandel.k(x, g = NULL, m = NULL, na.rm = T, rowname = NULL, 
			method=c("classical", "robust"), n = NA, ...)

Arguments

x

An R object (see Details below), which contains replicate observations or, if g is absent, means or standard deviations.

g

A primary grouping factor, usually corresponding to Laboratory in an inter-laboratory study. If not present, x is taken as a set of means or standard deviations (depending on whether type is "h" or "k".

m

A secondary grouping factor, usually corresponding to test item or measured quantity. m is ignored if x has more than one column.

na.rm

A logical value indicating whether 'NA' values should be stripped before the computation proceeds. Passed to functions such as mean and sd.

rowname

A single character label for the primary grouping factor (e.g. "Lab", "Organisation").

method

Character scalar giving the calculation method. "classical" gives the traditional calculation; "robust" gives a robust variant (see Details).

n

scalar number of observations per group. Required only if x consists of calculated standard deviations.

...

Additional parameters passed to other methods. Currently not implemented.

Details

mandel.k is a convenience wrapper for mandel.kh(..., type="k"). It is generic, with methods for numeric vectors, arrays, data frames, matrices and objects of class 'ilab'. All parameters are passed to mandel.kh.

Mandel's k is an indicator of relative dispersion for grouped sets of observations. Given a set of observations x_{ijl} where i, j, l denotes observation l, l=1, 2, ... n for measurand or test item j and group (usually laboratory) i, i=1, 2, ... p, Mandel's k is given by:

k=\sqrt{\frac{s_{ij}^2}{\sum_{i=1}^p{s_{ij}^2/p}}}

where s_{ij} is the standard deviation of values x_{ijk} over k=1, 2, ..., n.

If x is a vector, one-dimensional array or single-column matrix, values are aggregated by g and, if present, by m. If x is a data frame or matrix, each column is aggregated by g and m silently ignored if present. In all cases, if g is NULL or missing, each row (or value, if a vector) in x is taken as a pre-calculated mean (for Mandel's h) or standard deviation (for Mandel's k).

If x is an object of class 'ilab', g defaults to '$org' and m to $measurand.

The returned object includes a label ('grouped.by') for the primary grouping factor. For the 'ilab' method, this is "Organisation". For other methods, If rowname is non-null, rowname is used. If rowname is NULL, the default is deparse(substitute(g)); if g is also NULL or missing, "Row" is used.

If method="robust", Mandel's k is calculated by replacing the classical pooled standard deviation with the robust pooled standard deviation calculated by algorithm S (see algS).

Value

mandel.k returns an object of class "mandel.kh", which is a data frame consisting of the required Mandel's statistics and in which each row corresponds to a level of g and each column to a level of m or (if x was a matrix or data frame) to the corresponding column in x. In addition to the class, the object has attributes:

'mandel.type'

"h" or "k"

'grouped.by'

Character scalar giving the label used for the grouping factor g; see Details above for the defaults.

'n'

Number of observations per group (n if specified

Author(s)

S Ellison [e-mail]

References

Accuracy (trueness and precision) of measurement methods and results – Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method. ISO, Geneva (1994).

See Also

mandel.h, mandel.kh; pmandelh, pmandelk for probabilities, quantiles etc.; plot.mandel.kh, barplot.mandel.kh for plotting methods.

Examples

	data(RMstudy)

	#Data frame examples: note no secondary grouping factor
	h <- with(RMstudy, mandel.k(RMstudy[2:9], g=Lab))
	plot(h, las=2)

	#Vector variant
	RMstk <- stack(RMstudy[,2:9])
	names(RMstk) <- c("x", "meas")
		#names replace 'values' and 'ind'
	RMstk$Lab <- rep(RMstudy$Lab, 8)
	h2 <- with(RMstk, mandel.k(x, g=Lab, m=meas, rowname="Laboratory"))
		#Note use of rowname to override g
	plot(h2, las=2)
	
	#ilab method
	RM.ilab <- with(RMstk, construct.ilab(org=Lab, x=x, measurand=meas, 
		item=factor(rep("CRM", nrow(RMstk))) ) )

	plot(mandel.k(RM.ilab))
	
	#Robust variant
	krob <- with(RMstudy, mandel.kh(RMstudy[2:9], g=Lab, type="k", method="robust"))
	plot(krob, las=2)