metRology (version 0.9-16.1)

mandel.h: Calculate Mandel's h statistics for replicate observations

Description

mandel.h calculates Mandel's h statistics for replicate observations. Mandel's h is an indication of relative deviation from the mean value.

Usage

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

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

	## S3 method for class 'ilab':
mandel.h(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 <

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 hubers when method="robust".

Value

• mandel.h 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: [object Object],[object Object],[object Object]

Details

mandel.h is a convenience wrapper for mandel.kh(..., type="h"). 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 h is an indicators of relative deviation 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 $h$ is given by: $$h=\frac{\bar{x_{ij}}-\bar{x_j}}{s_j}$$ where $s_j=\sqrt{\sum_{i=1}^p{\frac{(\bar{x_{ij}}-\bar{x_j})}{p-1}}}$ 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 $h$ is replaced by a robust z score calculated by replacing $\bar{x_j}$ and $s_j$ with the robust estimates of location and scale obtained using Huber's estimate with tuning constant k set to 1.5 (unless otherwise specified in ...).

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