| mandel.h | R Documentation | 
mandel.h calculates Mandel's h statistics for replicate observations. 
Mandel's h is an indication of relative deviation from the mean value.
	mandel.h(x, g = NULL, m = NULL, na.rm = T, rowname = NULL, 
			method=c("classical", "robust"), n = NA, ...)
	## Default S3 method:
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, ...)
| x | An R object (see Details below), which contains replicate observations or, 
if  | 
| g | A primary grouping factor, usually corresponding to Laboratory in an
inter-laboratory study. If not present,  | 
| m | A secondary grouping factor, usually corresponding to test item 
or measured quantity.  | 
| na.rm | A logical value indicating whether 'NA' values should be
stripped before the computation proceeds. Passed to functions 
such as  | 
| rowname | A single character label for the primary grouping factor (e.g. "Lab", "Organisation"). | 
| method | Character scalar giving the calculation method.  | 
| n | scalar number of observations per group. Required only if  | 
| ... | Additional parameters passed to  | 
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 $measurand. 
The returned object includes a label ('grouped.by') for the primary grouping factor. 
For the 'ilab' method, this is "Organisation". For other methods, If rowname is 
non-null, rowname is used. If rowname is NULL, the default is deparse(substitute(g));
if g is also NULL or missing, "Row" is used.
If method="robust", Mandel's h is replaced by a robust z score calculated by 
replacing \bar{x_j} and s_j with the robust estimates of location and scale 
obtained using Huber's estimate with tuning constant k set to 1.5 (unless otherwise 
specified in ...). 
mandel.h returns an object of class "mandel.kh", which is  a data frame consisting
of the required Mandel's statistics and in which each row corresponds to a level of g
and each column to a level of m or (if x was a matrix or data frame) to the 
corresponding column in x. In addition to the class, the object has attributes:
"h" or "k"
Character scalar giving the label used for the grouping 
factor g; see Details above for the defaults.
Number of observations per group (n if specified
S Ellison s.ellison@lgcgroup.com
Accuracy (trueness and precision) of measurement methods and results – Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method. ISO, Geneva (1994).
mandel.k, mandel.kh;  
pmandelh, pmandelk  for probabilities, quantiles etc.;
plot.mandel.kh, barplot.mandel.kh for plotting methods.
	data(RMstudy)
	#Data frame examples: note no secondary grouping factor
	h <- with(RMstudy, mandel.h(RMstudy[2:9], g=Lab))
	plot(h, las=2)
	#Vector variant
	RMstk <- stack(RMstudy[,2:9])
	names(RMstk) <- c("x", "meas")
		#names replace 'values' and 'ind'
	RMstk$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)
	
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