construct.ilab(org, item, measurand, x, u, df, k, U, U.lower, U.upper,
distrib=NULL, distrib.pars=NULL, study=NA, title=NA, p=0.95, ...)
length(x)
or a named list
of names of distribution functions associated with u
. If a character vector,
distrib
is recycled to length length(x)
.u
to be passed to the relevant distribution function.
If distrib
is present but distrib.pars
is not, an attempt is made
tk
. Used only to set a default value
for df
when distrib
indicates a t-distribution and df
is
unspecified.NA
if not provided.
k numeric vector of coverage factors. The coverage factor is the
factor multiplying u to obtain U.
U numeric or character vector of expanded uncertainties or confidence
interval half-widths. U
is coerced to numeric but may include
a character representation of interval limits; see Details.
U.lower, U.upper numeric vectors of lower and upper limits for the confidence
interval around x.
study Identifier for study groups (see Arguments above).
... Other grouping factors (supplied in construct.ilab
)
which can be used for sub-categorisation.
}u
.U
is a character vector, it may contain character representations of range.
Two forms are permitted:
[object Object],[object Object]
If distrib.pars
is missing, an attempt is made to deduce appropriate
distribution parameters from x
, u
, df
and distrib
.
In doing so, the following assumptions and values apply for the respective distributions:
[object Object],[object Object],[object Object],[object Object]
In addition, if distrib
contains "t"
or "t.scaled"
, and
df
is NA
, the corresponding degrees of freedom are chosen based on
k
and p
.print.ilab
, subset.ilab
, plot.ilab
data(Pb)
construct.ilab(org=Pb$lab, x=Pb$value, measurand="Pb", item="none",
u=Pb$u, k=Pb$k, U=Pb$U, title=c("CCQM K30", "Lead in wine"),
method=Pb$method)
#Illustrate default for U and automatic distrib.pars
construct.ilab(org=Pb$lab, x=Pb$value, measurand="Pb", item="none",
u=Pb$u, k=Pb$k, distrib="norm")
construct.ilab(org=Pb$lab, x=Pb$value, measurand="Pb", item="none",
u=Pb$u, k=Pb$k, distrib="t.scaled")
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