Description Usage Arguments Value References Examples
Uncertainty propagation by Monte Carlo (GUM-Supp1 [1]).
Both gumS1(adapt=TRUE)
and gumS2
provide adaptative procedures.
gumS1
implements the code proposed in GUM-Supp1 which checks the
convergence of the estimators by blocks.
gumS2
implements the two-stage Stein procedure [2], which has
been shown to have a less erratic stopping criterion than gumS1
.
By default, gumS1
is non-adaptative and performs the user-specified
number of simulations.
1 2 3 4 5 6 7 8 | gumS1(fExpr, x.mu, x.u, x.pdf, x.df, x.cor = diag(length(x.mu)),
x.cov = NULL, nrun = 1000, adapt = FALSE, ndig = 1, p = 0.95,
delFrac = 1, stdev = TRUE, interval = TRUE, silent = FALSE,
nrunMax = 1e+06)
gumS2(fExpr, x.mu, x.u, x.pdf, x.df, x.cor = diag(length(x.mu)),
x.cov = NULL, nrun = 100, h1 = 30, ndig = 1, p = 0.95,
silent = FALSE, nrunMax = 1e+06)
|
fExpr |
An expression or a function object. |
x.mu |
Named vector of mean values
with names compatible with |
x.u |
Named vector of standard uncertainty values
(one of { |
x.pdf |
Named vector of pdf types (see |
x.df |
Named vector of degrees of freedom for |
x.cor |
Named correlation matrix between model parameters. |
x.cov |
Named variance/covariance matrix
between model parameters (one of { |
nrun |
Number of runs in each sample packet. |
adapt |
Flag to use the sequential adaptive method. |
ndig |
Number of significant figures to converge. |
p |
Coverage of confidence interval. |
delFrac |
Multiplicative factor on numerical tolerance. For compatibility with examples in GUM-Supp1 [1]. #' |
stdev |
Flag for adaptative method to converge standard
deviation ( |
interval |
Flag to converge confidence interval ( |
silent |
Flag to run without printout. |
nrunMax |
Maximum number of runs allowed. |
h1 |
Number of packets in first step of |
A list containing:
y.mu |
mean value of model |
y.u |
standard uncertainty of model |
y.low |
lower limit of confidence interval |
y.high |
upper limit of confidence interval |
p |
coverage of confidence interval (same as input) |
X |
(matrix) sample of inputs used to converge statistics |
Y |
(vector) sample of outputs corresponding to X |
[1] Evaluation of measurement data - Supplement 1 to the "Guide to the expression of uncertainty in measurement" - Propagation of distributions using a Monte Carlo method. JCGM 101:2008. PDF
[2] G. W\"ubbeler, P. M. Harris, M. G. Cox and C. Elster (2010) A two-stage procedure for determining the number of trials in the application of a Monte Carlo method for uncertainty evaluation. Metrologia 47:317. CrossRef
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