propagate (version 1.0-6)

mixCov: Aggregating covariances matrices and/or error vectors into a single covariance matrix

Description

This function aggregates covariances matrices, single variance values or a vector of multiple variance values into one final covariance matrix suitable for propagate.

Usage

mixCov(...)

Arguments

...

either covariance matrices, or a vector of single/multiple variance values.

Value

The aggregated covariance matrix.

Details

'Mixes' (aggregates) data of the following types into a final covariance matrix: 1) covariance matrices \(\mathbf{\Sigma}\) that are already available. 2) single variance values \(\sigma^2\). 3) a vector of variance values \(\sigma^2_1, \sigma^2_2, ..., \sigma^2_n\).

This is accomplished by filling a \(m_1 + m_2 + \ldots + m_n\) sized square matrix \(\mathbf{C}\) succesively with elements \(1 \ldots m_1, m_1 + 1 \ldots m1 + m2, \ldots, m_n + 1 \ldots m_n + m_{n + 1}\) with either covariance matrices at \(C_{m_n + 1 \ldots m_n + m_{n + 1}, m_n + 1 \ldots m_n + m_{n + 1}}\) or single variance values on the diagonals at \(C_{m_n, m_n}\).

References

Evaluation of measurement data - Guide to the expression of uncertainty in measurement. JCGM 100:2008 (GUM 1995 with minor corrections). http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf.

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. http://www.bipm.org/utils/common/documents/jcgm/JCGM_101_2008_E.pdf.

Examples

# NOT RUN {
#######################################################
## Example in Annex H.4.1 from the GUM 2008 manual
## (see 'References'), measurement of activity.
## This will give exactly the same values as Table H.8.
data(H.4)
attach(H.4)
T0 <- 60
lambda <- 1.25894E-4
Rx <- ((Cx - Cb)/60) * exp(lambda * tx)
Rs <- ((Cs - Cb)/60) * exp(lambda * ts)

mRx <- mean(Rx)
sRx <- sd(Rx)/sqrt(6)
mRx
sRx

mRs <- mean(Rs)
sRs <- sd(Rs)/sqrt(6)
mRs
sRs

R <- Rx/Rs
mR <- mean(R)
sR <- sd(R)/sqrt(6)
mR
sR

cor(Rx, Rs)

## Definition as in H.4.3.
As <- c(0.1368, 0.0018)
ms <- c(5.0192, 0.005)
mx <- c(5.0571, 0.001)

## We have to scale Rs/Rx by sqrt(6) to get the 
## corresponding covariances.
Rs <- Rs/sqrt(6)
Rx <- Rx/sqrt(6)

## Here we create an aggregated covariance matrix
## from the raw and summary data.
COV1 <- cov(cbind(Rs, Rx))
COV <- mixCov(COV1, As[2]^2, ms[2]^2, mx[2]^2)
COV

## Prepare the data for 'propagate'.
MEANS <- c(mRs, mRx, As[1], ms[1], mx[1])
SDS <- c(sRs, sRx, As[2], ms[2], mx[2])
DAT <- rbind(MEANS, SDS)
colnames(DAT) <- c("Rs", "Rx", "As", "ms", "mx")

## This will give exactly the same values as 
## in H.4.3/H.4.3.1.
EXPR <- expression(As * (ms/mx) * (Rx/Rs))
RES <- propagate(EXPR, data = DAT, cov = COV, nsim = 100000)
RES
# }