Description Usage Arguments Details Value Author(s) References See Also Examples
Produces an estimate of the covariance matrix of the parameter estimates for a fitted mixture of linear regressions, by inverting the observed Fisher information matrix.
1 2 |
object |
Object describing the fitted mixture of regressions, as returned
by |
useMC |
Text string specifying whether to call upon a Monte Carlo procedure if there are problems with the “analytic” procedure (i.e. if the calculated observed Fisher information is singular) or to simply give up and throw an error. |
nsim |
Positive integer scalar specifying how many simulated samples to generate for the purpose of calculating the Monte Carlo estimate of the covariance matrix. |
progRep |
Logical scalar; should nominal “progress reports” be issued during the simulation? |
seed |
Integer scalar. The seed for the random number generator used to
produce random samples from which to calculate the Monte Carlo
based estimate of the covariance matrix. If this argument is
not supplied, then it is randomly sampled from |
... |
Optional arguments |
If different variances are allowed amongst the components (i.e. if
object$eqVar
is FALSE
) then the parameters are taken
in the order beta.1, sigsq.1, lambda.1, ..., beta.K, sigsq.K for
a K component model — lambda.K is redundant and hence omitted.
If equal variances are assumed, the parameters are taken in the
order beta.1, lambda.1, ..., beta.K, sigsq.
In the foregoing beta refers to the linear coefficients, sigsq to the variance or variances, and lambda to the mixing probabilities.
The estimated covariance matrix. If the Monte Carlo method
was applied then this matrix has an attribute "seed"
.
This attribute will be the value of the seed
argument if
this was supplied, otherwise it is the randomly generated
replacement for this argument.
Rolf Turner r.turner@auckland.ac.nz
T. Rolf Turner (2000). Estimating the rate of spread of a viral infection of potato plants via mixtures of regressions. Applied Statistics 49 Part 3, pp. 371 – 384.
T. A. Louis (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, series B 44 pp. 226 – 233.
ncMcTest()
, cband()
mixreg()
, plot.cband()
,
plot.mixresid()
, qqMix()
,
residuals.mixreg()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Aphids.
fita <- mixreg(plntsInf~aphRel,ncomp=2,seed=42,data=aphids)
cMafi <- covMix(fita)
## Not run:
cMaMC <- covMixMC(fita)
## End(Not run)
# Kilns.
thStrt <- list(
list(beta=c(26.07,48808),sigsq=1.1573,lambda=0.33333333),
list(beta=c(23.48,32387),sigsq=1.8730,lambda=0.33333333),
list(beta=c(-0.0597,20760),sigsq=0.2478,lambda=0.33333333)
)
fitk <- mixreg(y ~ x,ncomp=3,data=kilnAoneOut,thetaStart=thStrt)
## Not run:
cMkfi <- covMix(fitk)
cMkMC <- covMixMC(fitk)
cMkMCs <- covMixMC(fitk,semiPar=TRUE)
## End(Not run)
|
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