mixnorm | R Documentation |
The normal mixture density and auxiliary functions.
mixnorm(..., sigma, param = c("ms", "mn"))
mn2norm(m, n, sigma, drop = TRUE)
## S3 method for class 'normMix'
print(x, ...)
## S3 method for class 'normMix'
summary(object, probs = c(0.025, 0.5, 0.975), ...)
## S3 method for class 'normMix'
sigma(object, ...)
sigma(object) <- value
... |
List of mixture components. |
sigma |
Reference scale. |
param |
Determines how the parameters in the list are interpreted. See details. |
m |
Vector of means |
n |
Vector of sample sizes. |
drop |
Delete the dimensions of an array which have only one level. |
x |
The mixture to print |
object |
Normal mixture object. |
probs |
Quantiles reported by the |
value |
New value of the reference scale |
Each entry in the ...
argument list is expected to
be a triplet of numbers which defines the weight w_k
, first
and second parameter of the mixture component k
. A triplet
can optionally be named which will be used appropriately.
The first and second parameter can be given in different
parametrizations which is set by the param
option:
Mean and standard deviation. Default.
Mean and number of observations. n
determines s
via the relation s=\sigma/\sqrt{n}
with \sigma
being the fixed reference scale.
The reference scale \sigma
is the fixed standard deviation in
the one-parameter normal-normal model (observation standard
deviation). The function sigma
can be used to query the
reference scale and may also be used to assign a new reference
scale, see examples below. In case the sigma
is not
specified, the user has to supply sigma
as argument to
functions which require a reference scale.
Returns a normal mixture with the specified mixture
components. mn2norm
returns the mean and standard deviation
given a mean and sample size parametrization.
sigma(object) <- value
: Allows to assign a new reference scale sigma
.
Other mixdist:
mixbeta()
,
mixcombine()
,
mixgamma()
,
mixmvnorm()
,
mixplot
,
mix
nm <- mixnorm(rob=c(0.2, 0, 2), inf=c(0.8, 2, 2), sigma=5)
print(nm)
summary(nm)
plot(nm)
set.seed(57845)
mixSamp <- rmix(nm, 500)
plot(nm, samp=mixSamp)
# support defined by quantiles
qmix(nm, c(0.01, 0.99))
# density function
dmix(nm, seq(-5,5,by=2))
# distribution function
pmix(nm, seq(-5,5,by=2))
# the reference scale can be changed (it determines the ESS)
ess(nm)
sigma(nm) <- 10
ess(nm)
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