mda.norm: Monotone data augmentation for incomplete multivariate normal...
In norm: Analysis of Multivariate Normal Datasets with Missing Values
mda.norm R Documentation
Monotone data augmentation for incomplete multivariate normal data
Description
Monotone data augmentation under the usual noninformative prior, as
described in Chapter 6 of Schafer (1996). This function simulates one
or more iterations of a single Markov chain. One iteration consists of
a random imputation of the missing data given the observed data and
the current parameter value (I-step), followed by a draw from the
posterior distribution of the parameter given the observed data and
the imputed data (P-step). The I-step imputes only enough data to
complete a monotone pattern, which typically makes this algorithm
converge more quickly than da.norm, particularly when the observed
data are nearly monotone. The order of the variables in the original
data matrix determines the monotone pattern to be completed. For fast
convergence, it helps to order the variables according to their rates
of missingness, with the most observed (least missing) variable on the
left and the least observed variable on the right.
Usage
mda.norm(s, theta, steps=1, showits=FALSE)
Arguments
s
summary list of an incomplete normal data matrix produced by the
function prelim.norm.
theta
starting value of the parameter. This is a parameter vector in packed
storage, such as one created by the function makeparam.norm. One
obvious choice for a starting value is an ML estimate or posterior
mode produced by em.norm.
steps
number of monotone data augmentation iterations to be simulated.
showits
if TRUE, reports the iterations so the user can monitor the progress
of the algorithm.
Value
Returns a parameter vector, the result of the last P-step. If the
value of steps was large enough to guarantee approximate
stationarity, then this parameter can be regarded as a proper draw
from the observed-data posterior, independent of start.
WARNING
Before this function may be used, the random number generator seed
must be initialized with rngseed at least once in the current S
session.
References
Chapter 6 of Schafer (1996).
See Also
rngseed, em.norm, prelim.norm, and getparam.norm.
Examples
data(mdata)
s <- prelim.norm(mdata)
thetahat <- em.norm(s) #find the MLE for a starting value
rngseed(1234567) #set random number generator seed
theta <- mda.norm(s,thetahat,steps=20,showits=TRUE) # take 20 steps
getparam.norm(s,theta) # look at result
norm documentation built on July 9, 2023, 7:34 p.m.
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| mda.norm | R Documentation |
Monotone data augmentation for incomplete multivariate normal data
Description
Monotone data augmentation under the usual noninformative prior, as
described in Chapter 6 of Schafer (1996). This function simulates one
or more iterations of a single Markov chain. One iteration consists of
a random imputation of the missing data given the observed data and
the current parameter value (I-step), followed by a draw from the
posterior distribution of the parameter given the observed data and
the imputed data (P-step). The I-step imputes only enough data to
complete a monotone pattern, which typically makes this algorithm
converge more quickly than da.norm, particularly when the observed
data are nearly monotone. The order of the variables in the original
data matrix determines the monotone pattern to be completed. For fast
convergence, it helps to order the variables according to their rates
of missingness, with the most observed (least missing) variable on the
left and the least observed variable on the right.
Usage
mda.norm(s, theta, steps=1, showits=FALSE)
Arguments
s |
summary list of an incomplete normal data matrix produced by the
function |
theta |
starting value of the parameter. This is a parameter vector in packed
storage, such as one created by the function |
steps |
number of monotone data augmentation iterations to be simulated. |
showits |
if |
Value
Returns a parameter vector, the result of the last P-step. If the
value of steps was large enough to guarantee approximate
stationarity, then this parameter can be regarded as a proper draw
from the observed-data posterior, independent of start.
WARNING
Before this function may be used, the random number generator seed
must be initialized with rngseed at least once in the current S
session.
References
Chapter 6 of Schafer (1996).
See Also
rngseed, em.norm, prelim.norm, and getparam.norm.
Examples
data(mdata)
s <- prelim.norm(mdata)
thetahat <- em.norm(s) #find the MLE for a starting value
rngseed(1234567) #set random number generator seed
theta <- mda.norm(s,thetahat,steps=20,showits=TRUE) # take 20 steps
getparam.norm(s,theta) # look at result
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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