conditionalDistMvnorm: Calculate the conditional mean & variance of a random vector...
In carx: Censored Autoregressive Model with Exogenous Covariates
Description
Usage
Arguments
Value
Description
Calculate the conditional mean & variance of a sub-vector
of a random vector given the rest elements, where the random vector follows a multivariate normal distribution.
This function calculates the conditional mean & variance of a multivariate normal distribution,
with (meanVec, varMat), conditional on y at the indices conditionalIndex.
Usage
1
conditionalDistMvnorm(y, conditionalIndex, meanVec, varMat)
Arguments
y
the vector of the known values to be conditioned on.
conditionalIndex
the index to be conditioned on.
meanVec
the mean vector the joint multivariate normal distribution.
varMat
the variance-covariance matrix of the joint multivariate normal distribution.
Value
a list consisting of 'mean' and 'var' representing the conditional mean and variance respectively.
carx documentation built on May 2, 2019, 3:43 a.m.
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Description Usage Arguments Value
Description
Calculate the conditional mean & variance of a sub-vector
of a random vector given the rest elements, where the random vector follows a multivariate normal distribution.
This function calculates the conditional mean & variance of a multivariate normal distribution,
with (meanVec, varMat), conditional on y at the indices conditionalIndex.
Usage
1 | conditionalDistMvnorm(y, conditionalIndex, meanVec, varMat)
|
Arguments
y |
the vector of the known values to be conditioned on. |
conditionalIndex |
the index to be conditioned on. |
meanVec |
the mean vector the joint multivariate normal distribution. |
varMat |
the variance-covariance matrix of the joint multivariate normal distribution. |
Value
a list consisting of 'mean' and 'var' representing the conditional mean and variance respectively.
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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