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Takes a parameter vector, such as one produced by em.norm or da.norm, and returns a list of parameters on the original scale.
● Data Source:
CranContrib
● Keywords: regression
● Alias: getparam.norm
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Combines estimates and standard errors from m complete-data analyses performed on m imputed datasets to produce a single inference. Uses the technique described by Rubin (1987) for multiple imputation inference for a scalar estimand.
● Data Source:
CranContrib
● Keywords:
● Alias: mi.inference
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Evaluates the observed-data loglikelihood function at a user-supplied value of the parameter. This function is useful for monitoring the progress of EM and data augmentation.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: loglik.norm
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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.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: mda.norm
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Evaluates the log of the observed-data posterior density at a user-supplied value of the parameter. Assumes a normal-inverted Wishart prior. This function is useful for monitoring the progress of EM and data augmentation.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: logpost.norm
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Sorts rows of x by missingness patterns, and centers/scales columns of x. Calculates various bookkeeping quantities needed for input to other functions, such as em.norm and da.norm.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: prelim.norm
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Household survey with missing values. See Schafer~(1997).
● Data Source:
CranContrib
● Keywords:
● Alias: mdata
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Data augmentation under a normal-inverted Wishart prior. If no prior is specified by the user, the usual "noninformative" prior for the multivariate normal distribution is used. This function simulates one or more iterations of a single Markov chain. Each 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).
● Data Source:
CranContrib
● Keywords: distribution
● Alias: da.norm
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Does the opposite of getparam.norm. Converts a list of user-specified parameters to a parameter vector suitable for input to functions such as da.norm and em.norm.
● Data Source:
CranContrib
● Keywords: regression
● Alias: makeparam.norm
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Simulates a value from a normal-inverted Wishart distribution. This function may be useful for obtaining starting values of the parameters of a multivariate normal distribution for multiple chains of data augmentation.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: ninvwish
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providing 
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