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A wrapper function linking the 3 single level JM Imputation functions. The matrix of responses Y, must be a data.frame where continuous variables are numeric and binary/categorical variables are factors.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1
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A wrapper function linking the 2-level JM Imputation functions. The matrices of responses Y and Y2, must be data.frames where continuous variables are numeric and binary/categorical variables are factors.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo2
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This function is similar to jomo1ranmixhr, but it returns the values of all the parameters in the model at each step of the MCMC instead of the imputations. It is useful to check the convergence of the MCMC sampler.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1ranmixhr.MCMCchain
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This function is similar to jomo1con, but it returns the values of all the parameters in the model at each step of the MCMC instead of the imputations. It is useful to check the convergence of the MCMC sampler.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1con.MCMCchain
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This function is similar to jomo1ran, but it returns the values of all the parameters in the model at each step of the MCMC instead of the imputations. It is useful to check the convergence of the MCMC sampler.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1ran.MCMCchain
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This function is similar to jomo1rancat, but it returns the values of all the parameters in the model at each step of the MCMC instead of the imputations. It is useful to check the convergence of the MCMC sampler.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1rancat.MCMCchain
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Impute a clustered dataset with mixed data types as outcome. A joint multivariate model for partially observed data is assumed and imputations are generated through the use of a Gibbs sampler where the covariance matrix is updated with a Metropolis-Hastings step. Fully observed categorical covariates may be considered as covariates as well, but they have to be included as dummy variables.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1ranmix
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Impute a single level dataset with mixed data types as outcome. A joint multivariate model for partially observed data is assumed and imputations are generated through the use of a Gibbs sampler where the covariance matrix is updated with a Metropolis-Hastings step. Fully observed categorical variables may be considered as covariates as well, but they have to be included as dummy variables.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1mix
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Impute a clustered dataset with continuous outcomes only. A joint multivariate model for partially observed data is assumed and imputations are generated through the use of a Gibbs sampler. Categorical covariates may be considered, but they have to be included with dummy variables.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1rancon
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0 images
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This function is similar to jomo1mix, but it returns the values of all the parameters in the model at each step of the MCMC instead of the imputations. It is useful to check the convergence of the MCMC sampler.
● Data Source:
CranContrib
● Keywords:
● Alias: jomo1mix.MCMCchain
●
0 images
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