PReMiuM (version 3.0.24)

clusSummaryBernoulliDiscrete: Sample datasets for profile regression

Description

Definition of skeleton of sample datasets for profile regression.

Usage

clusSummaryBernoulliDiscrete()
clusSummaryBernoulliNormal
clusSummaryBernoulliDiscreteSmall()
clusSummaryBinomialNormal()
clusSummaryCategoricalDiscrete()
clusSummaryNormalDiscrete()
clusSummaryNormalNormal()
clusSummaryPoissonDiscrete()
clusSummaryPoissonNormal()
clusSummaryVarSelectBernoulliDiscrete()
clusSummaryBernoulliMixed()

Value

• The output of these function is a list with the following components. These can be used as inputs for profile regression function profRegr().
• outcomeType
  The outcome type of the dataset.
• covariateType
  The covariate type of the dataset.
• nCovariates
  The number of covariates generated.
• nCategories
  The number of categories of the covariates if the covariates are discrete or mixed.
• nFixedEffects
  The number of fixed effects.
• fixedEffectsCoeffs
  The names of the fixed effects.
• missingDataProb
  The pobability of generating missing data.
• nClusters
  The number of clusters.
• clusterSizes
  The number of observations in each cluster.
• clusterData
  The dataset, including the outcome, the covariates, the fixed effects, the number of trials (if Binomial outcome) and the offset (for Poisson outcome).
• covNames
  The names of the covariates of the dataset.
• nDiscreteCovs
  The number of discrete covariates, if the covariate type is mixed.
• nContinuousCovs
  The number of continuous covariates, if the covariate type is mixed.
• outcomeT
  The name of the column of the dataset containing the number of trials (if Binomial outcome) or the offset (for Poisson outcome).

Details

clusSummaryBernoulliDiscrete generates a dataset with Bernoulli outcome and discrete covariates.

clusSummaryBernoulliNormal generates a dataset with Bernoulli outcome and Normal covariates.

clusSummaryBernoulliDiscreteSmall generates a dataset with Bernoulli outcome and discrete covariates (with smaller cluster sizes).

clusSummaryBinomialNormal generates a dataset with Binomial outcome and discrete covariates.

clusSummaryCategoricalDiscrete generates a dataset with categorical outcome and discrete covariates.

clusSummaryNormalDiscrete generates a dataset with Normal outcome and discrete covariates.

clusSummaryNormalNormal generates a dataset with Normal outcome and Normal covariates.

clusSummaryPoissonDiscrete generates a dataset with Poisson outcome and discrete covariates.

clusSummaryPoissonNormal generates a dataset with Poisson outcome and Normal covariates.

clusSummaryVarSelectBernoulliDiscrete generates a dataset with Bernoulli outcome and discrete covariates, suitable for variable selection as some covariates are not driving the clustering.

clusSummaryBernoulliMixed generates a dataset with Bernoulli outcome and mixed covariates.

Authors

David Hastie, Department of Epidemiology and Biostatistics, Imperial College London, UK

Silvia Liverani, Department of Epidemiology and Biostatistics, Imperial College London and MRC Biostatistics Unit, Cambridge, UK

Maintainer: Silvia Liverani

References

Liverani, S., Hastie, D. I., Azizi, L., Papathomas, M. and Richardson, S. (2013) PReMiuM: An R package for Profile Regression Mixture Models using Dirichlet Processes. Submitted. Available at http://uk.arxiv.org/abs/1303.2836

Examples

clusSummaryBernoulliDiscrete()