R/missingValues.R

In MixAll: Clustering and Classification using Model-Based Mixture Models

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#' @rdname missingValues-methods
#' @aliases missingValues,ClusterDiagGaussian-method
setMethod(
  f="missingValues",
  signature=c("ClusterMixedDataModel"),
  function(x)
  {
    nbData <- length(x@lcomponent)
    res <- vector("list", nbData)
    if(nbData>0)
    {
      for (l in 1:nbData)
      {
        res[[l]]  <- cbind(x@lcomponent[[l]]@missing, (x@lcomponent[[l]]@data)[x@lcomponent[[l]]@missing]);
        colnames(res[[l]])[3] <- "value";
      }
    }
    return(res)
  }
)

#' @rdname missingValues-methods
#' @aliases missingValues,ClusterDiagGaussianComponent-method
setMethod(
  f="missingValues",
  signature=c("ClusterDiagGaussianComponent"),
  function(x)
  { res = cbind(x@missing, x@data[x@missing]);
    colnames(res)[3] <- "value";
    nmiss <- nrow(x@missing)
    if (nmiss > 0) { rownames(res) <- 1:nmiss}
    return(res)
  }
)
#' @rdname missingValues-methods
#' @aliases missingValues,ClusterDiagGaussian-method
setMethod(
  f="missingValues",
  signature=c("ClusterDiagGaussian"),
  function(x) { return(missingValues(x@component));}
)

#' @rdname missingValues-methods
#' @aliases missingValues,ClusterGammaComponent-method
setMethod(
    f="missingValues",
    signature=c("ClusterGammaComponent"),
    function(x)
    { res = cbind(x@missing, x@data[x@missing]);
      colnames(res)[3] <- "value";
      nmiss <- nrow(x@missing)
      if (nmiss > 0) { rownames(res) <- 1:nmiss}
      return(res)
    }
)
#' @rdname missingValues-methods
#' @aliases missingValues,ClusterGamma-method
setMethod(
  f="missingValues",
  signature=c("ClusterGamma"),
  function(x) { return(missingValues(x@component));}
)

#' @rdname missingValues-methods
#' @aliases missingValues,ClusterCategoricalComponent-method
setMethod(
  f="missingValues",
  signature=c("ClusterCategoricalComponent"),
  function(x)
  { res = cbind(x@missing, x@data[x@missing]);
    colnames(res)[3] <- "value";
    nmiss <- nrow(x@missing)
    if (nmiss > 0) { rownames(res) <- 1:nmiss}
    return(res)
  }
)
#' @rdname missingValues-methods
#' @aliases missingValues,ClusterCategorical-method
setMethod(
    f="missingValues",
    signature=c("ClusterCategorical"),
    function(x) { return(missingValues(x@component));}
)

#' @rdname missingValues-methods
#' @aliases missingValues,ClusterPoissonComponent-method
setMethod(
  f="missingValues",
  signature=c("ClusterPoissonComponent"),
  function(x)
  { res = cbind(x@missing, x@data[x@missing]);
    colnames(res)[3] <- "value";
    nmiss <- nrow(x@missing)
    if (nmiss > 0) { rownames(res) <- 1:nmiss}
    return(res)
  }
)
#' @rdname missingValues-methods
#' @aliases missingValues,ClusterPoisson-method
setMethod(
  f="missingValues",
  signature=c("ClusterPoisson"),
  function(x) { return(missingValues(x@component));}
)

#' @rdname missingValues-methods
#' @aliases missingValues,ClusterPredict-method
setMethod(
  f="missingValues",
  signature=c("ClusterPredict"),
  function(x)
  { res = cbind(x@missing, x@data[x@missing])
    colnames(res)[3] <- "value"
    nmiss <- nrow(x@missing)
    if (nmiss > 0) { rownames(res) <- 1:nmiss}
    return(res)
  }
)

#' @rdname missingValues-methods
#' @aliases missingValues,ClusterPredictMixedData-method
setMethod(
  f="missingValues",
  signature=c("ClusterPredictMixedData"),
  function(x)
  {
    nbData <- length(x@ldata)
    res <- vector("list", nbData)
    if(nbData>0)
    {
      for (l in 1:nbData)
      {
        res[[l]]  <- cbind(x@lmissing[[l]], (x@ldata[[l]])[x@lmissing[[l]]]);
        colnames(res[[l]])[3] <- "value";
      }
    }
    return(res)
  }
)

#' @rdname missingValues-methods
#' @aliases missingValues,KmmComponent-method
setMethod(
    f="missingValues",
    signature=c("KmmComponent"),
    function(x) { return(NULL)}
)

#' @rdname missingValues-methods
#' @aliases missingValues,KmmModel-method
setMethod(
    f="missingValues",
    signature=c("KmmModel"),
    function(x) { return(NULL)}
)