R: Unsupervised learning for incomplete dataset

unsupMI {doMIsaul}

R Documentation

Unsupervised learning for incomplete dataset

Description

Unsupervised clustering for multiply imputed datasets using MultiCons() consensus (Faucheux et al. 2021 procedure)

Usage

unsupMI(
  Impute = FALSE,
  Impute.m = 5,
  cens.data.lod = NULL,
  cens.standards = NULL,
  cens.mice.log = 10,
  censsurv.var.log = NULL,
  censsurv.maxit = 10,
  data,
  log.data = FALSE,
  algo = "km",
  k.crit = "ch",
  comb.cons = FALSE,
  plot.cons = FALSE,
  return.detail = FALSE,
  not.to.use = c("time", "status"),
  cleanup.partition = TRUE,
  min.cluster.size = 10,
  level.order = NULL,
  Unclassified = "Unclassified"
)

Arguments

`Impute`	Default is `FALSE` to indicate that the user performed the imputation and provides the imputed data. Otherwise string ("`MImpute`", "`MImpute_surv`" or "`MImpute_lcens`") to perform the imputation within the call using the `MImpute()`, `MImpute_surv()` or `MImpute_lcens()` function.
`Impute.m`	Used only if Impute is not `FALSE` ; number of imputations to perform
`cens.data.lod`	passed to `MImpute_lcens()` if `Impute == MImpute_lcens`
`cens.standards`	passed to `MImpute_lcens()` if `Impute == MImpute_lcens`
`cens.mice.log`	passed to `MImpute_lcens()` if `Impute == MImpute_lcens`
`censsurv.var.log`	for `MImpute_lcenssurv imputation`: names of variables to log if `mice.log` is numeric. If `NULL`, all variables but those in`time.status.names` will be logged.
`censsurv.maxit`	for `MImpute_lcenssurv imputation:` passed to `mice()`.
`data`	Data, in the form of a list of data.frame(s). The list should be one length 1 if data are complete or if Impute is not `FALSE`, it should be a list of imputed dataframes if data are incomplete and imputed. If some columns are in `not.to.use`, they will be discarded for the clustering.
`log.data`	logical. Should all columns of the dataset be logged before applying clustering algorithms?
`algo`	vector of strings: name of clustering algorithms to use (use "`km`" for `k-means`, "`kmed`" for `K-medians`, "`hc`" for `hclust()` and/or "`mclust`" for `mclust()`).
`k.crit`	string. Criterion to select the optimal number of clusters (for each imputed dataset). Use "`ch`" for Calinski and Harabasz criterion (not available for `mclust`), "`CritCF`" for `CritCF` or "`bic`" for `BIC` (`mclust` only).
`comb.cons`	logical. Forced to `FALSE` if `length(algo)<2`. Use `TRUE` to perform an additional consensus from all partitions generates, whatever the algorithm.
`plot.cons`	logical. Use `TRUE` to print the MultiCons tree. Note that if all partitions are identical across the imputation no consensus will be performed and therefore not plot will be obtained even if `plot.cons = TRUE`.
`return.detail`	logical. Should the detail of imputation specific partition and the imputed data be returned, in the supplement to the final consensus partition?
`not.to.use`	vector of strings : names of the columns that should be discarded for the learning step.
`cleanup.partition`	should the partition be trimmed of small clusters. (The consensus may generate small clusters of observations for which there is no consensus on the cluster assignation)
`min.cluster.size`	if `cleanup.partition == TRUE`: Minimum cluster size (i.e., smaller clusters will be discarded)
`level.order`	if `cleanup.partition == TRUE`: optional. If you supply a variable the cluster levels will be ordinated according to the mean values for the variable
`Unclassified`	if `cleanup.partition == TRUE` string for the label of the unclassified observations. defaults value is `NA`.

Value

if length(algo)>1 a vector of final cluster ID ; if length(algo)>1 a data.frame with each column being the final cluster ID for the corresponding algorithm. Or if return.detail == TRUE, a list containing Consensus : the final cluster ID (or data.frame), Detail: the clusters obtained for each imputed dataset, Imputed.data a list containing the imputed datasets.

Examples

### With imputation included
data(cancer, package = "survival")
cancer$status <- cancer$status - 1
res.0 <- unsupMI(data = list(cancer), Impute = "MImpute_surv",
                 cleanup.partition = FALSE)

### With imputation not included
## 1 imputation
cancer.imp <- MImpute_surv(cancer, 3)
## 2 learning
res <- unsupMI(data = cancer.imp, cleanup.partition = FALSE)
summary(factor(res))
res.1 <- unsupMI(data = cancer.imp)
summary(factor(res.1))

## 2.bis learning with several algorithms
res.2 <- unsupMI(data = cancer.imp, algo = c("km", "hc"), comb.cons = TRUE,
                 plot.cons = TRUE)

[Package doMIsaul version 1.0.1 Index]