R: Semisupervised learning for a right censored endpoint

seMIsupcox {doMIsaul}

R Documentation

Semisupervised learning for a right censored endpoint

Description

MultiCons consensus based method for MI-Semisupervised clustering. The final partition is a consensus of the Pareto-optimal solutions.

Usage

seMIsupcox(
  Impute = FALSE,
  Impute.m = 5,
  center.init = TRUE,
  center.init.N = 500,
  center.init.Ks = 2:7,
  X,
  CVE.fun = "LP",
  Y,
  nfolds = 10,
  save.path = NULL,
  Unsup.Sup.relImp = list(relImp.55 = c(0.5, 0.5)),
  plot.cons = FALSE,
  cleanup.partition = TRUE,
  min.cluster.size = 10,
  level.order = NULL,
  Unclassified = "Unclassified",
  return.detail = FALSE
)

Arguments

`Impute`	Boolean. Default is FALSE to indicate that the user performed the imputation and provides the imputed data. If TRUE, the imputation will be performed within the call using the `MImpute_surv()` function. Note that if Impute is `TRUE`, `center.init` is also forced to `TRUE` as the center coordinates may depend on the imputation.
`Impute.m`	Used only if Impute is `TRUE`; number of imputations to perform
`center.init`	Either a User supplied List of dataframe containing the cluster centers coordinates (for example as obtained with `initiate_centers()`, Or `TRUE` to initiate the centers within the call of the function (performed with `initiate_centers()`). Note that if `TRUE` a random initialization will be performed. For a finer tuning of the center initialization the user should generate and provide the list of centers coordinates.
`center.init.N`	Used only if `center.init` is `TRUE`. The number to initialization to produce. Default to 500.
`center.init.Ks`	Used only if `center.init` is `TRUE`. Vector of number of clusters to generate for the initialization. Default to 2 to 7 clusters.
`X`	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 TRUE, of should be a list of imputed dataframes if data are incomplete. If columns named "`time`" and "`status`" are present they will be discarded for the clustering.
`CVE.fun`	string indicating how to calculate the cross validation error : only `LP` is available and stands for linear predictor approach (using the 'ncvreg' package).
`Y`	Passed to CVE.fun, Outcome data: should be dataframe or matrix with 2 columns: "time" and "status".
`nfolds`	Number of folds for cross-validation.
`save.path`	Path indicating where objectives values for each iteration should be saved. If null the values are not saved.
`Unsup.Sup.relImp`	List of weights for the unsupervised and supervised objectives for the Pareto optimal solution. Default is to use only one set of weights : same weight.
`plot.cons`	Logical. Should the consensus tree be plotted?
`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`.
`return.detail`	logical. Should the detail of imputation specific partition be returned, in supplement to the final consensus partition?

Value

A vector containing the final cluster IDs. Or if return.detail == TRUE, a list containing Consensus: the final cluster ID, Detail: the clusters obtained for each imputed dataset, Imputed.data a list containing the imputed datasets.

Examples

data(cancer, package = "survival")
cancer$status <- cancer$status - 1
cancer <- cancer[, -1]
### With imputation included
res <- seMIsupcox(X = list(cancer), Y = cancer[, c("time", "status")],
                  Impute = TRUE, Impute.m = 3, center.init = TRUE,
                  nfolds = 10, center.init.N = 20)

### With imputation and center initialization not included
## 1 imputation
cancer.imp <- MImpute_surv(cancer, 3)

## 2 Center initialization
# A low N value is used for example purposes. Higher values should be used.
N <- 20
center.number <- sample(2:6, size = N, replace = TRUE)
the.seeds <- runif(N) * 10^9
sel.col <- which(!colnames(cancer) %in% c("time", "status"))
inits <- sapply(1:length(cancer.imp), function(mi.i) {
 initiate_centers(data = cancer.imp[[mi.i]][, sel.col],
                  N = N, t = 1, k = center.number,
                  seeds.N = the.seeds)},
                USE.NAMES = TRUE, simplify = FALSE)

## 3 learning

res1 <- seMIsupcox(X = cancer.imp, Y = cancer[, c("time", "status")],
                   Impute = FALSE, center.init = inits, nfolds = 10,
                   cleanup.partition = FALSE)
res2 <- seMIsupcox(X = cancer.imp, Y = cancer[, c("time", "status")],
                  center.init = inits, nfolds = 10)

[Package doMIsaul version 1.0.1 Index]