domisaul

Do Multiple Imputation-Semisupervised And Unsupervised Learning

https://github.com/lilithf/domisaul

Repository

Do Multiple Imputation-Semisupervised And Unsupervised Learning

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# doMIsaul 



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## Overview

The goal of \code{doMIsaul} package is to provide functions to perform 
unsupervised and semisupervised learning for an incomplete dataset.

## Installation








You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("LilithF/doMIsaul")
```
## Example

This is a basic example which shows you how to perform unsupervised learning 
for an incomplete dataset:

```{r example_unsup}
library(doMIsaul)
data(cancer, package = "survival")
cancer$status <- cancer$status - 1
cancer <- cancer[, -1]

set.seed(1243)
res.unsup <- 
  unsupMI(data = list(cancer), Impute = "MImpute_surv", Impute.m = 10,
          cleanup.partition = TRUE, return.detail = TRUE)

cancer$part_unsup <- res.unsup$Consensus

plot_MIpca(res.unsup$Imputed.data, 1:228, color.var = cancer$part_unsup,
           pca.varsel = c("age", "sex", "ph.ecog", "ph.karno", "pat.karno",
                          "meal.cal",  "wt.loss"))

plot_boxplot(data = cancer, partition.name = "part_unsup",
             vars.cont = c("age", "meal.cal", "wt.loss"),
             unclass.name = "Unclassified", include.unclass = FALSE)

plot_frequency(data = cancer, partition.name = "part_unsup",
               vars.cat = c("sex", "ph.ecog"))

```

This is a basic example which shows you how to perform semisupervised learning 
for an incomplete dataset with a survival outcome:


```{r example_semisup}
## With imputation included
set.seed(345)
res.semisup <- 
  seMIsupcox(X = list(cancer[, setdiff(colnames(cancer), "part_unsup")]),
             Y = cancer[, c("time", "status")],
             Impute = TRUE, Impute.m = 10, center.init = TRUE,
             nfolds = 10, center.init.N = 50, 
             cleanup.partition = TRUE, return.detail = TRUE)
# This is an example, a larger value for center.init.N is recommended.

cancer$part_semisup <- res.semisup$Consensus[[1]]

plot_MIpca(res.semisup$Imputed.data, NULL, color.var = cancer$part_semisup,
           pca.varsel = c("age", "sex", "ph.ecog", "ph.karno", "pat.karno",
                          "meal.cal",  "wt.loss"))

plot_boxplot(data = cancer, partition.name = "part_semisup",
             vars.cont = c("age", "meal.cal", "wt.loss"),
             unclass.name = "Unclassified", include.unclass = TRUE)

plot_frequency(data = cancer, partition.name = "part_semisup",
               vars.cat = c("sex", "ph.ecog"))


```






## Reference publications
You may find more details on the methods implemented in this package in the 
associated publications: 

* Unsupervised MI learning: Faucheux L, Resche-Rigon M, Curis E, Soumelis V, 
Chevret S., Clustering with missing and left-censored data: A simulation study
comparing multiple-imputation-based procedures. Biometrical Journal. 2021;
63: 372– 393. 
* Semisupervised MI learning for a survival outcome: Faucheux L, Soumelis V,
Chevret S., Multiobjective semisupervised learning with a right-censored
endpoint adapted to the multiple imputation framework. Biometrical Journal.
2021; 1– 21. 

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