[morpheus.git] / vignettes / report.Rmd

---
title: Use morpheus package

output:
  pdf_document:
    number_sections: true
    toc_depth: 1
---

```{r setup,  results="hide", include=FALSE}
knitr::opts_chunk$set(echo = TRUE, include = TRUE,
  cache = TRUE, comment="", cache.lazy = FALSE,
  out.width = "100%", fig.align = "center")
```

## Introduction
<!--Tell that we try to learn classification parameters in a non-EM way, using algebric manipulations.-->

*morpheus* is a contributed R package which attempts to find the parameters of a mixture of logistic classifiers.
When the data under study come from several groups that have different characteristics, using mixture models is a very popular way to handle heterogeneity.
Thus, many algorithms were developed to deal with various mixtures models. Most of them use likelihood methods or Bayesian methods that are likelihood dependent.
*flexmix* is an R package which implements these kinds of algorithms.

However, one problem of such methods is that they can converge to local maxima, so several starting points must be explored.
Recently, spectral methods were developed to bypass EM algorithms and they were proved able to recover the directions of the regression parameter
in models with known link function and random covariates (see [XX]).
Our package extends such moment methods using least squares to get estimators of the whole parameters (with theoretical garantees, see [XX]).
Currently it can handle only binary output $-$ which is a common case.

## Model


TODO: retrouver mon texte initial + article.

2) Algorithm (as in article)

3) Experiments: show package usage
Commit	Line	Data
3d5b5060	1	---
c83df166	2	title: Use morpheus package
3d5b5060 BA	3
	4	output:
	5	pdf_document:
	6	number_sections: true
	7	toc_depth: 1
	8	---
	9
	10	```{r setup, results="hide", include=FALSE}
	11	knitr::opts_chunk$set(echo = TRUE, include = TRUE,
	12	cache = TRUE, comment="", cache.lazy = FALSE,
	13	out.width = "100%", fig.align = "center")
	14	```
	15
c83df166 BA	16	## Introduction
c83df166 BA	17	<!--Tell that we try to learn classification parameters in a non-EM way, using algebric manipulations.-->
3d5b5060	18
cff1083b BA	19	morpheus is a contributed R package which attempts to find the parameters of a mixture of logistic classifiers.
	20	When the data under study come from several groups that have different characteristics, using mixture models is a very popular way to handle heterogeneity.
	21	Thus, many algorithms were developed to deal with various mixtures models. Most of them use likelihood methods or Bayesian methods that are likelihood dependent.
	22	flexmix is an R package which implements these kinds of algorithms.
3d5b5060	23
cff1083b BA	24	However, one problem of such methods is that they can converge to local maxima, so several starting points must be explored.
cff1083b BA	25	Recently, spectral methods were developed to bypass EM algorithms and they were proved able to recover the directions of the regression parameter
c83df166	26	in models with known link function and random covariates (see [XX]).
cff1083b BA	27	Our package extends such moment methods using least squares to get estimators of the whole parameters (with theoretical garantees, see [XX]).
cff1083b BA	28	Currently it can handle only binary output $-$ which is a common case.
3d5b5060	29
c83df166 BA	30	## Model
	31
	32
3d5b5060	33
cff1083b	34	TODO: retrouver mon texte initial + article.
3d5b5060	35
cff1083b	36	2) Algorithm (as in article)
3d5b5060	37
cff1083b	38	3) Experiments: show package usage