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Commit 42c3522b authored by Laurence Viry's avatar Laurence Viry
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recup Multidim

parent c891eb57
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"This course is inspired by the MOOC (Massive Open Online Course) \"[Exploratory Multivariate Data Analysis](https://www.fun-mooc.fr/courses/course-v1%3Aagrocampusouest%2B40001S04EN%2Bsession04/about)\" (the first session in English was in 2017) from the platform FUN. <br\\>\n",
"(Multivariate Multidimensional Data Analysis (Département de mathématiques\n",
"appliquées d’Agrocampus Ouest - Rennes, F. Husson, J. Pagès, M. Houée-Bigot)\n",
"\n",
"The 2nd edition of the MOOC will start the 5h of March 2018, you can subscribe until april 20.\n",
"\n",
"Version en français: [Analyse de données multidimensionnelles](https://www.fun-mooc.fr/courses/course-v1:agrocampusouest+40001S04+session04/about)"
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"# Approach to make a multivariate analysis\n",
"[Tutorial F. Husson](http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson/Rcorner)\n",
"\n",
"1. Are there groups of variables? Use of Multiple factor analysis (function MFA i FactoMiner) <br\\>\n",
"<br\\>\n",
"2. What is the type of information?\n",
" * Contingency table -> Factorial correspondence analysis (AFC or AFMTC if several)\n",
" * Several tables of contingencies -> AFMTC\n",
" * Table \"individuals - variables\" -> principal components analysis (PCA), Multiple factor analysis (MFA), nalyse es correspondances multiples. <br\\>\n",
"<br\\>\n",
"3. What are the active elements? what are the elements that will participate in the construction of the axes?<br\\>\n",
"<br\\>\n",
"4. What are the additional elements? they do not participate in the construction of the axes but are useful for interpretation.<br\\>\n",
"<br\\>\n",
"5. What is the nature of the active variables?\n",
" * Quantitative variables: Principal Component Analysis (PCA)\n",
" * Qualitative Variables: Multiple Correspondence Analysis (MCA)\n",
" * Mixed variables: AFDM<br\\>\n",
" <br\\>\n",
"Whatever the method, the additional variables can be of two types.\n",
"<br\\>\n",
"6. Should we reduce the quantitative variables?<br\\>\n",
"<br\\>\n",
"7. Are there any missing data? How to treat them?<br\\>\n",
"<br\\>\n",
"8. The steps of the analysis<br\\>\n",
" * Start the factor analysis.<br\\>\n",
"<br\\>\n",
" * Describe the factorial axes by the active initial variables (dimdesc)<br\\>\n",
"<br\\>\n",
" * It may be interesting to use a classification method to determine groups of individuals (HCPC)<br\\>\n",
"<br\\>\n",
"<img src=\"../../figures/MultiFactorielAnalysis.jpg\",width=\"80%\",height=\"80%\">"
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"Pour en savoir plus [voir vidéo F. Husson ](https://www.youtube.com/watch?v=UrS00sOpeec) (in french).\n",
"\n",
"In this course, we present only how to analyze tables with quantitative variables using **principal components analysis** (PCA) and how to use the method with **FactoMineR** in **R**. \n",
"\n",
"# Introduction\n",
"The aim of the PCA method is to summarize a table of individuals x variables data, the variables being quantitatives.\n",
"\n",
"The PCA allows to study the similarities between individuals from the point of view of a group of variables and gives off profiles of individuals.\n",
"\n",
"It allows a balance of the linear links between variables from the correlation coefficients.\n",
"\n",
"These studies can be related to characterize individuals or groups of individuals by variables and to illustrate the links between variables from characteristic individuals.\n",
"\n",
"## Data - practicalities"
]
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"source": [
"# Studying individuals and variables\n",
"## Studying individuals\n",
"\n",
"## Studying variables"
]
},
{
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"metadata": {},
"source": [
"# PCA with FactoMineR \n",
"FactoMineR is an R package dedicated to multivariate Exploratory Data Analysis. It is developed and maintained by François Husson, Julie Josse, Sébastien Lê, d'Agrocampus Rennes, and J. Mazet."
]
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{
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"# Factoshiny: interactive graphs in exploratory multivariate data analysis "
]
},
{
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"# Interpretation aids"
]
},
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"# A detailed PCA example"
]
},
{
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"# Gestion des données manquantes en ACP\n"
]
},
{
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"metadata": {},
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"# Autres méthodes d'analyse multidimentionnelles\n",
"## Analyse factorielle des correspondances \n",
"### Données et objectifs"
]
},
{
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"## Analyse des correspondances multiples \n",
"### Données et objectifs"
]
},
{
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"## Classification \n",
"### Données et objectifs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyse Factorielle Multiple \n",
"### Données et objectifs"
]
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"# Quelques références \n",
"\n",
"* Analyse de données avec R, 2ème édition revue et augmentée. <br />\n",
" F. Husson, S. Lê & J. Pagès (2016). <br /> Presses Universitaires de Rennes\n",
" \n",
"\n",
"* Statistique avec R, 3ème edition revue et augmentée.<br />\n",
" P-A. Cornillon, A. Guyader, F. Husson, N. Jégou, J. Josse, M. Kloareg, \n",
" E. Matzner-Lober, L. Rouvière (2012). <br />Presses Universitaires de Rennes\n",
" \n",
"\n",
"* Exploratory Multivariate Analysis by Example Using R.<br /> \n",
" F. Husson, S. Lê & J. Pagès. 2nd edition (2017). <br />Chapman & Hall/CRC Computer Science & Data Analysis.\n",
" \n",
"* MOOC sur FUN "
]
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