{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "frank-pension",
      "metadata": {},
      "source": [
        "# Analyse des Correspondances Multiples : le cas de l'ergatif en warlipiri 🇫🇷\n",
        "\n",
        "[Xiaoou WANG](https://scholar.google.fr/citations?user=vKAMMpwAAAAJ&hl=en)\n",
        "\n",
        "Dans ce tutoriel nous explorons le jeu de données `lwarlipiri.rds` à l’aide d’une méthode exploratoire multivariée adaptée afin d'investiguer dans quelle mesure l’utilisation du marqueur de cas ergatif `CaseMarking` est liée aux variables suivantes :\n",
        "\n",
        "* l’animéité du sujet `AnimacyOfSubject`\n",
        "* l’ordre des mots `WordOrder`\n",
        "* l’âge du locuteur `AgeGroup`\n",
        "\n",
        "Nous allons utiliser `FactoMineR` et `factoextra`, ce dernier offrant des graphes plus intuitifs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "silver-greeting",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:31.002846Z",
          "start_time": "2021-04-21T12:47:28.713Z"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading required package: ggplot2\n",
            "\n",
            "Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "'data.frame':\t347 obs. of  4 variables:\n",
            " $ AgeGroup        : Factor w/ 2 levels \"adult\",\"child\": 2 2 2 2 2 2 2 2 2 2 ...\n",
            " $ CaseMarking     : Factor w/ 2 levels \"ergative\",\"other\": 1 1 1 1 1 1 1 1 1 1 ...\n",
            " $ WordOrder       : Factor w/ 2 levels \"subInitial\",\"subNotInitial\": 2 1 1 2 1 1 1 2 1 2 ...\n",
            " $ AnimacyOfSubject: Factor w/ 2 levels \"animate\",\"inanimate\": 1 1 1 1 1 2 2 1 1 2 ...\n"
          ]
        }
      ],
      "source": [
        "options(tidyverse.quiet = TRUE) # omettre les warnings pour ne pas encombrer le document\n",
        "library(\"factoextra\")\n",
        "library(\"FactoMineR\")\n",
        "library(\"tidyverse\")\n",
        "df <- readRDS(url(\"https://bit.ly/3qR6KkX\"))\n",
        "df <- df %>% select(AgeGroup:AnimacyOfSubject) # garder les varialbes pertinentes\n",
        "str(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "identical-ozone",
      "metadata": {},
      "source": [
        "## Pourquoi MCA\n",
        "\n",
        "Nous choisissons l'analyse des correspondances multiples (MCA) comme méthode car dans leur forme la plus classique `PCA` s'applique surtout aux variables continues, `CA` à une table de contingence et `MCA` à une table de variables catégorielles comme c'est le cas ici, d'autant plus que le nombre de facteurs par variable n'est pas élevé."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "amber-static",
      "metadata": {},
      "source": [
        "## Table de co-occurrence\n",
        "\n",
        "Après maintes explorations, nous avons trouvé une cooccurrence de comptage très pratique. Nous procédons donc à la construction d'un tel tableau.\n",
        "\n",
        "On produit aussi le tableau en utilisant des proportions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "charged-angola",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:31.071190Z",
          "start_time": "2021-04-21T12:47:28.880Z"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 2 × 6 of type int</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>adult</th><th scope=col>child</th><th scope=col>subInitial</th><th scope=col>subNotInitial</th><th scope=col>animate</th><th scope=col>inanimate</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>ergative</th><td>165</td><td>127</td><td>195</td><td>97</td><td>244</td><td>48</td></tr>\n",
              "\t<tr><th scope=row>other</th><td> 22</td><td> 33</td><td> 41</td><td>14</td><td> 39</td><td>16</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A matrix: 2 × 6 of type int\n",
              "\\begin{tabular}{r|llllll}\n",
              "  & adult & child & subInitial & subNotInitial & animate & inanimate\\\\\n",
              "\\hline\n",
              "\tergative & 165 & 127 & 195 & 97 & 244 & 48\\\\\n",
              "\tother &  22 &  33 &  41 & 14 &  39 & 16\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A matrix: 2 × 6 of type int\n",
              "\n",
              "| <!--/--> | adult | child | subInitial | subNotInitial | animate | inanimate |\n",
              "|---|---|---|---|---|---|---|\n",
              "| ergative | 165 | 127 | 195 | 97 | 244 | 48 |\n",
              "| other |  22 |  33 |  41 | 14 |  39 | 16 |\n",
              "\n"
            ],
            "text/plain": [
              "         adult child subInitial subNotInitial animate inanimate\n",
              "ergative 165   127   195        97            244     48       \n",
              "other     22    33    41        14             39     16       "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tableau 1\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 2 × 7 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>adult</th><th scope=col>child</th><th scope=col>subInitial</th><th scope=col>subNotInitial</th><th scope=col>animate</th><th scope=col>inanimate</th><th scope=col>row total</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>ergative</th><td>0.5650685</td><td>0.4349315</td><td>0.6678082</td><td>0.3321918</td><td>0.8356164</td><td>0.1643836</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>other</th><td>0.4000000</td><td>0.6000000</td><td>0.7454545</td><td>0.2545455</td><td>0.7090909</td><td>0.2909091</td><td>1</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A matrix: 2 × 7 of type dbl\n",
              "\\begin{tabular}{r|lllllll}\n",
              "  & adult & child & subInitial & subNotInitial & animate & inanimate & row total\\\\\n",
              "\\hline\n",
              "\tergative & 0.5650685 & 0.4349315 & 0.6678082 & 0.3321918 & 0.8356164 & 0.1643836 & 1\\\\\n",
              "\tother & 0.4000000 & 0.6000000 & 0.7454545 & 0.2545455 & 0.7090909 & 0.2909091 & 1\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A matrix: 2 × 7 of type dbl\n",
              "\n",
              "| <!--/--> | adult | child | subInitial | subNotInitial | animate | inanimate | row total |\n",
              "|---|---|---|---|---|---|---|---|\n",
              "| ergative | 0.5650685 | 0.4349315 | 0.6678082 | 0.3321918 | 0.8356164 | 0.1643836 | 1 |\n",
              "| other | 0.4000000 | 0.6000000 | 0.7454545 | 0.2545455 | 0.7090909 | 0.2909091 | 1 |\n",
              "\n"
            ],
            "text/plain": [
              "         adult     child     subInitial subNotInitial animate   inanimate\n",
              "ergative 0.5650685 0.4349315 0.6678082  0.3321918     0.8356164 0.1643836\n",
              "other    0.4000000 0.6000000 0.7454545  0.2545455     0.7090909 0.2909091\n",
              "         row total\n",
              "ergative 1        \n",
              "other    1        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Tableau 2\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 3 × 6 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>adult</th><th scope=col>child</th><th scope=col>subInitial</th><th scope=col>subNotInitial</th><th scope=col>animate</th><th scope=col>inanimate</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>ergative</th><td>0.8823529</td><td>0.79375</td><td>0.8262712</td><td>0.8738739</td><td>0.8621908</td><td>0.75</td></tr>\n",
              "\t<tr><th scope=row>other</th><td>0.1176471</td><td>0.20625</td><td>0.1737288</td><td>0.1261261</td><td>0.1378092</td><td>0.25</td></tr>\n",
              "\t<tr><th scope=row>column total</th><td>1.0000000</td><td>1.00000</td><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>1.00</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A matrix: 3 × 6 of type dbl\n",
              "\\begin{tabular}{r|llllll}\n",
              "  & adult & child & subInitial & subNotInitial & animate & inanimate\\\\\n",
              "\\hline\n",
              "\tergative & 0.8823529 & 0.79375 & 0.8262712 & 0.8738739 & 0.8621908 & 0.75\\\\\n",
              "\tother & 0.1176471 & 0.20625 & 0.1737288 & 0.1261261 & 0.1378092 & 0.25\\\\\n",
              "\tcolumn total & 1.0000000 & 1.00000 & 1.0000000 & 1.0000000 & 1.0000000 & 1.00\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A matrix: 3 × 6 of type dbl\n",
              "\n",
              "| <!--/--> | adult | child | subInitial | subNotInitial | animate | inanimate |\n",
              "|---|---|---|---|---|---|---|\n",
              "| ergative | 0.8823529 | 0.79375 | 0.8262712 | 0.8738739 | 0.8621908 | 0.75 |\n",
              "| other | 0.1176471 | 0.20625 | 0.1737288 | 0.1261261 | 0.1378092 | 0.25 |\n",
              "| column total | 1.0000000 | 1.00000 | 1.0000000 | 1.0000000 | 1.0000000 | 1.00 |\n",
              "\n"
            ],
            "text/plain": [
              "             adult     child   subInitial subNotInitial animate   inanimate\n",
              "ergative     0.8823529 0.79375 0.8262712  0.8738739     0.8621908 0.75     \n",
              "other        0.1176471 0.20625 0.1737288  0.1261261     0.1378092 0.25     \n",
              "column total 1.0000000 1.00000 1.0000000  1.0000000     1.0000000 1.00     "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "t1 = table(df$CaseMarking,df$AgeGroup)\n",
        "t2 = table(df$CaseMarking,df$WordOrder)\n",
        "t3 = table(df$CaseMarking,df$AnimacyOfSubject)\n",
        "t.total = cbind(t1,t2,t3)\n",
        "t.total\n",
        "# get total number of ergative and other\n",
        "nb.ergative = nrow(df[df$CaseMarking==\"ergative\",])\n",
        "nb.other = nrow(df[df$CaseMarking==\"other\",])\n",
        "# divide each number by the total\n",
        "data.col.totals <- cbind(t.total, c(nb.ergative,nb.other)) \n",
        "colnames(data.col.totals)[7] <- \"row total\" \n",
        "data.col.totals[1,] = data.col.totals[1,]/nb.ergative \n",
        "data.col.totals[2,] = data.col.totals[2,]/nb.other\n",
        "cat(\"Tableau 1\\n\\n\")\n",
        "data.col.totals\n",
        "# get the proportional table respect to columns\n",
        "col.profiles <-prop.table(as.matrix(t.total), margin=2)\n",
        "col.profiles <- rbind(col.profiles, colSums(col.profiles))\n",
        "rownames(col.profiles)[3] <- \"column total\"\n",
        "cat(\"\\n\\nTableau 2\\n\\n\")\n",
        "col.profiles"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "textile-error",
      "metadata": {},
      "source": [
        "## Quelques remarques issues des tableaux de proportion\n",
        "\n",
        "Il est à noter que les remarques suivantes ne s'appliquent qu'aux données de ce tutoriel.\n",
        "\n",
        "1. L'usage de l'ergatif est plus fréquent chez l'adulte que chez l'enfant, ce qui n'est pas le cas d'other. Si l'on regarde le tableau 2, les adultes utilisent plus l'ergatif qu'other, les enfants aussi. Cependant cette préférence est plus marquée chez l'adulte.\n",
        "\n",
        "2. L'ergatif et l'other (les autres cas) s'utilisent plus fréquemment en subInitial que subNotInitial. Si l'on regarde le tableau 2, la préférence d'ergative en subNotInitial est plus marquée qu'en subInitial.\n",
        "\n",
        "3. L'ergatif et l'other s'utilisent plus fréquemment en animate qu'en inanimate. L'ergative est plus lié à animate qu'other si l'on regarde le tableau 2."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "stretch-cycle",
      "metadata": {},
      "source": [
        "## Que nous dit MCA\n",
        "\n",
        "### Choix des dimensions\n",
        "\n",
        "Si l'on regarde les valeurs propres et les pourcentage de variance, il semble que toutes les quatres dimensions sont importantes. La discontinuité est à peine observable. La difficulté de focaliser sur uniquement 2 dimensions survient assez fréquemment en MCA."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "touched-halloween",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:32.246835Z",
          "start_time": "2021-04-21T12:47:29.037Z"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 4 × 3 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>eigenvalue</th><th scope=col>variance.percent</th><th scope=col>cumulative.variance.percent</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>Dim.1</th><td>0.2952060</td><td>29.52060</td><td> 29.52060</td></tr>\n",
              "\t<tr><th scope=row>Dim.2</th><td>0.2645395</td><td>26.45395</td><td> 55.97456</td></tr>\n",
              "\t<tr><th scope=row>Dim.3</th><td>0.2470208</td><td>24.70208</td><td> 80.67664</td></tr>\n",
              "\t<tr><th scope=row>Dim.4</th><td>0.1932336</td><td>19.32336</td><td>100.00000</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A matrix: 4 × 3 of type dbl\n",
              "\\begin{tabular}{r|lll}\n",
              "  & eigenvalue & variance.percent & cumulative.variance.percent\\\\\n",
              "\\hline\n",
              "\tDim.1 & 0.2952060 & 29.52060 &  29.52060\\\\\n",
              "\tDim.2 & 0.2645395 & 26.45395 &  55.97456\\\\\n",
              "\tDim.3 & 0.2470208 & 24.70208 &  80.67664\\\\\n",
              "\tDim.4 & 0.1932336 & 19.32336 & 100.00000\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A matrix: 4 × 3 of type dbl\n",
              "\n",
              "| <!--/--> | eigenvalue | variance.percent | cumulative.variance.percent |\n",
              "|---|---|---|---|\n",
              "| Dim.1 | 0.2952060 | 29.52060 |  29.52060 |\n",
              "| Dim.2 | 0.2645395 | 26.45395 |  55.97456 |\n",
              "| Dim.3 | 0.2470208 | 24.70208 |  80.67664 |\n",
              "| Dim.4 | 0.1932336 | 19.32336 | 100.00000 |\n",
              "\n"
            ],
            "text/plain": [
              "      eigenvalue variance.percent cumulative.variance.percent\n",
              "Dim.1 0.2952060  29.52060          29.52060                  \n",
              "Dim.2 0.2645395  26.45395          55.97456                  \n",
              "Dim.3 0.2470208  24.70208          80.67664                  \n",
              "Dim.4 0.1932336  19.32336         100.00000                  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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AAAIIIIAAAggggEC6CWR5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88+1rx588zWpPYIIIAAAjtEILeghmmp1miPUsdvWSvb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\n9BRJxxN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EAAIIIIAAAggg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\ngg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "mca.object <- MCA(df, graph=FALSE)\n",
        "eig.val <- get_eigenvalue(mca.object)\n",
        "eig.val\n",
        "fviz_screeplot(mca.object, addlabels = TRUE, ylim = c(0, 45))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "sharing-enclosure",
      "metadata": {},
      "source": [
        "Pour mieux trancher sur le choix des dimensions, nous visualisons aussi les contributions et qualités de chaque catégorie."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "suitable-stranger",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:32.387931Z",
          "start_time": "2021-04-21T12:47:29.119Z"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 8 × 4 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Dim 1</th><th scope=col>Dim 2</th><th scope=col>Dim 3</th><th scope=col>Dim 4</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>adult</th><td>0.31235074</td><td>0.00824843</td><td>0.53708810</td><td>0.1423127</td></tr>\n",
              "\t<tr><th scope=row>child</th><td>0.31235074</td><td>0.00824843</td><td>0.53708810</td><td>0.1423127</td></tr>\n",
              "\t<tr><th scope=row>ergative</th><td>0.46272978</td><td>0.23373604</td><td>0.01939376</td><td>0.2841404</td></tr>\n",
              "\t<tr><th scope=row>other</th><td>0.46272978</td><td>0.23373604</td><td>0.01939376</td><td>0.2841404</td></tr>\n",
              "\t<tr><th scope=row>subInitial</th><td>0.05888853</td><td>0.73068770</td><td>0.04486426</td><td>0.1655595</td></tr>\n",
              "\t<tr><th scope=row>subNotInitial</th><td>0.05888853</td><td>0.73068770</td><td>0.04486426</td><td>0.1655595</td></tr>\n",
              "\t<tr><th scope=row>animate</th><td>0.34685511</td><td>0.08548600</td><td>0.38673727</td><td>0.1809216</td></tr>\n",
              "\t<tr><th scope=row>inanimate</th><td>0.34685511</td><td>0.08548600</td><td>0.38673727</td><td>0.1809216</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A matrix: 8 × 4 of type dbl\n",
              "\\begin{tabular}{r|llll}\n",
              "  & Dim 1 & Dim 2 & Dim 3 & Dim 4\\\\\n",
              "\\hline\n",
              "\tadult & 0.31235074 & 0.00824843 & 0.53708810 & 0.1423127\\\\\n",
              "\tchild & 0.31235074 & 0.00824843 & 0.53708810 & 0.1423127\\\\\n",
              "\tergative & 0.46272978 & 0.23373604 & 0.01939376 & 0.2841404\\\\\n",
              "\tother & 0.46272978 & 0.23373604 & 0.01939376 & 0.2841404\\\\\n",
              "\tsubInitial & 0.05888853 & 0.73068770 & 0.04486426 & 0.1655595\\\\\n",
              "\tsubNotInitial & 0.05888853 & 0.73068770 & 0.04486426 & 0.1655595\\\\\n",
              "\tanimate & 0.34685511 & 0.08548600 & 0.38673727 & 0.1809216\\\\\n",
              "\tinanimate & 0.34685511 & 0.08548600 & 0.38673727 & 0.1809216\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A matrix: 8 × 4 of type dbl\n",
              "\n",
              "| <!--/--> | Dim 1 | Dim 2 | Dim 3 | Dim 4 |\n",
              "|---|---|---|---|---|\n",
              "| adult | 0.31235074 | 0.00824843 | 0.53708810 | 0.1423127 |\n",
              "| child | 0.31235074 | 0.00824843 | 0.53708810 | 0.1423127 |\n",
              "| ergative | 0.46272978 | 0.23373604 | 0.01939376 | 0.2841404 |\n",
              "| other | 0.46272978 | 0.23373604 | 0.01939376 | 0.2841404 |\n",
              "| subInitial | 0.05888853 | 0.73068770 | 0.04486426 | 0.1655595 |\n",
              "| subNotInitial | 0.05888853 | 0.73068770 | 0.04486426 | 0.1655595 |\n",
              "| animate | 0.34685511 | 0.08548600 | 0.38673727 | 0.1809216 |\n",
              "| inanimate | 0.34685511 | 0.08548600 | 0.38673727 | 0.1809216 |\n",
              "\n"
            ],
            "text/plain": [
              "              Dim 1      Dim 2      Dim 3      Dim 4    \n",
              "adult         0.31235074 0.00824843 0.53708810 0.1423127\n",
              "child         0.31235074 0.00824843 0.53708810 0.1423127\n",
              "ergative      0.46272978 0.23373604 0.01939376 0.2841404\n",
              "other         0.46272978 0.23373604 0.01939376 0.2841404\n",
              "subInitial    0.05888853 0.73068770 0.04486426 0.1655595\n",
              "subNotInitial 0.05888853 0.73068770 0.04486426 0.1655595\n",
              "animate       0.34685511 0.08548600 0.38673727 0.1809216\n",
              "inanimate     0.34685511 0.08548600 0.38673727 0.1809216"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 8 × 4 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Dim 1</th><th scope=col>Dim 2</th><th scope=col>Dim 3</th><th scope=col>Dim 4</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>adult</th><td>12.196854</td><td> 0.3594274</td><td>25.0635418</td><td> 8.489687</td></tr>\n",
              "\t<tr><th scope=row>child</th><td>14.255073</td><td> 0.4200808</td><td>29.2930145</td><td> 9.922322</td></tr>\n",
              "\t<tr><th scope=row>ergative</th><td> 6.211199</td><td> 3.5011305</td><td> 0.3111011</td><td> 5.826714</td></tr>\n",
              "\t<tr><th scope=row>other</th><td>32.975820</td><td>18.5878203</td><td> 1.6516640</td><td>30.934552</td></tr>\n",
              "\t<tr><th scope=row>subInitial</th><td> 1.595288</td><td>22.0889316</td><td> 1.4524475</td><td> 6.851806</td></tr>\n",
              "\t<tr><th scope=row>subNotInitial</th><td> 3.391783</td><td>46.9638545</td><td> 3.0880865</td><td>14.567804</td></tr>\n",
              "\t<tr><th scope=row>animate</th><td> 5.417680</td><td> 1.4900297</td><td> 7.2189316</td><td> 4.317163</td></tr>\n",
              "\t<tr><th scope=row>inanimate</th><td>23.956304</td><td> 6.5887251</td><td>31.9212131</td><td>19.089953</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A matrix: 8 × 4 of type dbl\n",
              "\\begin{tabular}{r|llll}\n",
              "  & Dim 1 & Dim 2 & Dim 3 & Dim 4\\\\\n",
              "\\hline\n",
              "\tadult & 12.196854 &  0.3594274 & 25.0635418 &  8.489687\\\\\n",
              "\tchild & 14.255073 &  0.4200808 & 29.2930145 &  9.922322\\\\\n",
              "\tergative &  6.211199 &  3.5011305 &  0.3111011 &  5.826714\\\\\n",
              "\tother & 32.975820 & 18.5878203 &  1.6516640 & 30.934552\\\\\n",
              "\tsubInitial &  1.595288 & 22.0889316 &  1.4524475 &  6.851806\\\\\n",
              "\tsubNotInitial &  3.391783 & 46.9638545 &  3.0880865 & 14.567804\\\\\n",
              "\tanimate &  5.417680 &  1.4900297 &  7.2189316 &  4.317163\\\\\n",
              "\tinanimate & 23.956304 &  6.5887251 & 31.9212131 & 19.089953\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A matrix: 8 × 4 of type dbl\n",
              "\n",
              "| <!--/--> | Dim 1 | Dim 2 | Dim 3 | Dim 4 |\n",
              "|---|---|---|---|---|\n",
              "| adult | 12.196854 |  0.3594274 | 25.0635418 |  8.489687 |\n",
              "| child | 14.255073 |  0.4200808 | 29.2930145 |  9.922322 |\n",
              "| ergative |  6.211199 |  3.5011305 |  0.3111011 |  5.826714 |\n",
              "| other | 32.975820 | 18.5878203 |  1.6516640 | 30.934552 |\n",
              "| subInitial |  1.595288 | 22.0889316 |  1.4524475 |  6.851806 |\n",
              "| subNotInitial |  3.391783 | 46.9638545 |  3.0880865 | 14.567804 |\n",
              "| animate |  5.417680 |  1.4900297 |  7.2189316 |  4.317163 |\n",
              "| inanimate | 23.956304 |  6.5887251 | 31.9212131 | 19.089953 |\n",
              "\n"
            ],
            "text/plain": [
              "              Dim 1     Dim 2      Dim 3      Dim 4    \n",
              "adult         12.196854  0.3594274 25.0635418  8.489687\n",
              "child         14.255073  0.4200808 29.2930145  9.922322\n",
              "ergative       6.211199  3.5011305  0.3111011  5.826714\n",
              "other         32.975820 18.5878203  1.6516640 30.934552\n",
              "subInitial     1.595288 22.0889316  1.4524475  6.851806\n",
              "subNotInitial  3.391783 46.9638545  3.0880865 14.567804\n",
              "animate        5.417680  1.4900297  7.2189316  4.317163\n",
              "inanimate     23.956304  6.5887251 31.9212131 19.089953"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "var <- get_mca_var(mca.object)\n",
        "# Cos2: quality on the factor map\n",
        "var$cos2\n",
        "# Contributions to the principal components\n",
        "var$contrib"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "recreational-fields",
      "metadata": {},
      "source": [
        "Nous voyons bien qu'ergative et other contribuent peu à la dimension 3 (avec une mauvaise qualité en plus) et que les contributions globales des catégories sur la dimension 4 sont finalement assez faibles. Nous décidons donc de nous focaliser quand même sur les deux  premières dimensions."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "quantitative-scanner",
      "metadata": {},
      "source": [
        "### Corrélation entre variables et dimensions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "vital-secondary",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:32.716022Z",
          "start_time": "2021-04-21T12:47:29.273Z"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9T+znwzx+1rpn9e+0asQQABBBAoCQECpJJoZiqJQO4F7r33Xhk9enTuMybHxhNY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\nAAIIIIAAAgjkRYBZ7PLCSqYIFL/AwIEDZfny5fLee+8Vf2WpIQI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AgXJIVQxtqaz5v1lVvV7JyhIsmTLtiubHo5GPnqazFb\nbu62M23axLqtBXlGPvhIxAY1ng1KEtJaa4nZYH27yFtzYan7f/e9RGxg5k3/XLwZ9o6R3jWq\nDug9/Y80dhFadTEa+AaveozgvX/MkWKuvEbKHn5Uojq73eIlEnnmefEPHShe27auOJnW0e1U\n4D+uvPJK94cpcCrw6jR58dUx+NfkhSnwAgTnJJ65a0gsc2MZnJuaW/z73OReurlgmX3bB4b6\nGrzPPtfCzaHeAGm2HUR/6KGH2i/kl8i1114rAwcOtMMuOteo8W677RZbppHnhAkT5JprrpGe\nPXvK5Zdf7gKq2AYhetPMjkHRf2FM+g39r7/+6u6ctE4ODMJY4FrKVKl36CKeRI4+QmxlUm7V\nsmsXF/xU6vq2bSS5vpWr7TeX9rxKXq6ZVWqMUxaJrfOv/6ftdnehSAt7L2jbP4i365/EGzZE\njA2yzD9vcd8cBIGW7lteXiYtqn1Ny7UkarNr0aK5u0Ok+etYqGj/3cSzkzi0uOl6Ox7JjnGy\ndWp+ygmx/TKpo8uzCH7sueeebjxXqjYpguo1ehX0bod+2RRJ/hKh0UtS+AfUXgF6FwnP3LSl\neq5lv4xq3rx5bjIs4Vz0C2f9pl6/wcYz+xNBPXVYh/6uk7IT0KBI7x61sNdOeNprw/o4Z86c\nKSeeeKKcfPLJaY8t0j/wBx98sPs3efJkGT9+fH2HYX0RC3g9Nxbz6hTx/rizRPYakFBTs2CB\n2L8UNe8MJWxlv2nTPD59UowNGIO7NrqJ0eBLZ6bbaku3h84o54Ije6en7OXnxbPd+IIU/d9r\nVW8bcCves2ONzFHHuzFKbsKG320k8n87B1lXlS/LOsYy4w0CCCCAAAIIIIBAkwlE6juyfkt8\n+umnpx0cJee36667yu233568mM8lJODt1s/V1tz7QEKtzS+/uFnsolv+QYzt315X8o45yt5B\nMqJ3h+KTueVWO2Bpzb5m2mc2arIB1U47JgRHxnblM3b8kUvV4xPc+3L7HcEKG2TVk7yD7Jio\nDu3F3H6XmP9Nkchfjk24BZ2LOtZTBFYjgAACCCCAAAIINIJAvXeQ0imDdgX78ccfXXebbt26\nJVw4prM/2xS3gHfEYSJ2SmxjZ6KL/qarRA47RMy334mv02T/8KNEXrCzzdUzYC5y4P5i7IQI\n5nI77mXGTPF23EHMe1PFPPKYnTHBnsbVY4q8Lbewkz20FGNnnfO3s93rduhru9Z9KP61N4j8\nZCeCsMn8stiNe3IfOq0t5t33xLeTMHgD9nCLUv3wbPcSz07WoAGSdhf0jj8mYbNc1DEhQz4g\ngAACCCCAAAIINIlAvXeQ6ivVuHHjZN1115Xu3bvLb3/7W/deJ2ggIRAIaL/WsmefEu+Uk1yA\nEd2pn/iH2wDj408k8s/rJLJH/2DTOl8jEx8X78JhtqvdNPGHjxCxQVbZlJftmKW2sS56Xpcu\nEnnkoapnJJ14qkR7bSm+ffX22Usib09x+ZuX7D7VKXLpJTbgiYh/yQjxx9XdFTSY0tvb3Y5H\nslPax6dc1TE+T94jgAACCCCAAAIINL6AZ+/+2A5JDUs61fdWW20lp5xyivTr10/atWsnb7zx\nhujsVu+//75suWXVuJCG5c5e2jT6jCkdyNmpU6eiANHpuOXLWXb0m52Mwc6A6KU5QYb5+Wc3\nDbjeaTI6I13PjZ2Hsd3loi3bizfwICkb90CCkVm0SET322gj8WwQpJ7z7HTiybMqul8BnWZc\nH/payyQSCRnX86Ghdawn29CtVku1S/YMXUELpEDqqRPgMElD9g220Hap1YkF9NzEMzeebezs\noEwqkL2lTnilE7Lo33Q8c+PJJA3ZO2oOFRUVsnjxYmlvx24zSUMakzToBXqHDh3cDDbJTfDa\na69J79695dZb7TiQ6jRgwAB56aWX5BU71TcBUqDCayDgApBemwQf0341Dzwk/pCh4o28TMzd\nY6Rs+kfi2ZlWzI0322nsouL930418nIPibXP5aovueksczgVfUPrWF85w7b+pptucrMxXX+9\nfTAvCQEEEEAAAQQQKBKBervYvfrqq7LxxhvLHXfcIfpArvikz0D6+OOPRbfRqRY18nz66adl\n6tSp0qNHj/hNeY9AVgLeAfuJtO8gZvjlIrO/Fv+AQyR6wEDX1c6zY5O8k0/MKn92zlxgrH1g\n9L333pv5juyBAAIIIIAAAgiEWKDeAOmQQw5xzzG6+uqrpVevXu6CSIMhTTrD3bHHHute9VlC\nHe239fvtt5/oPvvuu2+Iq03RCk3A23BD8c4+XWwk7opunn9RzKJfJHLR+RJ5Yry7m1RodaK8\nCCCAAAIIIIAAAuETqDdAKrNjMk444QSZMWOGnHvuuXLBBRfIFltsEXu20ejRo2WBfZbNo48+\nKmPGjHFjj+6//35msgtfWxd0iXQ8kbnVznoXl7weG4pOsqAzzJEQQAABBBBAAAEEEMiFQL0B\nUnAQHUw4ePBg+eqrr1zAdMYZZ0ifPn1k4sSJdhKxtu6hsMcff7xbFuzDKwK5EvBHXGEnXFiY\nkJ25f6yYd95NWMYHBBBAAAEEEEAAAQSyEUg7QAoO0tLOIjZs2DAXKB100EFyzDHHyA477CAv\nvvhisAmvCORUwEz/XIw+Myk52fkXo2ef62ZSS17FZwQQQAABBBBAAAEEGiKQVoCkY47uuece\nOf3002XQoEESzFp16aWXukBpl112kQMPPNBN9a0z25EQyKWAP2SYm6kuZZ5vv+seCptyHQsR\nQAABBBBAAAEEEMhQoLy+7fU5JzrxwnPPPSc9e/Z0z5WYNGmSm9pbZ6vTufyvueYaGTJkiIwc\nOVJ233132W233eTGG2+UTTfdtL7sWY9AnQJ6/kXuubvObewc9HWvZ21eBI488kg3zXdeMidT\nBBBAAAEEEECgiQTqvYP07rvvyuTJk+WLL75wEzVMmTJFvv/+e/cQqf/85z+xYv/mN7+Rf/3r\nXzJz5kzp1q2bPPXUU7F1vEGgoQL6jCLPnlt1/rPP6SI1voBO2nL++ec3/oE5IgIIIIAAAggg\nkEeBeu8g6bONdBKG7t27x4qhEzboM5B++eWX2LLgjS7/97//7Z6LFCzjFQEEEEAAAQQQQAAB\nBBAoBIF6A6S+ffuKTsyg44z22GMPqayslDfffFP0ztJVV11Vax11enASAggggAACCCCAAAII\nIFBIAvUGSO3atRPtVvfAAw+Ijj2aO3euG1v03//+V3r37l1IdaWsCCCAAAIIIIAAAggggECd\nAvUGSLr3BhtsIBdffLH7V2durEQAAQQQQAABBBBAAAEEClggrQCptvotXbpUXn/9dfnyyy/d\nGKWdd95ZunTpUtvmLEcAAQQQQAABBBBAAAEEQi1Q7yx2Wvptt91WRowYkVARndlus802k732\n2ksGDx4s+tDYjTbayE3vnbAhHxBAoCgFXn75ZR4QXZQtS6UQQAABBBAobYG07iDp1N069ihI\nOs33wQcfLCtWrJCBAwfKoYce6t7fe++9ct5550nnzp3luOOOCzbnFQEEilBAp/iuqKiQY445\npghrR5UQQAABBBBAoFQF0gqQknH0+Uc6xfcjjzwihxxySGz18ccfL0cddZQMGzaMACmmwhsE\nEEAAAQQQQAABBBAoFIG0utglV+azzz5zY40OOOCA5FUyZMgQmTdvnsyZM6fGOhYggAACCCCA\nAAIIIIAAAmEWaFCAtO6667pudM2aNatRt4ULF7pl2v2OhAACCCCAAAIIIIAAAggUkkDaAZLO\nVvevf/3LPSRWu9V98cUX8uGHHybUVccj3HLLLdKmTRs3YUPCSj4ggAACCCCAAAIIIIAAAiEX\nSGsMUt++feWtt96Ss88+21WnrKzMvWoXu2+++ca91/FIZ5xxhixYsEBGjhwp5eVpZR1yHoqH\nAAIIIIAAAggggAACpSSQVhTzwgsviDFGZs2a5e4a6Z2jDz74QPQ5SEGaNm2am9Hqsssukwsu\nuCBYzGseBaLnnCfeFptL5KQTMjqK+Wy6+LfdKZFjjhRvh76xfc3ixeK1bx/7zBsE6hLYfPPN\n3eyVdW3DOgQQQAABBBBAoNAEPBv4mFwUWu8ctWvXTpo3b56L7MjDCmjT6PTqatqpU6caJpXN\n24j3532l7LGHa6yra4H/7PPi73OARP5jg6RBx7tNoxcNF5n2mZQ9+Vhduxb0OvXUCUS6du1a\n0PUIS+HVUk3xzE2LqKc+IiESSbvnc24OXIS56FjYlStXunMTz+wbWD216zx/37O3XLJkiSxb\ntsz9TcczN57aY6lVq1bZZ1biOegwmcX2i/L29otyPEVy9pdY/7Dzy164v13m1jtEVq8u3ApQ\ncgQQQAABBBBAAAEEciCQswApKMttt90mI0aMCD7ymqaASTM4MdFomjmyGQIIIIAAAggggAAC\nCGQqkPMA6dZbbxUdh0SqX8B/ebJU9ttDKtf5rUSbt5XKbhtK9ODDxMyenbiznTJdu8BVbvUH\nibbqIJU79xMz9f3Ebeyn6BHHSLT/3jWW+3eNlsrNthbz+Rc11hk7rkzX2QFlYqa87t77jz1R\nYzsWIIAAAggggAACCCBQCgI5D5BuvPFGGT9+fCnYZVVH/8Fx4mswY/t8Rs45SyK33CDe1luJ\neXKiRO34oPihYS2HXijm6uvE22lHidx5q3ib9JToLv1FKisTymBmfy1m5pcJy9yHBT+LTLfB\nUapnU3XoUDUOSZ9p1b2be6/5kxBAAAEEEEAAAQQQKEWBtGaxywRmwIABmWxestv611wv0rGj\nlD0/STz76tJZZ0p0j33E/PdlEQ10em4szZ95TpqPGy+REZdI5NJLqrb7y3ES7bqOmGtuyNrP\ns5M/eMPOFf8fV4m3UQ+J2PckBBBAAAEEEEAAAQRKVSCjO0h6V8P3/QSrGTNmyMSJE+WTTz6p\nsS5hQz4kCJS98aqUfTJ1TXBk17pxSL/vVbWdnelGU7O333Gv3tlnutfgB4FMIMFrUwnozJXz\n589vqsNzXAQQQAABBBBAIC8CaQVIGhTpg2C33nprmV09PkYfENu/f3/p1auX7L///rLVVlvJ\n2muvLXfeeWdeClpsmXqtW4ssXiL+FVe6sUOVfbaXaPsuYkbdXlXV6skYyqfZZxbZbnCxu0zV\nEHrnRzrXnPq72JyoT3gF9t57b9l1113DW0BKhgACCCCAAAIINEAgrQBp6NChcthhh0mLFi2k\nZcuWsmrVKjnggANk8uTJ7gJJJ2YYPny4dOvWTU477TS54Ybsu341oC4FtYt//T8lunlv8a+8\nRsz3P4j3p/+rGl907tkJ9fB0drvysoRlsQ8aZKWRjG0vEgIIIIAAAggggAACCNQvUO8YpNX2\nAl0DoEMOOcTdRdIsn3zySfnoo4/kiiuukEsuqR4XY5dffvnl8te//lVuuukmOe+88+o/eolu\nYX76Sfy/XSjSp7eUvfy8ePahXEGK2pnkXKruyrjaTtzQ7N2pYuwsc559UF+QjE7Q8P33In/o\nEyyy/fHsRAupgiE7eQMJAQQQQAABBBBAAAEE6heo9w7Sd9995+4YnXrqqbHcdNyRPrn4lFNO\niS0L3px88smi+3z5pZ1kgJRSwEz7zA44EjcrXXxwZOzTys1Lk6v2qZ6hrnKbqgDI3PXvhLzM\n2IfsLHaJz0T6f/buA86JMn3g+DNZWDrSUbA3FEVRxIIFC2LFdoh6iv2wK8ghKnqKvZ+oICq2\ns1fsKOj/VLDrCSqiiA2UXqQtu+xm5v8+syQku5O2ySaT5Pd+xEx55533/b6zSZ7MzDtW61Zi\nbgoRZ86ccF7HPBVZh+9OmEx/SnlFwmxkQAABBBBAAAEEEECgkAUSnkHaeOONpbS0VH755Zew\ng96LVGW+wM+dO1c6dOgQXq4TocCodWhktqi1zKiA1W1HkaZNxHnyabF79hBrzz3E+Xqq2LeZ\nSxPN2SVNzl/LxTKvFUcfKZVvvCUNLx8hYs7mWQcdII4ZuMG+4mqRUnPGKCJZ/Y4Q540JEjz+\n7xK49BKRNWvEHmPuCfM6qxSxnTvZto04X3wp9o23iHXIwWLt1qNmDuYRQAABBBBAAAEEECh4\ngYRnkBqay7YOOugg9x6jSZMmuSD77ruvOyjDTTfdZJ4vuiqMpAM3jBgxQrp16yZtdRABkqeA\n1b69BF54Rqyeu4l91jkS7NLNfbUOP1RKPpvibuO8Z4b61mRZUjb2PrFOOF7sm2+TYM+9xb7c\nDPk9epR5blHn6jzr/m+dcZpYeg+TBlsnnCL2OReItUNXCYxKfE+YO4R4ICD2VdeKbYYVJyGA\nAAIIIIAAAgggUIwClhm621zsFT8tMGc1evTo4Z4xOuaYY6R3797ug0yHDx9ubntpKP369ZMV\nZljqKVOmuJfjffLJJ9K9e/f4hbLWFXCWLRNZskRkyy3dMz1W48ZhGe2a+fPnu2fwNOB07zua\n9bP7fCSrJMbADWZrd1AGzWeeaxRZXrjgGBPuobBwoRkdr53EKz/G5r5frO1baNrXsWNH39c1\nHyq42WabmecclzHUd4Y6S4/NduZvL2B+qCClJ7DUXK5cUVHh/q3jmZ6lbq2ezc09sHo1CSk9\nAf2utHr1avdHZDzTs9St1VNv+WjatGn6hRV5Cfp5vtzclrGBuS8eTzM+WjLHg36hnDFjhjuE\ntw7AMH78+PBmOqLds88+KyXmC/uJJ54oV155pXTt2jW8non4Au7w3eZyRGfyFLEfe0JKHo49\nTLql9wmFnpMUp1hLP8S6bh8nh/cqy5ytMt8ovFeyFIEaAp9++qn7Q0mNxcwigAACCCCAAAJ5\nLZBUgKQtbNGihehw3/pPI3YdiEHvQdKhv/U+JR3iW6dJqQs4ZsS64CVm1L+p08Q5/xyxeuya\neiFsgUCWBfRHkSROQGe5VuwOAQQQQAABBBBITyDpAClyNy1btnTPEnGmKFKl7tPOI4+Z+4am\nuQVooNRgyrqR7OpeJFsigAACCCCAAAIIIIBAHQRSuth9jRkVTS+108vqYqXffvtNPv7441ir\nWV5DwDFn4+wR16xf+tEnYj/z3Pp5phBAAAEEEEAAAQQQQCBrAkkFSBoQ6YNf9TI7PWukZ5AG\nDx7s3qBds6b33Xef7L333jUXMx9DwL7uRpGFi6LW2sNHiGNuliMhgAACCCCAAAIIIIBAdgWS\nCpBuvvlmueuuu6RLly7uPUibbLKJjBo1Snr27OmOspbdKhfO3pyfZolzz+jaDZrzR/UzkWqv\nYQkCCCCAAAIIIIAAAgjUo0DCAElvwr7jjjvkqKOOkunTp8vtt98uP/30kzz99NPy888/u89I\nWqZDVZNSFrAvHWYe/lrluZ1jHhrrzJ7tuY6FCCCAAAIIIIAAAgggUD8CCQdp0NHq9GGwZ5xx\nRlQNTjrpJHcc/yOPPFKOPfZYmThxIs9IiBKKP+OUl7sPf9UHwMZMK81DeNu2ibmaFQjkUkB/\nNCk3x/HUqVNzWQ32jQACCCCAAAIIZFQgYYAUGrr7r7/+qrXjvn37yrhx4+S0006TM888U554\n4olaeVjgLaAPcLVO+bv3ynVL3SGUzYNiSQj4UWDevHme9yH6sa7UCQEEEEAAAQQQSFYgYYDU\noUMH2WijjeShhx6SAQMG1Hq67qmnnurehzR8+HBp1aqVNGzYMNl9kw8BBBBAAAEEEEAAAQQQ\n8JVAwgBJa3vttdfKOeecIxtuuKEMGTJERo4cGdWIyy67TJYvXy433XQTD4uNkmEGAQQQQAAB\nBBBAAAEE8kkg4SAN2phBgwbJo48+Kno26ZtvvvFs34033ihjxoyRJk2aeK5nIQIIIIAAAggg\ngAACCCDgd4GkAiRtxOmnny6zZs2Sp556KmabzjvvPJk5cyb3IsUUYgUCCCCAAAIIIIAAAgj4\nWSBhgDTbDDW9cuXKcBuaNm0anvaaaN++vZxyyilRq7744ouoeWYQQAABBBBAAAEEEEAAAT8K\nJAyQ9NlH22yzjYwePVoqKiqSbkMwGJTnnntOdt11V7nwwguT3o6MCCCQHwIPPvigPP744/lR\nWWqJAAIIIIAAAggkKZAwQDrssMPkySeflDvvvFM6deokZ511lrz99tuyePHiqF3okNRz5syR\nSZMmuQ+W7dKli5tXn5H0/vvvR+VlBgEE8l9gl112kR49euR/Q2gBAggggAACCCAQIZDUKHZ9\n+vSR7777TsaOHSujRo2SRx55xC1CB2TYeOON3aG/9f6k1atXu8sty3Ivs7v55pulc+fOEbtj\nEgEEEEAAAQQQQAABBBDwr0BSAZJWX+89uvTSS+Xiiy+WKVOmyLfffit6+Z3+m28eZtq9e3c3\nWDrooIPkyCOPdJ+d5N9mUzMEEEAAAQQQQAABBBBAoLZA0gFSaNMGDRrI/vvv7/4LLeMVAQQQ\nQAABBBBAAAEEECgEgYT3IBVCI2kDAggggAACCCCAAAIIIJCMAAFSMkrkQQABBBBAAAEEEEAA\ngaIQSPkSu6JQoZEIIJBQ4Oqrr3aH/n/ssccS5iUDAggggAACCCCQLwIESPnSU9QTAZ8J6HD/\nZWVlPqsV1UEAAQQQQAABBNIT4BK79PzYGgEEEEAAAQQQQAABBApIIKUAac2aNTJjxgxZu3Zt\nTILffvtNPv7445jrk10xe/ZsefbZZ2XixImyatWqhJulmj9hgWRAAAEEEEAAAQQQQACBohNI\nKkDSgGjo0KHSokUL6dq1q7Rs2VIGDx7seXnNfffdJ3vvvXdakE888YQMHDhQvv/+e3n++efl\nvPPOk2XLlsUsM9X8MQtiBQIIIIAAAggggAACCBS1QFIB0s033yx33XWXdOnSRf75z3/KJpts\nIqNGjZKePXu6D4nNpKCeCXr00Ufd8q+77joZO3asNGrUSJ577jnP3aSa37MQFiKAAAIIIIAA\nAggggAACRiBhgOQ4jtxxxx1y1FFHyfTp0+X222+Xn376SZ5++mn5+eef5aCDDop7didV5c8/\n/1w6deok3bt3dzfVB9MeeuihMmnSJM+iUs3vWQgLEUAAAQQQQAABBBBAAAEjkHAUuz/++MO9\nB+iMM86IAjvppJOkbdu2cuSRR8qxxx7r3itUWloalacuM/PmzZPOnTtHbaoB0+LFi8W2bQkE\nomO6VPJPmzZNBgwYEFX2YYcdJq+//nrUstDMBRdcIKecckpoNur1hRdecM+qRS1cN7PffvvJ\nrbfe6rVKfvjhB6lpGcrYpk0befPNN0OzosGpJr3EceHChdK7d++Y939pfTbeeOPwtpET2o7/\n/e9/kYvC0//6179EDbySnr17/PHHvVbJcccdJ8OGDfNc99FHH7lnGr1Wbr311qKXRHqllStX\nSt++fb1Wucv+7//+T5o0aeK5/sQTT5Tff//dc52e7dx9993ddXoMqWUo3XTTTTH7/+yzz5az\nzjorlDXqVY8Z3dYr7bHHHnL33Xd7rRK9R0//drxS06ZN5b333vNa5S5TGzXySk8++aRstdVW\nXqvcy2Nj3Rc4fPhwOeaYYzy30+G7H3jgAc91eszocVVZWRnlqZm/+uorufDCCz2307PPetms\nV9LjXI/xWGnChAnSqlUrz9Wnn366/Pjjj57r9G9R/ya90p133ikvvvii1yr3Mt/zzz/fc53e\nG3nNNdd4rtMfd+6//37Pdfp+pX87Xknf2/RvJ1bq16+f+z7otX7cuHGyww47eK2SK6+8Uv77\n3/96rtNLpU844QTPdc8884zcc889nusOPPBAufHGGz3X6Q9p+rfjldq3by+vvfaa1yp3Wa9e\nvcLvezUzjR8/XjbccMOai935c889V/T93SvplQgHH3yw1yoZPXq06N+OVzr++OPl0ksv9Vol\nH3zwgVx++eWe67bbbjv3KgivlUuXLpUjjjjCa5W77MMPP5SGDRt6rtf66OexV9J27Lrrrl6r\nZOTIkaIjTnqlc845R/Rvxyup92233ea1yr2MXn849UqzZs1y/3a81unl+e+8847XKnfZAQcc\nIOXl5Z7r9XjcfPPNPdddcskloj+WeqURI0a431O81unfzcMPP+y1yv1R+IorrvBc99lnn7m3\nGXit3GKLLdwfkL3WrV69Wvr06eO1yl2mPwQ3b97cc71+F9Efpb2SXuGz1157ea1yv4u88sor\nnuu07/UY8EpvvfWWXH/99V6rpEePHqK3U3ilOXPm1PqeFcqnVwO9//77odlar4cffnjMH9z1\n80ivYqqZ9DNd/xYnT55cc5U7r7eH9O/f33OdfhcZM2aM57pDDjlErr32Ws91U6dOdW/98Fq5\n0UYbycsvv+y1yv0OG+8WFP1e0a5dO89t9buI3nbilfS7iP7teCX9/qP383sl/S5y8cUXh1ep\npaYVK1a479P6t+OVdtxxR3nooYe8VrnfB44++mjPdbpQv4tYluW5XmOJ+fPne67T7yI77bST\n5zr9HhvrJIp+Fzn55JOjttNYJdZ3iciMDSJnvKb1gNb0119/1VqtX9j0Dea0006TM888M+YX\n31obxlmgOPomGpn03iftuOXLl0vr1q0jV7mYyebXL3MaaEUmfcPSDy2vpINShA6Ymut1Xazt\n9EtsrO20DrG2031EbhcKkELLlyxZ4n4hrVkXna+qqoraNjKPHuyx9llRURFzOx3COdZ2OnBG\nZF0j96dlxtpO+zDWdtqGWNtp+cFgMOa2enzG2la/eEfuM3Ja+yrWdtr+yLyRbYzX/+oda7t4\n/R+vL3TfWs9YAVLNNkbWNV7/xzvG4/W//t3ofYKaarY1Xv+H/pYj6xea1v6N1ReaJ94xHq//\n47nqcRxrn/GOcf0SF2u7uh7jGiDVtAzZ6Gu8NupxFWvbuh7j8fo/3ntcvP7XKwJi1VPbqKaR\n73u6LJTi9b+ax+oP7atY+4z3/h+v/+O1MV7/JzrGdX1JSUmoyVGv8fo/3t9/vDbG+/uvj/e4\neH2ojdV7jXW/XinetvXxHhfvGFfvWMebfkeJdbzp8ljbaZvjfcbFO8br+h4X7zMu3ntcvM84\n7adYbdQvprFstP26Xaz7zWO9x+n7Rbz3uLoe4/H6P97nuP6IG6uNyfz9x9q2Pvpf3xsi9xd6\n79XXeP2fznuc7i9WgKR9H+vYifcel2r/R7ZZj7tYyTIQ1acpYuUwy/UMjv4qohGa/spdM+mv\nTPpLtP6irL9+6a/nSRRbsxh3XiN2xYv8dfbrr792o9x3333XvR8pcsNU80du6/dpNdSAUd9U\n9GwdKT0B9dSzRx07dkyvILZ2BdRSTfHMzAGhnvrrYc2z5JkpvbhK0Q9Z/dKoxyae6fe9euqZ\njUxcJZJ+bfK7BA0u9IupfqbjmX5fqqf+8OL13TT90ourBA2YNfjZYIMN8DRdH329WoxjQYMQ\nPS2mlzdEBi6h7Jdddpl7GYee5o91aUkob6JX/YKg0WBk0j8A/VUmdDYrcl2q+SO3ZRoBBBBA\nAAEEEEAAAQQQiBRIKkAaNGiQe011hw4d5JtvvoncPjyt16PrtZyx7hEJZ0wwoWeq9D4dPU0b\nSnpNe837kkLrUs0f2o5XBBBAAAEEEEAAAQQQQKCmQFIBkm6kN/PpzZdPPfVUzTLC8/q8opkz\nZ6Z1L1LoBkbdj14n+Msvv4jeLBi630F3pus0aNKUTH43I/9DAAEEEEAAAQQQQAABBBIIJByk\noeb2kdd56qABkWd6QnljjRgUWh/vVS+j05FTdOQdDYT0jJSO+qSjG4WSjq6moxbpqE3J5A9t\nxysCCCCAAAIIIIAAAgggEE8g5QBJb8rWYQH1Ya56o2GspPnqmnbZZRfRYSkXLFggOixszZts\naw7nmCh/XevBdgggEFtAh5bX0Xx0qGgSAggggAACCCBQKAIpB0j6nA4d/17Hwdfx3GsOsZ1J\nmFRHx0o1fybrSlkIFJuAjl6po94QIBVbz9NeBBBAAAEEClsg5QDp6aefdof8/uSTT2I+0K6w\nyWgdAggggAACCCCAAAIIFKpA0oM0hAAaN27sPoE21tO+Q/l4RQABBBBAAAEEEEAAAQTyTSDl\nAOn444+Xb7/9Vr788st8ayv1RQABBBBAAAEEEEAAAQTiCqR8id1ee+0lDz74oBx44IFywgkn\nyOabb+4+xbjmXoYPH15zEfMIIIAAAggggAACCCCAgK8FUg6Q5syZI3feeaesXLlSxo0bF7Nx\nBEgxaViBAAIIIIAAAggggAACPhVIOUB68skn3Ye0XnXVVXL44Ye7w3D7tG1UCwEE6lFgv/32\nk4qKinrcA0UjgAACCCCAAALZF0g5QJo2bZp069bNfZhr9qvLHhFAwC8CeiY5need+aUd1AMB\nBBBAAAEEEIgUSHmQhl133TXuA2IjC2caAQQQQAABBBBAAAEEEMgngZQDpFNPPdX91XjYsGFS\nXl6eT22lrggggAACCCCAAAIIIIBAXIGUL7GbMmWKdOrUSe644w53sAadbtOmjViWFbUjvRSP\nhAACCCCAAAIIIIAAAgjkk0DKAdLSpUtl7dq10rNnz3xqJ3VFAAEEEEAAAQQQQAABBBIKpBwg\nDRo0SPQfCQEEEEAAAQQQQAABBBAoNIGU70EqNADaU1gC9r2jJTj8ypw1ylm+PGf7zvaOf/jh\nB5kxY0a2d8v+EEAAAQQQQACBehVIeAZp7ty50rdvX+nVq5c8+OCDMnr0aLn//vsTVuq7775L\nmIcMCGRawHnldXGmmvvfbr0p00UnLC945dUi07+XkldfSpi3EDIMHDhQysrKZNGiRYXQHNqA\nAAIIIIAAAgi4AgkDpEAgIM2bN5fGjRu7G5SWlrrz+CHgRwHr6ivE+is3Z3Gc0WPF2nsvP7JQ\nJwQQQAABBBBAAIEkBRIGSBtuuKF8+umn4eL+8Y9/iP4jIZBJAaeqSqSkpNZoiLH2ofmtBrUP\n38D+vWNtUr08GPRcH6u8mpmdykqxGjasuTip+WT3kVRhZEIAgbwQcGbNEllr3je6bp8X9aWS\nCCCAAAIidboHyXEcmT17tnzyyScyZ84csW0bSwRSFnBWrZLg4KFS1XVnCTZt5f6r6rar2Pc/\n4D5rK1Sg88WXbh7ns88lOOJfUrV1Vwk2aiFV2+8k9mP/CWVzX4NnDpKqXuuDpODpZ4sucz79\nTIL795U2W2znbm/fcLOb337tDanq2UuCjVtKVafN3X1HFWhm7P/7r1Ttf7BUddhYgqVmvyZf\n8LgB4vz6q5vV+flnt35i2uNM/sidtl8aHy7GHvugVO28W3X72nWS4NF/C28bzsQEAggUpIA9\ndLjYQ4YVZNtoFAIIIFCoAikFSJMnT3bvRWrZsqVsttlm7vSmm24qTZo0kWOOOSbqTFOhgtGu\nzAg45kxOcPe9xXnoEbH23UcCY+4Ra9BZIstXiH3+JeI8+/z6HZn7XGTGjxI8+1xxHhgn1qF9\nxRpxucjKVWKfMUjsNyesz/v7bJEfZ0bNOxPfleCh/cTabFNZM/hCd5199UgJnnWO2H8/Vayt\nt5LAteb+IXMGy93351+Et7efflbsPoeJudlGAoMvksA9d4q1807ivPq6BA8/ujqQa9VKAmec\nJqJnljp3cqetbbdxywiee6HY510sYtobuOVGCQz/pzhffS3BnXuK88OP4f0wgQAChSdgT3pX\nnNfeFH0Psl9/s/AaSIsQQACBQhUwZ4MSpmAw6Fx22WWOuR/JadCggXPQQQc55jI7Z/DgwY4J\njJwdd9zRMT7uvzvvvDNheWRITsCcmXPMIBnO4sWLk9sgj3IFXxrvVEojJzjyhqha21M+cpdX\nnTQwvNx+/wN3WeXGWzr2ypXrl3/xZXXeU04PL6s68BCnss2G6+f3P7h6P7fc7qjn/PnzHfub\nb6vLsxo59qefhfMGJ06qznvjLeFllTv1cMuzly4NL9OJqj6HuXntH2eGl1e2bOdUHdYvPG9/\n8qlTafZRdcTR4WU6Yf/xh1PZsFlU3qgMeTJjfhxx2rVrlye19X81FyxYYH43CPq/onlQwyVL\nlrjvnbn0tCsrncodule/15j3usqtuzp2RUUe6NWuonpW5Gnda7cmt0uWL1/uHpt4ZqYf1HP1\n6tWZKazIS1FH/c6JZ/WBUPsmDo9I8KWXXpLbbrtNunbtKhMnTpTOnTvXyqVnly644AIZOnSo\n9OjRQ3r37l0rDwsQCAlYxx4tJXPMtfnt24cWVb9uuolIgxKRFSuil5s56/jjxDIDhoTTrruY\nszbmEF68JLwo1oR16snrV+24g0jLFiLt2om1x+7h5dZ2XdxpZ/6C8LKSjz8wZ7WWi9W6dXiZ\n3ockmvfd//OsZyijrWfBzM8G1kXnhxa5r5b5+7H6HCjO2xPFMWemrKZNo9bny0wrc+asUaNG\n+VJd6olAVgUcc2mtTI8YBn/Wz+KMuk+sYZdmtR7sDAEEEEAgdYGkAqSbb75ZOnXqJG+//bZn\ncKS73XfffeX111+X7bff3h0KnAAp9c4opi0syxKnQwdxXnhJ7M/MJW3fzxBHL437489qhmDt\n+9osczlnZLLMCItiLveUiorIxbWnG5sv8WawkVDSfYsGJRutX+auM5eKusncYxdKVrNm4sw2\n99k9/Jg4Zghvt45azzXl1VliDPrgrvzJBIAm6SV2ttY1Mi0xQZ3uxnxpkp26Ra7Jm+lJkyZF\n3SuWNxW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Ba5NNpOTJx/K6LVxil9fdR+URQAABBBBAAAEEEEAgkwKc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\nCF0AAAAASUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fviz_mca_var(mca.object, \n",
        "               repel = TRUE, # Avoid text overlapping (slow if many point)\n",
        "               ggtheme = theme_minimal())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "above-creek",
      "metadata": {},
      "source": [
        "Vous voyez bien que sur la dimension 1 ergative est lié à `adult`, `animate` et `subInitial`, se différenciant nettement d'other. Sur la dimension 2 ergative est toujours plus proche de `adult`, mais se regroupe avec `subNotInitial et inanimate` cette fois-ci. Pour savoir quelle dimension regarder nous avons besoin de savoir les contributions et qualités de chaque catégorie à ces deux dimensions. Nous avons déjà abordé ce point plus haut, ici nous utilisons des graphes pour mieux comprendre cette question.\n",
        "\n",
        "Le graphe suivant permet de savoir les corrélations des variables avec chaque dimension. Vous voyez que la dimension 1 est surtout corrélée avec Animacy, Age et CaseMarking et la dimension 2 avec WordOrder. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "metallic-spanish",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:32.962794Z",
          "start_time": "2021-04-21T12:47:29.364Z"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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2abihRERH7/3c3VgEprSlwN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\nNFzgDwM+gQUkJrALAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fviz_mca_var(mca.object, choice = \"mca.cor\", \n",
        "            repel = TRUE, # Avoid text overlapping (slow)\n",
        "            ggtheme = theme_minimal())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "processed-berkeley",
      "metadata": {},
      "source": [
        "Combinant les informations des tableaux et de MCA, nous arrivons aux conclusions suivantes :\n",
        "\n",
        "1. Au niveau de l'âge du locuteur c'est surtout la dimension 1 qu'il faut regarder,  l'utilisation de l'ergatif est surtout liée à l'adulte, contrairement aux autres cas qui sont plutôt liés à l'enfant.\n",
        "\n",
        "2. Au niveau de l'ordre des mots c'est plutôt la dimension 2 qu'il faut regarder. L'ergatif est plus fortement lié à subNotInitial qu'à subInitial par rapport à other. Cependant l'ergatif et les autres cas sont tous utilisés plus souvent en subInitial, ce qui est très visible sur les tableaux mais moins sur le graphe de MCA.\n",
        "\n",
        "3. Au niveau de l'animéité du sujet, l'ergatif et les autres cas sont tous utilisés plus fréquemment quand le sujet est animé, ce qui, encore une fois, n'est pas facile à visualiser en MCA. Cependant sur MCA il est plus facile de voir que l'ergatif est plus fortement lié au sujet animé que les autres cas.\n",
        "\n",
        "Vu que les contributions de animate et d'inanimate sont grandes sur la dimension 3, nous décidons de regarder aussi cette dimension."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "hairy-mexico",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:33.266298Z",
          "start_time": "2021-04-21T12:47:29.452Z"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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04vvPCCPvzwQ1WpUiVaNpYhgAACCCCAAAIIIIAAAgkVKJMAKdxCM3CD+SEhgAAC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\nAggggAACCCCQtgIESGl76Gk4AggggAACCCCAAAIIFBQgQCoowjw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A8xFAIHUFvvrqK910003KyMhQ8+bNU7ehtAwBBBBAAIEYCtCDFENM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wi8l72RUia9aKdDw05+yPOYvkzPlZ7JN7\nizRpLIFht7n57aeeEWfyWyJr17kDLeg5IicYlGCnI0WWLBXr3LPFumaQu67zxlti/3eQBGrX\nFuusf4ulZ4oW/SrOO++LddIJYh24fw6wWU8WLHSnrT32EKlfX5wRL4hzx1CxqlTJyaPlvDpB\nZN58s52u7rzINmaecrLMb9JIWi37s0AbwxtgAgEEEEAAAQSiBAiQojh4gQACqSxgX329yIaN\nkvHtl2IdekhOUy84T4K1aokzfIQ4A84T67DOeQSad8EcsUwwE0rBf58jUq2aZHxjtpE73zrP\nnPnZ/+BQFvev8+bbIibICgwbIoHbbgkvc/r1kWCXo8R5+10REyAFTjlJ7MW/uQFSoL8JpDp1\nDOeNnAiYutnnTXPXs8w2Qsl+aaxI82Y5gZaZGdnGTNPGqosWSZWsLAn+9yrvNoY2xF8EEEAA\nAQQQcAW4xI4dAQEE0kLAWb9enCkfihzWKS84ym154Pz+5lSMCS7eeDPKwjr5xHAQpAv0cjYN\neqwLz4+ab5kAKzDwkuh1Tz9VMpb9KtYN10XNl72bimSaO4M2boyeX8QrPeMlNaqL8/Ir4ZzO\nvF9Evp8tAROg6b1Nu9PG8MaYQAABBBBAAAFXgDNI7AgIIJAeAuYyNjeZy+yy9z0gus2h+49C\neXKXWvu2jMrnmGBEk9UqK2q++6L1flHz3IDFXBrnTHxN7JnfmsvgfhFHL5n7c3lOvmDO/UxR\nKxXywqpaVfTMkfPiy+KsXStWvXrinj0y1/PpGSw3heof0ca9zOWE2RUrmugu9x6rUJ5CymIR\nAggggAAC6SxAgJTOvU/bEUgjgfBDWNu2Cd+vk7/5VrN9omeZMzZRafv2nJc6Il3+FApAcufr\n2Sa9lE5+XSyyz95ide7onmXS+56C3XrmX7tYr/Uyu+ALo8WZMEnk0ovFGTvO3EfVXaxmzdz1\nvdq42dwXVbde3fD2C7QxvIQJBBBAAAEEEFABAiT2AwQQSAsBK2vfnHaaMyoZZqCDyKQDKohe\nPte4ceTsAtOhbTgLFxVYFhpQIbTAHna3GxwFXnxBAv3NfUu5yfnrL5FdJsDKHREvNL84f60j\nDhfZL0vs8ZMk4wAzmIMZfMG6e1h41VD9dBCKUBvXm3uQGph7kIrbxvDGmEAAAQQQQCBNBbgH\nKU07nmYjkG4C1p57usGFmMvd3EvdIgCchx+T4J4tzChxIyPmekweaC7Na3eQOP83Rpw1a8IZ\n9N4fe/jz4dc64fw8131tHZ8zbHdooTPx9ZzJyLNQek+SptAZqpxXnv8HzKASYoYSt595TqRm\nDbHOOD2cLy5tDG+NCQQQQAABBNJTgAApPfudViOQlgKBJx4xN+yIBE8+XWxzb5AzY6bYd94j\n9m3mLEyXI8S65KJCXfS+osAjD4hs2SLBQw7PWffu+9xp2bQpZ93cB8HqJXWa7CuvdsvRsoJD\nbhf7uhtEKldyB1TIWcH8b+4n0mTf96DokOGFJcuMdKfJmfCae0+S3psUmSLb+PPtd8gDZ/SR\nuWf3L3YbI7fFNAIIIIAAAukoQICUjr1OmxFIU4HAMb0kMPV9EyRZYvc7R4KHdzOBwx1idTlS\nMsaMdEeCK4omYO75yfjiY7H2by3240+JPepFE6icIYHbh7irWmYIcE362rrqv+J8+bVbjpal\no+hlfPmZWH3PEPlpjjirV+esY0bLk65d3OX2ldeIEwq23KXR/1lNmoh1bC93ZuCC/0QvNK8i\n29h62D3y7JwFst+rE0vUxgIbZQYCCCCAAAJpJMA9SGnU2TQVAQRMAHFUDwks+Fmcdevce3ik\n6V5i1c0bxECNAj2PloCTOyBDBJqjAzGsWCFWx0MlY4p5zlFEsofdlfMq92yQBkoZj5szVuaf\ne0lf40Zi1azp5sl40ZxdevH/wmtbNWpI5udTxckd+ltfZ3w8Jbw8/0TGu9HDkedfHmrjayNH\nyh0XXyID775bLrv5pvzZeI0AAggggAACHgKcQfJAYRYCCKS+gAZF1sEHFQiOCmu5XmIXPLC9\nZB/2r6hs7j1II14QaVDf3OfUKmqZvrDMvFBwVGBhxAzNE5nPMfcp2W9GB2IR2Yuc3FW9uvxk\nHvC0o1r0ZXhFrkgGBBBAAAEE0liAM0hp3Pk0HQEESi5gmYfKOo89Jdn/OkoC5lI3HazBec+c\n7THPJgq8Ze4Lyr0HqeRbLriG8+xzYg8ZJtaiuWI1aFAwA3MQQAABBBBAIO4CnEGKOykbRACB\nVBYIPHifBMa9JDqkto5c53zwkVhHHiEZH7zj3v8Tr7brw2DtoXeKbNgo9q23x2uzbAcBBBBA\nAAEEihDgDFIRQCxGAAEEIgWszEyx/t1XRP+VYdLBI+Sf9W4Jzsj/E+e/l7qXBJZhkWwaAQQQ\nQAABBIwAZ5DYDRBAAIEkE9BnKDnPRTxXyXYkOOjaEteyU6dOcs8990ivXjmj3pV4A6yAAAII\nIIBAGgoQIKVhp9NkBBBIbgF78HXmYU12dCU/nyb2pNej5xXxqkWLFtKnTx854ADzgFsSAggg\ngAACCBRLgACpWExkQgABBBIjYE9+S5yPP/UszL7+JnG2Fxx+3DMzMxFAAAEEEEBgtwS4B2m3\n2FgJAQQQKCOB5cvFunZQ7I3/Ml+kfbvYy1mCAAIIIIAAAqUSIEAqFR8rI4AAAvEVCFw+ML4b\nZGsIIIAAAgggUCIBAqRicjmOuUk6GJRs8+DG8kzJUo/yNAiVHeqPQIArRW3bFv1X3vtnqG/K\n+y8WeT2gxwz2ixwP9ou8/UItNLFviHvsZN/I2Tf0c5X9IsdC/2e/yLNI1u+f2kfxfP5gqMUE\nSCGJIv7qjrF161b3zVJE1jJdrAevbdu2ya5du8q0HD9sfMeOHW7QioXIdnNfin7R2bJlix+6\nrszruHPnTixylfXYxX6Rg8F+kffWUwv9cYl9I+dLsH6eYJEXLGLBMSPvaJEzpd8x9LuGfqYk\nU9JjWeXKleNeJQKkYpLqB0mNGjWkatWqxVyjbLKtX79eqlevLpUqVSqbAny0Ve0TdahYsaKP\nal02VQ39elKrVq2yKcBnW9UDORYiP//8szz33HNy7rnnSufOnX3Wi/GvLvtFnqn+sJSRkcH7\nxJCEzqZxzDCDZ5ofYdetW8d+kftW4ZiRd8zYvHmzVKtWzf2XN7f8pzQ4Kosribg2qfz7lhog\ngAACZSKgAdJTTz0lM2bMKJPts1EEEEAAAQRSUYAAKRV7lTYhgAACPhCw775PnNx7HnxQXaqI\nAAIIIJAmAgRIadLRNBMBBBBIJgHnq6/FvvV2cZ59LpmqRV0QQAABBBAQAiR2AgQQQACBhAq4\noyFddY1bpj30TnHMPQ8kBBBAAAEEkkWAAClZeoJ6IIAAAmki4Ix+UWTWDzmtXfePaJBEQgAB\nBBBAIFkECJCSpSeoBwIIIJAGAs6mTWLffFtUS53hI8SZOy9qHi8QQAABBBAoLwECpPKSp1wE\nEECgjAVatmwpffr0kTZt2pRxScXfvH3XvSIrV0WvkB0Ue/B10fN4hQACCCCAQDkJECCVEzzF\nIoAAAmUt0LFjR7nnnnvk6KOPLuuiirV9Z/FicR570jOvM/UTsd96x3MZMxFAAAEEEEikAA+K\nTaQ2ZSGAAALpLLBxkwSeezq2QLXyfRB37IqxBAEEEEAgnQQIkNKpt2krAgggUI4CVvt2ov9I\nCCCAAAIIJLMAl9glc+9QNwQQQAABBBBAAAEEEEioAAFSQrkpDAEEEEAAAQQQQAABBJJZgAAp\nmXuHuiGAAAIIIIAAAggggEBCBQiQEspNYQgggEDiBFauXCnTp0+XpUuXJq5QSkIAAQQQQMDn\nAgRIPu9Aqo8AAgjEEvjss89kwIAB8sYbb8TKkvD5zsJFErxisDjTvyyybOe339y89mu7V3/7\nyacleMPNUeU42dnibNkSnqdDj7v1+fyL8LziTtgPPSrB24YVNzv5EEAAAQR8IkCA5JOOopoI\nIIBAKgg4f/4pztPDxfl5btHNWbEyJ++06UXn9cjhTH5bnJGjwksCc+dJsH0nEfM3nP5akVPG\nT3PCs4o7YU98TZwxLxc3O/kQQAABBHwiwDDfPukoqokAAgggUDIBa8hNYq3fEF4p8wsTaP0c\nERzpkv1bS2D8yww/HlZiAgEEEECAAIl9AAEEEECgWAJOMChiWWIF4nvxgW7XysgoVh28Mull\nc1ZmwY+zQPduXtmj5ln164vVr0/UvMgXum0xdbNMu0kIIIAAAukhEN9PufQwo5UIIIBAWgnY\nk9+S7A6dJVillgSr15Vsc5ma/ULepWuKETzzXAn2PL6Aiz1ipGQfcLA48xdEL9u+Q4IDr5Ts\n+k0kWLO+ZHfvJc6s76PzeLxyvv0uZ3szv5HgLbdJ9r4HSLBSDcne/yCxR78YtUZwwCWSfURO\nkBS85nqp9MTT7vLgv01djzrWndYytX722FfD6zqbN0tw8LXu/GDV2qL/stt2EPvZ58RxnHA+\nJhBAAAEEUlOg4E9uqdlOWoUAAgggsBsCOpiC3edMkU4dJXDvXSIVK4g9bqLYFw0U2blTAgMv\ndbfq/P6HyMpVBUtYs1bkFxMcbd8etcw2wY003UsCw4aIZAfFHfDgyO6S8emHYh1+WFTeqBdb\nt7rbC150mYi5R8k6s59I3Tri/N8YsS+4RKRBAwmcmBuoLVkqsmChu7plziYF5/4igQ+ninXS\nCWIduH/OZnO3J+vWua/1bFaw05EiZl3r3LPFumaQOHN+FueNt8T+7yAJ1K4t1ln/jqoSLxBA\nAAEEUkuAACm1+pPWIIAAAmGBWrVqScuWLaVu3brheSWdsF+fbE4P2ZLx2jixGjd2V7fO6y9B\nE8w4JuDY7VSzpmTMmCaWCTg0Wb1PlWCrNhK8aYhkfvZR0Zs19xZl/LFQrOrV3bzOKSdJsOOR\n4oybIBIKkCK2EjDLs81ADBVMgBTobwIfE/B5JefNt90ATAO3wG23hLM45jK8YJejxHn7XREC\npLALEwgggEAqChAgpWKv0iYEEEDACBx//PHy/vvvS1ZW1m57WHvvLXpRmX3jrRIwZ1Osgw8S\nywQ3mXOKvhyusEIDAy8JB0eaz2raVKwzThdn7DhxNmwQywR3hSWrb+9wcOTm69BepIL5SNMz\nVqVI1umnSsayX90zUVGb2bupSKa5T2rjxqjZvEAAAQQQSD0B7kFKvT6lRQgggEDcBKwL/iNW\nj27ivDhWgu06SXaTZhK88FKxP5paujLM6HH5k5W1b86sRSZAKSJp4BaZ3IEjTOAmO3ZEzi7x\ntDsYwx57iDPpdQledY17X1V205YS3NsEmeZSQD2bRkIAAQQQSG0BAqTU7l9ahwACCJRKQM/k\nBD56TwLvThbr0ovMPUgVc+73OcZc0nbl1UVu2zH3KXmmSpUKzg6NZBf6WzBH3hxzL1RZJGfV\nKgke2F7scy8Q5613ROrVNfdZXSIZX32Wc4aqLAplmwgggAACSSXAJXZJ1R1UBgEEEEguAWfX\nLhETNAROOE5E/5nkmHt5gqf1NQ9YfVYcfdaQOeMiFUzA4hUM6eANXsk8MDZ/cn77PWfWvi3z\nL0rYa3vY3SK/LpbAiy+Ye5XOCZfr/PWXyC4z5LfNGaQwChMIIIBAigpwBilFO5ZmIYAAAvEQ\nsM85T4JmKG1n2bLw5qyD2op1VHcTKZlZW7a48606ZrCF1auj8um9RM60L93l+f+z9V4jM2Jc\nKDlr1riXtcmRh4tVo0Zodnz/hp6VlG9EvchCnJ/nui+t43OGAQ8tcya+njOpz0UiIYAAAgik\ntAABUkp3L41DAAEESicQuPN2c9+NGfraPDvIHjVGnC+/Evvu+8R5+RWRwzuL1by5W4B18onu\n/TnBvmeLPWGS2GNekuAxZp7XWSVdwzx/yO57ltiffS72u+9LsMcxImbI7YyH73e3Vxb/OXXq\nuJu173tQ7Kee8SzC6pwzup1tLh90Zsx0/wWH3C72dTeIVK4kzvr1nusxEwEEEEAgdQS4xC51\n+pKWIIAAAlEC2eZsxxZzhmeXuUyugl4CtxvJ2q+VBB5/WOwhw8QekPPMIw0ULDNsdmDk8PAW\nrQvOE8s8c8h56lmxTTAllSqKdfaZYv3vWvMcpbPC+UITgWeeEHv48+L0yD1T02wfyfh4ilid\nO4WyxP3vrmN7iXTtIs6UD8V53zxvyQxXnj8Fbh8itgnq9LlHQfO8Jzcd2kEyvvzMBFXmksJX\nx4tjzpRZ5nlLJAQQQACB1BSwzFPBeSx4Mfp2ypQp0rVrV6latWoxcpddliVLlkijRo2kktcN\nzmVXbFJuedOmTa5DRXPTeLqnjWbo4a3m13fdN0gia9eulXr16qVBBbcLAABAAElEQVQ9xbhx\n4+Sss86Sxx57TAYNGlQqD0cvLdPL7LZtF2mVJVbocrV8W3UHZTD38EiL5mJVrpxvacGXzooV\n5t6eXZJ/VLqCOUs/J7RfOLlDdetw5YUlRx8y27iRO6x5Yfn8uGyNuaQxwwyGUSf3rJof2xCv\nOtvmvrIN5nJQLPRkcVD++OMP9/lp8fL183ZCxww/tyFedV++fLnUNs+tq1atWrw2GZftzJs3\nTwKBgLRuXXBk1NIUwBmk0uixLgIIIJAmAm5AlHs5XWFNtvQHiwP2LyxL1LLQw2ejZpbxi6IC\no1DxevaMhAACCCCQfgLcg5R+fU6LEUAAAQQQQAABBBBAIIYAAVIMGGYjgAACCCCAAAIIIIBA\n+gkQIKVfn9NiBBBAAAEEEEAAAQQQiCFAgBQDhtkIIIAAAggggAACCCCQfgIESOnX57QYAQQQ\nQAABBBBAAAEEYggwil0MGGaXj4AOv1vcEabKp4aUioB/BM444wz57rvvpE2bNv6pNDVFAAEE\nEECgnAU4g1TOHUDxeQLOtm0S7Hq0OOYZNiQEECi9gD4ctqZ51g/PTSu9JVtAAAEEEEgfAQKk\n9OnrpG+p88DDIj/OEXvIsKSvKxVEAAEEEEAAAQQQSE0BAqTU7FfftcpZtkxsDZBMckaMFGfO\nz75rAxVGAAEEEEAAAQQQ8L8AAZL/+zAlWmD/72aRrdty2hK0xR58XUq0i0YggAACCCCAAAII\n+EuAAMlf/ZWStXW+/EqccROj2uZ88pnYr0+OmscLBBBAAAEEEEAAAQTKWoAAqayF2X6hAo7j\nSHDQtZ557OtuFGfHDs9lzEQAAQQQQAABBBBAoCwECJDKQpVtFlvAGf2iyKwfvPP//oc4jzzu\nvYy5CCBQpMCUKVPkpJNOkrFjxxaZlwwIIIAAAgggkCPAc5DYE8pVIHDBeaL/SAggEH+B9evX\ny8KFC2XNmjXx3zhbRAABBBBAIEUFOIOUoh1LsxBAAAEEEEAAAQQQQKDkAgRIJTdjDQQQQAAB\nBBBAAAEEEEhRAQKkFO1YmoUAAggggAACCCCAAAIlFyBAKrlZ2q/h/PqrBK8YLM606UVa2E8+\nLcEbzDOOcpP90KMSvG1Y6GWhf4ODrxV75P8VmoeFCCCAAAIIIIAAAgjEU4AAKZ6a6bKt5X+J\n8/Rwceb8XGSLnclvizNyVDifPfE1cca8HH5d2ITzzHPivP9BYVlYhgACCCCAAAIIIIBAXAUY\nxS6unGwsv4A15Cax1m/IP5vXCCCQAIFu3brJiBEjpFevXgkojSIQQAABBBBIDQECpNToxzJr\nhZOdLVZm0btJrHyB7t1KVDcnGBQrI6NE65AZAQS8BRo3bizdu3eXZs2aeWdgLgIIIIAAAggU\nEOASuwIkzHAWLJTggEsku/l+EqxUQ7L3PUDsZ58rCJMdlOCtQyV7nywJVqkl2YceLvbLr0Tl\nc7dzROFBkrN9uwRvHiLZBx0iwaq1JfvI7uLM+j5qO7xAAAEEEEAAAQQQQCARAkWfGkhELSgj\naQScv/6S4DEnimzbJoGrrxKpU9sEPa+K/d9BIvu2lECvnuG62kNuF6lfX6xzz3LzO2PHiX3e\nhSL16kng+GNz8i1ZKmICrsKSPfBKc1/SS2JdcpFY1wwS5/NpEuxmyjFnr0gIIIAAAggggAAC\nCCRSgAApkdo+KMu+4BKRlSslY+4PYu27r1tj6/z/SHC/tmJfe4NYP3yT14patSTjy0/FatTI\nnWcf01Ps408V58OPREIBUl5uzyn79cnijH5JArffKoGht+bk0fIa7iHO/Q97rsNMBBBAAAEE\nEEAAAQTKSoBL7MpK1ofbdRxHnK9niHXqyeHgSJthVa4sGR+9JxkTXzUvrHDLrJNOCAdHbr4e\n3UWqVBaZX/gZo/AGzERoqHDrqssjZ0vg+muiXvMCAQQQQAABBBBAAIFECHAGKRHKfilj8WKR\nTZtFsnLOHEVW22qVFX7p5E5Z5pK7yGRVqiRizio5W7ZEzi58+sc5InXriFWnTlQ+y1ymJ/XN\nPxICCCCAAAIIIIAAAgkU4AxSArGTvqgNG3OqWKFC8apa2QREXsmciSpucnbuFIk1Sl61asXd\nDPkQQMBD4LvvvpPbbrtNPvnkE4+lzEIAAQQQQAABLwECJC+VdJ3XskVOy3VghXzJNvcV2Xfc\nLTqIQzyTdWgHkb9Xi7PZnLmKSDpsuCxfHjGHSQQQKKnAr7/+KuPGjZM5c8yZWhICCCCAAAII\nFEuAAKlYTOmRyapdW+SgNuJMfkucjblnk3KbrgM02PfcL1KjRlwxrMM6u9tzRrwQtV1nrLnf\nyQwjTkIAAQQQQAABBBBAIJEC3IOUSG0flBV45AGxe54gwaOOlcAdQ0UqZIoz6kWRn+eJdeN1\nYpkAqfgX0BXdYOvffcWaMEnsG28R2bVLrKN7iDPzG7FvGiJSsZiX+hVdDDkQQAABBBBAAAEE\nECiWgK8DpGAwKLNnz5Z58+ZJ69atpWPHjoU2Wi83+e2336Ly1K1bVw499NCoeen8InD0USIf\nvC32pVeIfeJpORTmXiPr1hvNUNwmaIlzssyoeIGXR5vyLhf73gdEbjRDfVevJoFnnhB76J1x\nLo3NIYAAAggggAACCCBQuIBvAyQNji677DJZsWKFdOnSRSZMmCA9evSQa66JPTz0q6++KtOn\nTzdXieVdJta2bVsCpHz7SOCYXhL4fYE4q1aJrF0nYu5Nckeoy81ndesqmc72fGvlvMxcsSRq\nfsbHU6JeZ86cHvVaX1hVq0rGS6PEve/o18XuKHpWRoYE+p9TIC8zEEAAAQQQQAABBBAoSwHf\nBkgaEG02N/aPHz9eqpnRzpYsWSL9+/eXE088Ufbbbz9Ps4ULF8rFF18sffr08VzOzGgBq2FD\nEf2XoGTpaHatvfsuQVWgGAQQQAABBBBAAIE0F/DtIA16JqhXr15ucKR9uM8++0ibNm3ko48+\n8uzSHTt2yNKlS2MGT54rMRMBBBDwscCBBx4oAwcOlE6dOvm4FVQdAQQQQACBxAr49gySXlrX\npEmTKC19/ffff0fNC734/fffxbZtmTFjhjz22GPu2Se9JO+CCy6QSvqA04iUbYaY3qnP54lI\nuq7O32UGEijP5JhnDGk9AgHfxrZx49PLLNVC72NK96QWuo+W9/6ZLP2gHliYE7Lm3szBgwdL\n8+bN8TA7J/tF3jtULTTxPhHRz1X2jZx9g/0ixyH0P/tFSELc7xjJ6KHffcrie6AvAyT9Urxm\nzRqpWbNmXs+ZKX2tl9F5pUWLFrmz9UzS5ZdfLvoAxTfeeEPWrVsnN998c9Qqq1evlh9//DFq\nnn6IaFBWsWLFqPmJfqGB28qVKwmQDLy+UTVQLIs3RqL7tbTlq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+xKokVeBBBAAAEEEEAAAQQQSGkBAqSU7l4ahwACCCCAAAIIIIAAAiUR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\nRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot(mca.object, axes=3:4, invisible=\"ind\", title=\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "narrative-chambers",
      "metadata": {},
      "source": [
        "Effectivement ergative et other sont tous relativement proches d'animate, cependant comme on a évoqué plus haut, les contributions d'ergative et d'other sont petites au niveau de la dimension 3, rendant l'interprétation difficile.\n",
        "\n",
        "4. En guise de remarque finale, nous trouvons que quand les variables ne sont pas très nombreuses (comme c'est le cas ici), les tableaux de proportions sont plus faciles à interpréter que MCA. Nous pensons que le nombre limité de variables de cet exercice a quelque peu réduit l'intérêt de l'usage de MCA mais il est toujours d'intéressant d'avoir des graphes de MCA à ses côtés même lorque les tableaux montrent des tendances relativement nettes.\n",
        "\n",
        "Enfin, nous voudrions aussi montrer l'utilité la fonction unique de MCA qui consiste à comparer les catégories au sein de chaque variable. Cela confirme que l'usage d'ergative et celui des autres cas sont bien distincts.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "least-blair",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-21T12:47:33.477062Z",
          "start_time": "2021-04-21T12:47:29.530Z"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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nevmZOfhAACCCAQEgIESDlsxm3btl3wBt19+/bZq7j+WM/hJbN1eCCvna0C+/gg\n6u/+cCUf4wb56TRICuf2D5X6a6B7ockQ8uTJI6dPn/YqQMrOx/bAgQNSqFAhj0MLFChge59O\nnDjhsZ0VBBBAAAHnChAg5bDt+vXrd8EzjBkzxt7Yqzfz5nbSb861B6FSpUq5femguN7OnTvt\nHy8VK1YMivLkdiH0W/eoqCipUKFCbl86KK63fft20V6FcuXKBUV5crsQ+gWO/gFfpkyZ3L60\nT69XsmRJOwzu22+/9el5s3qysmXLyrFjxzwOi42NtUPsAvHz3aMgrCCAAAII+EyAe5B8RsmJ\nEEAAAQRCWUC/aNi/f79HFbVXSVO1atXsO/8ggAACCDhfgADJ+W1IDRAISYG4d0TizE+ouHzm\ndSokq0ilHCbQqlUr+f77722PkavoOuW3zmanEz2QEEAAAQRCQ4AAKTTakVogEFICGhyJ3o9v\nngclf5uX520fZgMJAf8L6IQPQ4cOTblQjx49ZM+ePaIPiz158qQsXrxYBg0aJDrU2puJIVJO\nxAICCCCAQFALECAFdfNQOATCVKBX2nrHdU27jS0I+FNg4sSJ9qGwrmtUqVJFJk+ebKcHj4mJ\nkQ4dOkinTp2kV690PrCug3hHAAEEEHCcAJM0OK7JKDACYSCQ19TxTKp6Jj2bM9VGVhHIucBT\nTz0l+kqddJKL1Onmm28Wve9Ip/zWiRmio/k1mtqIdQQQQMDpAvxkd3oLUn4EQlAg+rS578j9\nuZ1mOfreEKwoVXKkgD5Utnr16o4sO4VGAAEEELiwAEPsLmxEDgQQCIBAtN5/1N+8ppvgKCEA\nBeCSCCCAAAIIIBCWAvQghWWzU2kEnCEQ3dcZ5aSUCCCAAAIIIBA6AvQghU5bUhMEEEAAAQQQ\nQAABBBDIoQABUg4BORwBBBBAAAEEEEAAAQRCR4AAKXTakpoggAACCCCAAAIIIIBADgUIkHII\nyOEIIIAAAggggAACCCAQOgIESKHTltQEAQQQQAABBBBAAAEEcihAgJRDQA5HAAEEEEAAAQQQ\nQACB0BEgQAqdtqQmCCCAAAIIIIAAAgggkEMBAqQcAnI4AggggAACCCCAAAIIhI4AAVLotCU1\nQQABBBBAAAEEEEAAgRwKECDlEJDDEUAAAQQQQAABBBBAIHQECJBCpy2pCQIIIIAAAggggAAC\nCORQgAAph4AcjgACCCCAAAIIIIAAAqEjQIAUOm1JTRBAAAEEEEAAAQQQQCCHAgRIOQTkcAQQ\nQAABBBBAAAEEEAgdAQKk0GlLaoIAAggggAACCCCAAAI5FCBAyiEghyOAAAIIIIAAAggggEDo\nCBAghU5bUhMEEEAAAQQQQAABBBDIoQABUg4BORwBBBBAAAEEEEAAAQRCR4AAKXTakpoggAAC\nCCCAAAIIIIBADgUIkHIIyOEIIIAAAggggAACCCAQOgIESKHTltQEAQQQQAABBBBAAAEEcihA\ngJRDQA5HAAEEEEAAAQQQQACB0BEgQAqdtqQmCCCAAAIIIIAAAgggkEMBAqQcAnI4AggggAAC\nCCCAAAIIhI4AAVLotCU1QQABBBBAAAEEEEAAgRwKECDlEJDDEUAAAQQQQAABBBBAIHQECJBC\npy2pCQIIIIAAAggggAACCORQgAAph4AcjgACCCCAAAIIIIAAAqEjQIAUOm1JTRBAAAEEEEAA\nAQQQQCCHAgRIOQTkcAQQQAABBBBAAAEEEAgdAQKk0GlLaoIAAggggAACCCCAAAI5FCBAyiEg\nhyOAAAIIIIAAAggggEDoCESHTlWoSW4IxEWcv0p04vllJy/F1RWJWiESkf98LRLni8TfKBI5\n1WzbLZK4zry+NsumzhFlkt4ln3kval7lzLaqZlde817AHPOQOfYJs9zULP/b7E8nJUwy+ZeY\n674hkvCqydfXvLttS+cQie9sznmvydsuvb1sQwABBBBAAAEEEPCFAAGSLxTD5BzuwZFWWddD\nJUhyb8LELSbA+a/ZcsIELVeb9xLm1cC8EsxLg6MW5qWB4lnzOmpeG8yuH8z7ehP0xJnll81y\nQfM6Z9bvNlkLmeVUKeIWs/2mpI0JHyUFSO7bUmVnFQEEEEAAAQQQQCCXBAiQcgmay+SuQOJa\nE6h0EykWVVIiEiMk8X8mWPnZlGGjCUaeNctnTBB0gwnw5iaVK+F5s22VWd6a/Kph3huZV4zJ\nb4KcyB4m/0smqKlolh8z537f5J9u9ucx20ywE2l6duKvMeunzHons2+yeY012z4x24qZbZeZ\n95PmZQKoyClm33yzvNK8VzbvB0w+c/2IK5O2Rbxg1k0ZxdQh8ZDZXsf0NJlzkRBAAAEEEEAA\nAQT8L0CA5H9jrhAAgcRvzEUvNx08jx6SfEvyS8FjZvycCV4SY90Ks+/8ckRzs2+2WT9oAhIz\nLC7yIRMEDTXv5hVveowiTDDlSolLzb4xJmhZlrQlvpVZNkFR5INmXQOgp8wxJgDTXqeIUibv\nv8y5TXki2phtRc2y9jZFmPdj5jgTDCW8ZN4HmfdJSdsSTa9URElznu/Merw5lwnWEvebY0gI\nIIAAAggggAACfhdgkga/E3OBQAhEmF4eHf5Wql1ZKfCxGeOW+pOuw+VcyexL0B4a83VBpOkZ\nsvcWJe+LqGC2DTFBigY/JljRlGiG0kVcY15Rya/WZtv3dpfHPxGmdynydrPJ9BJFLjbvJujR\nQClhpFk+65HVc6WwyWeCJ70HKuEfyXnPeWZhDQEEEEAAAQQQQMA/Aqn/bPTPVThrSAjY+420\nF0STGfYVzPcfJS40QckjpkNo3j6Ja3ROEkaYMuskDGbImk3rkt/1zfQsyY+mF2eiCUz+MEFP\nB7d9ZjHyDrOtrtn3YdL2iHZmecn5PHqtCNPLY4Mw98DrfBaJaGjOb3qI5J/mtcIEPi+a9+PJ\nGUxvkntKnGHWTPmipptTauCWaF7x7jlYRgABBBBAAAEEEPCXAEPs/CUboueN1mFoDkgRtU0Q\n8h8zoi2mhETtik4KNCqaba+aWMPcM6T3BdmJFLQuf5uXGfqW8Kx5N0PiIq4z79rj45Yih5nj\nmiRtiChrAhdzjjjTMWWPNf+L4s0QOx1eJ8XNywRTGaWI6mZPfxPz6D1Mn5uXKY/eYxR/q3nX\n3iKTIsyQvQQz5M72WmlPU1Xz2qV7SAgggAACCCCAAAL+FiBA8rcw5w+IgAZIUXNEYtccscFP\nwYomOjIpeqUJSszwtQgTELlS5EATkPQ1Qc8XZnu+5K0mSIkyL1eKiDHHbnatmbwmmEoYbNa1\n90kDI73nyKTIv8w5ypj3jknr+m+0mWzBlaJeSVpKfMgEQOb8CZ3NdUyPUaIJhOy170raH70w\nbTmjWrrOwjsCCCCAAAIIIICAvwQYYucvWc4bFAKJRXR8mmdyD450j508YbdZ+M0z3wXXUg2N\n0/x631GEF/+rdOrvqHEmCPrWvJYkB0epLpi6nKl2s4oAAggggAACCCDgBwEv/pTzw1U5JQJB\nJKAPeY0w9wbFdzXBiuu+oFwoX0Qjc5HLTS/StFy4GJdAAAEEEEAAAQQQ8EqAAMkrJjKFuoBO\n560PgtWZ43Sa7dxKej+TmFGAJAQQQAABBBBAAIHgECBACo52oBQBFtDhbFE/mUKY4Cj+UhMk\neTHcLmqTOeaw50tKeF8Rve9Ir6Mz3JEQQAABBBBAAAEEgkOAACk42oFSBIFAhJnHIepXE7C0\nNkHSleb1qAlgdmZcMA2qIsysdR6vdO5LyugMCU8m7Ym4N6McbEcAAQQQQAABBBDIbQECpNwW\n53pBLRBhHtIaNdbMQvejCY6WmSDJPN8o/l9mef75YsfVNBPXmUAo5VX5/D5vlhLNhBDx5gGy\niRPMtb40AZaZIY+EAAIIIIAAAgggEBwCBEjB0Q6UIsgEIq8103P/bgKlKaZgB0xAc7UJiMxk\nDvHdzboZWueRMullcuVLNFOBJ8w1x5teqXgTYCVuT+6tMj1VJAQQQAABBBBAAIHgEYgOnqJQ\nEgSCTyDSTNogOnGD6fVJnGxeU9Mvoz7Y1T4ktkDy/tPm/ZDJv8O81phlE2zps5J0SvHI8ebd\n9CBFZGE4XvJZeUMAAQQQQAABBBDwswABkp+BOX1oCERUMAGN9h6Zlw6tS50SzPOMdIIH0cBI\nkwZKZsIGva8pwvQ+RTxtXleZl7lniYQAAggggAACCCAQvAIESMHbNpQsWAXWmoLVcyucGYYX\nfavbOosIIIAAAggggAACjhUgQHJs01HwQAlE1zVXTgzU1bkuAggggAACCCCAgD8FmKTBn7qc\nGwEEEEAAAQQQQAABBBwlQIDkqOaisAgggAACCCCAAAIIIOBPAQIkf+pybgQQQAABBBBAAAEE\nEHCUAAGSo5qLwiKAAAIIIIAAAggggIA/BZikwZ+6nBsBBBBAIKgFlixZIr/++qs0adJEWrdu\nLZGRmX9vuGzZMtmzZ0+aOnXo0EGioqLSbGcDAggggIDzBAiQnNdmlBgBBBBAIIcCp0+flssv\nv1wOHDggDRs2lD59+siNN94on376qeTJkyfDs3fp0kWWLl2aZn9sbKzExMSk2c4GBBBAAAHn\nCWT+VZnz6kOJEUAAAQQQuKDASy+9JBokrV27Vn744QdZtGiRTJs2TT7++OMMj42Li5PVq1fL\n8OHD5fjx4x4vgqMM2diBAAIIOE6AAMlxTUaBEUAAAQRyIpCYmCjvvfeedO7cWYoWLWpP1aBB\nA7n55ptl1KhRGZ56/fr1cubMGbniiiukcOHCHq8MD2IHAggggIDjBBhil8Mm27Vrl+zevTvT\ns2zZskXOnTsnJ06cyDSfP3bGx8eL/jEQiGv7oz5ZPafWPdzrn5CQENbtr9/6h+vnX/+/hEL9\nT506ZdtQ7xfKLOXNm9feS5RZHt23d+9eG+g0b97cI6uuf/fddx7b3FeWL18u+fLlk5IlS9pe\npP3790v79u2lVatW7tlYRgABBBBwuAABUg4b8IEHHpC5c+de8CxVqlSRQ4cOXTCfvzIE8tr+\nqlNWzhvO9dcgmfoH7v9eVj6n/sirX844vf3Pnj0rmzdvltQBTWovnWBh3bp1UqtWrdS7PNb1\nSytNpUqV8theokQJOXnypB06l96QuRUrVtiAs1mzZnLJJZfYYXn9+/eXF154QQYMGOBxLlYQ\nQAABBJwrQICUw7abNWuW6C/vzNLYsWPl3XfflYoVK2aWzS/79u3bJ9qDUL58eb+cP9hPqt8U\naypXrlywF9Uv5dPZtvSPxrJly/rl/MF+Uu3d1RvuS5cuHexF9Uv5tIc7f/78tsfDLxfIpZMW\nKVJEdAjcvHnzMr2iziKnPTwXSgcPHrRZdJiceypUqJBd1R7H9AIk/Rl+9913296j4sWLi/Zs\n9ejRQwYOHCg33XSTnfTB/XwsI4AAAgg4U4AAKYftpr+QCxYsmOlZ9Bd2RESEREfnPrdeN1DX\nzhQlF3dS/8B89nKxiTO9FO3v/PbXn7Pe/KzN9IPgtrNMmTJ27ejRo25bRXRdA+qMvlB46qmn\nPPLrz/6ePXvKhx9+KD/99BMBkocOKwgggIBzBZikwbltR8kRQAABBLIh4OrN1ym+3ZPeU6TD\noTN6FpL2GOnsde6pevXqttcqnO9zc/dgGQEEEAgFAQKkUGhF6oAAAggg4LWABkjVqlWTGTNm\neBwzc+ZMO5TPY6Pbij4vSR8I6550UgcdZq33JZEQQAABBEJDgAApNNqRWiCAAAIIeCmgPUQ6\nXG7MmDGiQZE+D+n999+XxYsXy6BBg1LO8vzzz8vQoUNT1nVa8AULFsjbb79tJ76YPHmy9OvX\nTxo1aiR33HFHSj4WEEAAAQScLZD7N8U424vSI4AAAgiEgEC3bt3szHidOnUSnemxbt26MnLk\nSKlfv35K7SZOnCj16tWTJ554wm7r3bu3xMbGSp8+feSZZ56x90Vdf/31Mm7cOLucciALCCCA\nAAKOFiBAcnTzUXgEEEAAgewI6KQ5Q4YMkddff11c9x6lPs+2bds8NmnPk+bXqb23bt0qlSpV\nuuAkPR4nYAUBBBBAwBECBEiOaCYKiQACCCDgDwGdBl0nZshK0gfS1q5dOyuHkBcBBBBAwEEC\n3IPkoMaiqAgggAACCCCAAAIIIOBfAQIk//pydgQQQAABBBBAAAEEEHCQAAGSgxqLoiKAQPAK\nxI0QibvMvEYFbxkpGQIIIIAAAghcWIB7kC5sRA4EEEAgU4G4Tmb31OQsS02QZB6vEz0l00PY\niQACCCCAAAJBKkAPUpA2DMVCAAEHCbiCI1eRv3Et8I4AAggggAACThMgQHJai1FeBBAIPoGI\nVEVKvZ5qN6sIIIAAAgggELwCBEjB2zaUDAEEnCLQO1VBX0i1zioCCCCAAAIIOEaAAMkxTUVB\nEUAgWAWiB5qS7TCvl81rj7n/qL95JyGAAAIIIICAIwWYpMGRzUahEUAg2ASiK5kSvRRspaI8\nCCCAAAIIIJBVAXqQsipGfgQQQAABBBBAAAEEEAhZAQKkkG1aKoYAAggggAACCCCAAAJZFSBA\nyqoY+RFAAAEEEEAAAQQQQCBkBQiQQrZpqRgCCCCAAAIIIIAAAghkVYAAKati5EcAAQQQQAAB\nBBBAAIGQFSBACtmmpWIIIIAAAggggAACCCCQVQECpKyKkR8BBBBAIF2B+FUicQXNK8K8YkTi\nt6SbjY0IIIAAAggEtQABUlA3D4VDAAEEnCOQeJUp6+nk8p4QSbzSOWWnpAgggAACCLgECJBc\nErwjgAACCORMIDbV4QdSrbOKAAIIIICAAwQIkBzQSBQRAQQQcIRAjVSlbJpqnVUEEEAAAQQc\nIECA5IBGoogIIICAEwQi1plStjQvc/+RXCsSvcQJpaaMCCCAAAIIeApEe66yhgACCCCAQPYE\noqLMcQuzdyxHIYAAAgggECwC9CAFS0tQDgQQQAABBBBAAAEEEAi4AAFSwJuAAiCAAAIIIIAA\nAggggECwCBAgBUtLUA4EEEAAAQQQQAABBBAIuAABUsCbgAIggAACCCCAAAIIIIBAsAgQIAVL\nS1AOBBBAAAEEEEAAAQQQCLgAAVLAm4ACIIAAAggggAACCCCAQLAIECAFS0tQDgQQQAABBBBA\nAAEEEAi4AAFSwJuAAiCAAAIIIIAAAggggECwCBAgBUtLUA4EEEAAAQQQQAABBBAIuAABUsCb\ngAIggAACCCCAAAIIIIBAsAgQIAVLS1AOBBBAAAEEEEAAAQQQCLgAAVLAm4ACIIAAAggggAAC\nCCCAQLAIECAFS0tQDgQQQAABBBBAAAEEEAi4AAFSwJuAAiCAAAIIIIAAAggggECwCBAgBUtL\nUA4EEEAAAQQQQAABBBAIuAABUsCbgAIggAACCCCAAAIIIIBAsAhEB0tBKAcCwSSwYqtIq0FJ\nJcobJXL4g2AqHWVBAAEEEEAAAQQQ8JcAPUj+kuW8jhZwBUdaib/jRUp2c3R1KDwCCCCAAAII\nIICAlwIESF5CkS28Bc6aIImEAAIIIIAAAgggEPoCBEih38bUEAEEEEAAAQQQQAABBLwUIEDy\nEops4SWQx9x35J5Wv+i+xjICCCCAAAIIIIBAqAowSUOotiz1ypHAESZlyJEfByOAAAIIIIAA\nAk4VoAfJqS1HuRFAAAEEEEAAAQQQQMDnAgRIPiflhAgggAACCCCAAAIIIOBUAQIkp7Yc5UYA\nAQQQQAABBBBAAAGfCxAg+ZyUEyKAAAIIIIAAAggggIBTBQiQnNpylBsBBBBAAAEEEEAAAQR8\nLkCA5HNSTogAAggggAACCCCAAAJOFSBAcmrLUW4Ecklg926RvpNy6WJcBgEEEEAAAQQQCLAA\nz0EKcANweQSCWaBIF5GExKQSvjtLZGZPkVZ1grnElA0BBBBAAAEEEMiZAD1IOfPjaARCWsAV\nHLkqecM7riXeEUAAAQQQQACB0BQgQArNdqVWCORYQIfWkRBAAAEEEEAAgXATIEAKtxanvgh4\nKVChQtqMDcql3cYWBBBAAAEEEEAglAQIkEKpNakLAukIRC6NkjjzPz0uwrxapJMhk00nRoq4\nfkhcXkNk0SuZZGYXAggggAACCCAQAgJM0hACjUgVEMhQIEEkekkekeSJFmSxCZL6mG0DMzwi\nzY5YEySREEAAAQQQQACBcBFwfTkcLvWlngiElUDEvnT+i38ZVgRUFoFMBZYsWSLDhg2TefPm\nSUKC+UbBi7Rr1y4ZOXKkfPXVV3LkyBEvjiALAggggICTBNL568lJxaesCCCQmUBiuXT+4Hsw\nsyPYh0B4CJw+fVqaNGkit9xyi0ydOlU6duwo9957r5w7dy5TgD59+kitWrVk0qRJ8tRTT0mz\nZs1kw4YNmR7DTgQQQAABZwkQIDmrvSgtAlkT0PuOrvrbjKkzh5lludEsvpC1U5AbgVAUeOml\nl0SDpLVr18oPP/wgixYtkmnTpsnHH3+cYXU1z+DBg2XGjBkya9YsWb9+vVSuXFm6deuW4THs\nQAABBBBwngABkvPajBIjkCWBhAYJEm2+FI/W+5GmZelQMiMQkgKJiYny3nvvSefOnaVo0aK2\njg0aNJCbb75ZRo0alWGdhw8fbnuMrr76apsnf/780rNnT/npp59ky5YtGR7HDgQQQAABZwkQ\nIDmrvSgtAggggEAOBfbu3StnzpyR5s2be5xJ17VHKaO0efNmadHCcypIPUYDrsyOy+h8bEcA\nAQQQCE4BZrHLYbs8+OCDsmDBgkzPEhsbK/pN47Zt2zLN58+dgby2P+vl7bnDuf5643k41//s\n2bNhXX8dRub09j98+LANQGrWrJnpf/k8efLIzJkzpWrVqpnmc/X2lCpVyiNfiRIl5OTJk3L8\n+HGJiYnx2KcrW7dulbZt23psL168uF3XoIuEAAIIIBAaAgRIOWxHHaJx1VVXZXqWn3/+WebM\nmSOFChXKNJ8/dp46dcp+uxmIa/ujPlk9p9ZfU8GCBbN6aEjk1z/2IiIiqH8Yt39UVJT9gsbJ\nH+jIyEjRYOb555/PtBp58+aV8uXLZ5pHdx48eNDmKVy4sEde18/JEydOpBsgHThwIM3P8QIF\nCtj/Y3oMCQEEEEAgNAQIkHLYjq1btxZ9ZZZ0+MXChQvtL/jM8vlj3+7du+3Utam/KfXHtYLx\nnDt37rR/vIRr/bX3QP9ADtf6a4CsfzSHa/01QA6F+hcrVkxKly4tjzzyiE9+zJQpU8ae5+jR\nox7n03XthSpbtqzHdteKbj927Jhr1b7rCAH9Ga+TNZAQQAABBEJDgHuQQqMdqQUCCCCAgJcC\nFStWtDm1R8g97d+/X6pUqSLaY5VeqlChgmge9+Q6R7Vq1dw3s4wAAggg4GCB9H8LOLhCFB0B\nBBBAAIHMBDRA0oBGp+t2T3r/ks5ml1Fq1aqVfP/997bHyJVHz6H3mNaoUcO1iXcEEEAAAYcL\nECA5vAEpPgIIeC8Q/6x5LpR5UG78Ye+PIWfoCWgPkT7kdcyYMXZSBx2K+v7778vixYtl0KBB\nKRXWe56GDh2ast6jRw/Zs2eP6MNidfiiK3+/fv2kSJEiKflYQAABBBBwtgD3IDm7/Sg9Agh4\nKRCnj7uJTcqcON4ESftEokp7eTDZQk5AH+6q03Z36tRJ4uPjpW7dujJy5EipX79+Sl0nTpwo\n9erVkyeeeMJu0+F3kydPlocfftg+MFZnsLvzzjulV69eKcewgAACCCDgfAECJOe3ITVAAIEL\nCMTPNRmSgyObNVEk8TG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8o+DZBKliyZsu5aKFGihH3shWuddwQQQAAB5wjkKEDS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\n+zfffOPcr9SmTRvnfiR7kCwJAQSiC3Q48WLn5uD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nn5RRo0ZJzZo1Zfr06XL88ccXbiQ4sGXLFrnnnnvkwQcfdLoFnz9/vtP0LjifdwQQ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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 420,
              "width": 420
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotellipses(mca.object, keepvar=c(1:3), magnify = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "different-brief",
      "metadata": {},
      "source": [
        "## Référence\n",
        "\n",
        "Cours de Guillaume Desagulier intitulé linguistique outillée et traitements statistiques\n",
        "\n",
        "https://corpling.modyco.fr/workshops/M2TAL/4.multivariate.html"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "R",
      "language": "R",
      "name": "ir"
    },
    "language_info": {
      "codemirror_mode": "r",
      "file_extension": ".r",
      "mimetype": "text/x-r-source",
      "name": "R",
      "pygments_lexer": "r",
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      "displayLangs": [
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      "hotkey": "alt-t",
      "langInMainMenu": true,
      "sourceLang": "en",
      "targetLang": "fr",
      "useGoogleTranslate": true
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
      "sideBar": false,
      "skip_h1_title": true,
      "title_cell": "Table des matières",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "varInspector": {
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      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
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        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
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      "types_to_exclude": [
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        "_Feature"
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}