interpret.md
In ggfacto: Graphs for Correspondence Analysis

the MCA interpretation table is stable

Code
  print(mca_interpret(fx_mca(), axes = 1:2), n = Inf, width = Inf)
Output
  | Axe     |eigenvalue  |% variance  |cumul.  | |Benzecri's modified rate  |cumul. mod.  |
  |:--------|-----------:|-----------:|-------:|-|-------------------------:|------------:|
  |         | *Variance* |            |        | | *Benzecri*               |             |
  |         |   *<var>*  |  *<col%>*  |        | |                *<col%>*  |             |
  | Axe 1   |     0.234  |     23.4%  | 23.4%  | |                   82.6%  |      82.6%  |
  | Axe 2   |     0.196  |     19.6%  | 43.0%  | |                   15.4%  |      98.0%  |
  | Axe 3   |     0.177  |     17.7%  | 60.7%  | |                    2.0%  |       100%  |
  | Axe 4   |     0.146  |     14.6%  | 75.4%  | |                          |             |
  | Axe 5   |     0.131  |     13.1%  | 88.4%  | |                          |             |
  | Axe 6   |     0.116  |     11.6%  |  100%  | |                          |             |
  |**Total**|   **1.000**|    **100%**|        | |                  **100%**|             |

  # A tabxplor tab: 8 x 7
    Axe                                 Question       contrib Positive_levels
                                                        <col%>                
  1 Axe 1: 23.4% of variance (mod. 83%) dinner           31.3% "dinner"       
  2 Axe 1: 23.4% of variance (mod. 83%) tea.time         25.7% "Not.tea time" 
  3 Axe 1: 23.4% of variance (mod. 83%) lunch            22.2% ""             
  4 Axe 1: 23.4% of variance (mod. 83%) Above mean ctr   73.8% ""             
  5 Axe 2: 19.6% of variance (mod. 15%) always           32.2% "always"       
  6 Axe 2: 19.6% of variance (mod. 15%) evening          31.6% "evening"      
  7 Axe 2: 19.6% of variance (mod. 15%) breakfast        27.7% "Not.breakfast"
  8 Axe 2: 19.6% of variance (mod. 15%) Above mean ctr   91.5% ""             
      `  ` Negative_levels  `   `
    <col%> <chr>           <col%>
  1  29.2% ""                    
  2  14.5% "tea time"       11.2%
  3        "lunch"          19.0%
  4  43.6% ""               30.2%
  5  21.1% "Not.always"     11.0%
  6  20.8% "Not.evening"    10.9%
  7  13.3% "breakfast"      14.4%
  8  55.2% ""               36.3%
  # contribution to the variance of the axis (vs the mean contribution): ×10 ×5 ×2 ×1 ×1 ×2 ×5 ×10
  # contrib: the whole question's contribution to the axis

Code
  print(mca_interpret(fx_mca(), axes = 1:2, complete = TRUE), n = Inf, width = Inf)
Output
  | Axe     |eigenvalue  |% variance  |cumul.  | |Benzecri's modified rate  |cumul. mod.  |
  |:--------|-----------:|-----------:|-------:|-|-------------------------:|------------:|
  |         | *Variance* |            |        | | *Benzecri*               |             |
  |         |   *<var>*  |  *<col%>*  |        | |                *<col%>*  |             |
  | Axe 1   |     0.234  |     23.4%  | 23.4%  | |                   82.6%  |      82.6%  |
  | Axe 2   |     0.196  |     19.6%  | 43.0%  | |                   15.4%  |      98.0%  |
  | Axe 3   |     0.177  |     17.7%  | 60.7%  | |                    2.0%  |       100%  |
  | Axe 4   |     0.146  |     14.6%  | 75.4%  | |                          |             |
  | Axe 5   |     0.131  |     13.1%  | 88.4%  | |                          |             |
  | Axe 6   |     0.116  |     11.6%  |  100%  | |                          |             |
  |**Total**|   **1.000**|    **100%**|        | |                  **100%**|             |

  # A tabxplor tab: 8 x 12
    Axe                                 Question       contrib Positive_levels
                                                        <col%>                
  1 Axe 1: 23.4% of variance (mod. 83%) dinner           31.3% "dinner"       
  2 Axe 1: 23.4% of variance (mod. 83%) tea.time         25.7% "Not.tea time" 
  3 Axe 1: 23.4% of variance (mod. 83%) lunch            22.2% ""             
  4 Axe 1: 23.4% of variance (mod. 83%) Above mean ctr   73.8% ""             
  5 Axe 2: 19.6% of variance (mod. 15%) always           32.2% "always"       
  6 Axe 2: 19.6% of variance (mod. 15%) evening          31.6% "evening"      
  7 Axe 2: 19.6% of variance (mod. 15%) breakfast        27.7% "Not.breakfast"
  8 Axe 2: 19.6% of variance (mod. 15%) Above mean ctr   91.5% ""             
       ctr  coord   cos2 Negative_levels `ctr ` `coord ` `cos2 ` spread
    <col%> <mean> <row%> <chr>           <col%>   <mean>  <row%> <col%>
  1  29.2%   2.42    44% ""                                            
  2  14.5%   0.68    36% "tea time"       11.2%    -0.53     36%   100%
  3                      "lunch"          19.0%    -1.35     31%       
  4  43.6%               ""               30.2%                   55.3%
  5  21.1%   0.85    38% "Not.always"     11.0%    -0.44     38%   100%
  6  20.8%   0.84    37% "Not.evening"    10.9%    -0.44     37%   100%
  7  13.3%   0.55    33% "breakfast"      14.4%    -0.59     33%   100%
  8  55.2%               ""               36.3%                   88.6%
  # contribution to the variance of the axis (vs the mean contribution): ×10 ×5 ×2 ×1 ×1 ×2 ×5 ×10
  # contrib: the whole question's contribution to the axis
  # coord: coordinate on the axis
  # cos2: quality of representation
  # spread: share of the group's contribution the gap between its two sides accounts for

the CA interpretation table is stable

Code
  print(ca_interpret(fx_ca(), complete = TRUE), n = Inf, width = Inf)
Output
  | Axe     |eigenvalue  |% variance  |cumul.  |
  |:--------|-----------:|-----------:|-------:|
  |         | *Variance* |            |        |
  |         |   *<var>*  |  *<col%>*  |        |
  | Axe 1   |     0.041  |     88.8%  | 88.8%  |
  | Axe 2   |     0.005  |     11.2%  |  100%  |
  |**Total**|   **0.046**|    **100%**|        |

  # A tabxplor tab: 9 x 11
    Axe                      Variable                  Positive_levels    ctr
                                                                       <col%>
  1 Axe 1: 88.8% of variance "Rows"                    "Black"          72.6%
  2 Axe 1: 88.8% of variance "Rows: above mean ctr"    ""               72.6%
  3 Axe 1: 88.8% of variance "Columns"                 "Never married"  54.2%
  4 Axe 1: 88.8% of variance "Columns: above mean ctr" ""               54.2%
  5 Axe 2: 11.2% of variance "Rows"                    ""                    
  6 Axe 2: 11.2% of variance "Rows: above mean ctr"    ""                    
  7 Axe 2: 11.2% of variance "Columns"                 "Widowed"        46.2%
  8 Axe 2: 11.2% of variance ""                        "Divorced"       26.0%
  9 Axe 2: 11.2% of variance "Columns: above mean ctr" ""               72.3%
     coord   cos2 Negative_levels `ctr ` `coord ` `cos2 ` spread
    <mean> <row%> <chr>           <col%>   <mean>  <row%> <col%>
  1   0.45    98% ""                                            
  2               ""                                            
  3   0.30   100% "Married"        30.5%    -0.16     92%  84.7%
  4               ""               30.5%                   84.7%
  5               "Other"          84.5%    -0.22     63%       
  6               ""               84.5%                        
  7   0.17    84% "Married"        21.1%    -0.05      8%  86.2%
  8   0.09    86% ""                                            
  9               ""               21.1%                   86.2%
  # contribution to the variance of the axis (vs the mean contribution): ×10 ×5 ×2 ×1 ×1 ×2 ×5 ×10
  # coord: coordinate on the axis
  # cos2: quality of representation
  # spread: share of the group's contribution the gap between its two sides accounts for

the PCA interpretation table is stable

Code
  print(pca_interpret(fx_pca(), axes = 1:2), n = Inf, width = Inf)
Output
  |Axe|eigenvalue|% variance|cumul.|
  |:-|-:|-:|-:|
  ||*Variance*|||
  ||*<var>*|*<col%>*||
  |Axe 1|5.086|72.7%|72.7%|
  |Axe 2|1.157|16.5%|89.2%|
  |Axe 3|0.345|4.9%|94.1%|
  |Axe 4|0.158|2.3%|96.4%|
  |Axe 5|0.129|1.8%|98.2%|
  |... of 7|...|...|...|
  |Total|7.000|100%||

  # A tabxplor tab: 8 x 10
    variable mean_Variables sd_Variables `sd/mean_Variables` `coord_Axe 1`
                     <mean>         <sd>                <cv>        <mean>
  1 mpg               20.09         5.93                 30%         -0.93
  2 cyl                6.19         1.76                 28%          0.96
  3 disp             230.72       121.99                 53%          0.95
  4 hp               146.69        67.48                 46%          0.87
  5 drat               3.60         0.53                 15%         -0.75
  6 weight             3.22         0.96                 30%          0.88
  7 qsec              17.85         1.76                 10%         -0.54
  8 Total                                                                 
    `contrib_Axe 1` `cos2_Axe 1` `coord_Axe 2` `contrib_Axe 2` `cos2_Axe 2`
             <col%>       <row%>        <mean>          <col%>       <row%>
  1             17%          87%         -0.09              1%           1%
  2             18%          92%         -0.08              1%           1%
  3             18%          91%          0.09              1%           1%
  4             15%          76%         -0.36             11%          13%
  5             11%          56%         -0.48             20%          23%
  6             15%          78%          0.35             10%          12%
  7              6%          29%          0.81             56%          65%
  8            100%                                       100%             
  # coordinate on the axis (Total): -0.8 -0.4 -0.2 -0.1 +0.1 +0.2 +0.4 +0.8
  # contrib: its contribution to the variance of the axis; an axis sums to 100 %
  # cos2: quality of representation
  # sd/mean: coefficient of variation - the standard deviation as a percentage of the mean, comparable between variables measured in different units