dimcontrib.RdIdentifies the categories and individuals that contribute the most to each dimension obtained by a Multiple Correspondence Analysis.
dimcontrib(resmca, dim = c(1,2), best = TRUE)Contributions are sorted and assigned a positive or negative sign according to the corresponding categories or individuals coordinates, so as to facilitate interpretation.
Contributions of individuals cannot be computed for objects created by wcMCA function.
Returns a list with the following items :
a list of categories contributions to axes
a list of individuals contributions to axes
Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).
Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).
# specific MCA on Music example data set
data(Music)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- speMCA(Music[,1:5], excl = junk)
# contributions to axes 1 and 2
dimcontrib(mca)
#> $var
#> $var$dim.1
#> ctr weight
#> Jazz.Yes -35.34 95
#> Classical.Yes -27.72 142
#> Classical.No 10.80 351
#>
#> $var$dim.2
#> ctr weight
#> Rap.Yes -40.74 77
#> Rock.Yes -30.57 135
#> Rock.No 11.19 360
#>
#>
#> $ind
#> $ind$dim.1
#> 4900 4763 986 1419 1762 2017 1624 4021
#> -1.357604 -1.357604 -1.357604 -1.357604 -1.357604 -1.357604 -1.357604 -1.357604
#> 3857 1642 4064 3208 2660 2957 3694 3413
#> -1.357604 -1.357604 -1.197912 -1.197912 -1.197912 -1.197912 -1.197912 -1.197912
#> 4491 2004 1147 1049 3574 4308 11 1982
#> -0.913815 -0.848472 -0.848472 -0.848472 -0.767956 -0.767956 -0.767956 -0.767956
#> 3775 3767 3109 643 516 621 1362 2841
#> -0.767956 -0.767956 -0.767956 -0.767956 -0.767956 -0.767956 -0.767956 -0.767956
#> 3557 1023 2106 4990 2364 1387 1291 3863
#> -0.767956 -0.767956 -0.767956 -0.723274 -0.723274 -0.721477 -0.649088 -0.649088
#> 1627 1704 23 4046 1185 3788 3340 2535
#> -0.649088 -0.649088 -0.649088 -0.649088 -0.649088 -0.649088 -0.649088 -0.649088
#> 4984 2259 3436 2675 1857 2394 4845 1481
#> -0.649088 -0.649088 -0.649088 -0.649088 -0.649088 -0.649088 -0.412279 -0.412279
#> 1924 2508 4304 4661 1212 4707 460 865
#> -0.412279 -0.412279 -0.412279 -0.412279 -0.412279 -0.412279 -0.412279 -0.412279
#> 1011 893 2828 2489 3636 2066 1089 506
#> -0.412279 -0.333642 -0.333304 -0.326520 -0.326520 -0.326520 -0.326520 -0.274455
#> 1459 3639 20 2950 3858 304 689 2441
#> -0.274455 -0.274455 -0.274455 -0.274455 -0.274455 -0.274455 -0.274455 -0.274455
#> 4755 296 3623 1899 2934 1553 297 4838
#> -0.274455 -0.205402 -0.205402 -0.205402 -0.205402 -0.205402 -0.205402 0.206194
#> 459 4485 4742 3386 4010 4187 1327 3621
#> 0.206194 0.228793 0.228793 0.283084 0.283084 0.283084 0.283084 0.283084
#> 2100 1501 2500 1436 4563 1834 4698 1328
#> 0.283084 0.283084 0.283084 0.283084 0.283084 0.283084 0.283084 0.283084
#> 2750 1238 1016 4154 3094 620 4167 1714
#> 0.283084 0.283084 0.283084 0.283084 0.283084 0.283084 0.283084 0.283084
#> 450 2218 377 3777 421 2421 2578 1202
#> 0.283084 0.312470 0.363280 0.363280 0.363280 0.363280 0.363280 0.363280
#> 4668 3726 2043 4151 2034 381 3294 4565
#> 0.363280 0.363280 0.363280 0.363280 0.363280 0.363280 0.363280 0.363280
#> 1040 1441 2285 963 1313 4361
#> 0.363280 0.363280 0.363280 0.363280 0.363280 0.363280
#>
#> $ind$dim.2
#> 4757 4564 740 1848 4705 3550 3478 3760
#> -1.930377 -1.930377 -1.850166 -1.850166 -1.850166 -1.455065 -1.455065 -1.385538
#> 2961 2419 4312 223 4076 1882 1986 420
#> -1.385538 -1.385538 -1.385538 -1.385538 -1.385538 -1.385538 -1.385538 -1.385538
#> 1094 4990 2364 2004 1147 1049 1489 1665
#> -1.385538 -1.291869 -1.291869 -1.226406 -1.226406 -1.226406 -0.909133 -0.854354
#> 1972 1250 3452 2828 4485 4742 377 3777
#> -0.854354 -0.541072 -0.541072 -0.458115 -0.380185 -0.380185 -0.338292 -0.338292
#> 421 2421 2578 1202 4668 3726 2043 4151
#> -0.338292 -0.338292 -0.338292 -0.338292 -0.338292 -0.338292 -0.338292 -0.338292
#> 2034 381 3294 4565 1040 1441 2285 963
#> -0.338292 -0.338292 -0.338292 -0.338292 -0.338292 -0.338292 -0.338292 -0.338292
#> 1313 4361 2218 2489 3636 2066 1089 3386
#> -0.338292 -0.338292 -0.320103 -0.317821 -0.317821 -0.317821 -0.317821 -0.305208
#> 4010 4187 1327 3621 2100 1501 2500 1436
#> -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.305208
#> 4563 1834 4698 1328 2750 1238 1016 4154
#> -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.305208
#> 3094 620 4167 1714 450 4845 1481 1924
#> -0.305208 -0.305208 -0.305208 -0.305208 -0.305208 -0.285781 -0.285781 -0.285781
#> 2508 4304 4661 1212 4707 460 865 1011
#> -0.285781 -0.285781 -0.285781 -0.285781 -0.285781 -0.285781 -0.285781 -0.285781
#> 371 4838 459 4246 4618 641 3508 1546
#> -0.259666 -0.258787 -0.258787 0.213267 0.213267 0.241062 0.246782 0.246782
#> 2846 151 3126 1945 1093 1176 1075 4043
#> 0.246782 0.246782 0.246782 0.246782 0.246782 0.246782 0.246782 0.246782
#> 4107 949 2026 3942 1820 3986 1932 1670
#> 0.246782 0.246782 0.246782 0.246782 0.246782 0.276616 0.276616 0.276616
#> 2404 31 4274 280 3793 701 1959 3769
#> 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616
#> 4814 2114 984 3286 58 3702 4000 2827
#> 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616
#> 161 1111 4367 696 88 4100 2312 2610
#> 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616
#> 337 1059 1506 3377 1433 89 2906 4930
#> 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616
#> 422 106 180 3657 927 4458 4251 1877
#> 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616 0.276616
#> 3063
#> 0.276616
#>
#>