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)object of class MCA, speMCA, or csMCA
numerical vector of the dimensions to describe (default is c(1,2))
logical. If FALSE, displays all the categories. If TRUE (default), displays only categories and individuals with contributions higher than average
Contributions are sorted and assigned a positive or negative sign according to the corresponding categories or individuals coordinates, so as to facilitate interpretation.
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
#>
#>