supvars.RdFrom MCA results, computes statistics (weights, coordinates, squared cosines, contributions, test-values, variances) for categorical supplementary variables.
supvars(resmca, vars)
varsups(resmca, vars)object of class MCA, speMCA, csMCA, stMCA or multiMCA
A data frame of categorical supplementary variables. All these variables should be factors.
Returns a list with the following items :
numeric vector of categories weights
data frame of categories coordinates
data frame of categories squared cosines
a list of data frames of categories within variances, variance between and within categories and variable square correlation ratio (eta2)
data frame of categories typicality test statistics
data frame of categories p-values from typicality test statistics
data frame of categories correlation coefficients
varsups is softly deprecated. Please use supvars instead.
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 of 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)
# computes statistics for Gender and Age supplementary variables
supvars(mca, Music[, c("Gender","Age")])
#> $weight
#> Gender.Men Gender.Women Age.15-24 Age.25-49 Age.50+
#> 253 247 78 204 218
#>
#> $coord
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> Gender.Men 0.002461 -0.095251 -0.044400 -0.011305 0.014944
#> Gender.Women -0.002521 0.097565 0.045478 0.011579 -0.015307
#> Age.15-24 0.445924 -0.781930 -0.313437 -0.002163 0.105164
#> Age.25-49 -0.050511 -0.236099 0.105504 0.102956 0.003924
#> Age.50+ -0.112283 0.500710 0.013419 -0.095570 -0.041299
#>
#> $cos2
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> Gender.Men 0.000006 0.009293 0.002019 0.000131 0.000229
#> Gender.Women 0.000006 0.009293 0.002019 0.000131 0.000229
#> Age.15-24 0.036754 0.113010 0.018159 0.000001 0.002044
#> Age.25-49 0.001758 0.038417 0.007671 0.007305 0.000011
#> Age.50+ 0.009746 0.193811 0.000139 0.007061 0.001319
#>
#> $var
#> $var$Gender
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> Men 0.290292 0.244867 0.207113 0.178409 0.129570
#> Women 0.266459 0.186373 0.198275 0.164282 0.128262
#> within 0.278519 0.215971 0.202747 0.171430 0.128924
#> between 0.000002 0.002026 0.000410 0.000022 0.000029
#> total 0.278521 0.217997 0.203157 0.171452 0.128953
#> eta2 0.000006 0.009293 0.002019 0.000131 0.000229
#>
#> $var$Age
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> 15-24 0.224468 0.162922 0.216555 0.333632 0.080558
#> 25-49 0.299116 0.241927 0.199184 0.195215 0.123800
#> 50+ 0.254595 0.101594 0.192788 0.087921 0.150360
#> within 0.268060 0.168417 0.199105 0.170028 0.128634
#> between 0.010461 0.049580 0.004052 0.001424 0.000319
#> total 0.278521 0.217997 0.203157 0.171452 0.128953
#> eta2 0.037558 0.227433 0.019946 0.008308 0.002475
#>
#>
#> $typic
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> Gender.Men 0.055648 -2.153442 -1.003786 -0.255574 0.337862
#> Gender.Women -0.055648 2.153442 1.003786 0.255574 -0.337862
#> Age.15-24 4.282548 -7.509471 -3.010171 -0.020778 1.009972
#> Age.25-49 -0.936718 -4.378377 1.956531 1.909278 0.072764
#> Age.50+ -2.205306 9.834220 0.263555 -1.877042 -0.811141
#>
#> $pval
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> Gender.Men 0.955622 0.031284 0.315482 0.798279 0.735467
#> Gender.Women 0.955622 0.031284 0.315482 0.798279 0.735467
#> Age.15-24 0.000018 0.000000 0.002611 0.983423 0.312509
#> Age.25-49 0.348903 0.000012 0.050403 0.056226 0.941994
#> Age.50+ 0.027433 0.000000 0.792123 0.060512 0.417285
#>
#> $cor
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> Gender.Men 0.002 -0.096 -0.045 -0.011 0.015
#> Gender.Women -0.002 0.096 0.045 0.011 -0.015
#> Age.15-24 0.192 -0.336 -0.135 -0.001 0.045
#> Age.25-49 -0.042 -0.196 0.088 0.085 0.003
#> Age.50+ -0.099 0.440 0.012 -0.084 -0.036
#>