R/get_hmfa.R
get_hmfa.RdExtract all the results (coordinates, squared cosine and
contributions) for the active individuals/quantitative variables/qualitative
variable categories/groups/partial axes from Hierarchical Multiple Factor
Analysis (HMFA) outputs.
get_hmfa(): Extract the results for variables and individuals
get_hmfa_ind(): Extract the results for individuals only
get_mfa_var(): Extract the results for variables (quantitatives, qualitatives and groups)
get_hmfa_partial(): Extract the results for partial.node.
a list of matrices containing the results for the active individuals, variables, groups and partial nodes, including :
coordinates
cos2
contributions
# Multiple Factor Analysis
# ++++++++++++++++++++++++
# Install and load FactoMineR to compute MFA
# install.packages("FactoMineR")
library("FactoMineR")
data(wine)
hierar <- list(c(2,5,3,10,9,2), c(4,2))
res.hmfa <- HMFA(wine, H = hierar, type=c("n",rep("s",5)), graph = FALSE)
# Extract the results for qualitative variable categories
var <- get_hmfa_var(res.hmfa, "quali.var")
print(var)
#> Hierarchical Multiple Factor Analysis results for qualitative variable categories
#> ===================================================
#> Name Description
#> 1 "$coord" "Coordinates"
#> 2 "$cos2" "Cos2, quality of representation"
#> 3 "$contrib" "Contributions"
head(var$coord) # coordinates of qualitative variables
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> Saumur 0.28342472 0.4390421 -0.09638752 -0.36769255 -0.14533044
#> Bourgueuil -0.07481551 -0.6213326 -0.41687271 0.64796797 -0.14011318
#> Chinon -0.66719470 -0.2753669 0.89037475 0.03920255 0.60982847
#> Reference 1.23034230 -0.2804136 0.07449961 0.35097213 0.03705687
#> Env1 -0.60476095 -0.5210792 -0.56556282 -0.22598702 0.16935600
#> Env2 -0.61315234 0.1048772 0.84674562 -0.29209644 -0.32484663
head(var$cos2) # cos2 of qualitative variables
#> NULL
head(var$contrib) # contributions of qualitative variables
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> Saumur 0.28718092 3.25760489 0.6860754 15.41542454 5.7667557
#> Bourgueuil 0.01091496 3.55871242 6.9999633 26.11268188 2.9237144
#> Chinon 0.57869886 0.46599028 21.2884105 0.06372088 36.9233500
#> Reference 3.44379596 0.84564799 0.2608216 8.93792011 0.2385949
#> Env1 0.83205681 2.92010718 15.0313270 3.70559696 4.9833906
#> Env2 0.61093393 0.08449392 24.0665310 4.42196882 13.0964154
# Extract the results for individuals
ind <- get_hmfa_ind(res.hmfa)
print(ind)
#> Hierarchical Multiple Factor Analysis results for individuals
#> ===================================================
#> Name Description
#> 1 "$coord" "Coordinates"
#> 2 "$cos2" "Cos2, quality of representation"
#> 3 "$contrib" "Contributions"
head(ind$coord) # coordinates of individuals
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> 2EL 0.1155896 -0.3839978 -0.6324715 -0.8971297 0.196409100
#> 1CHA -1.0733840 -0.8748015 -0.7149103 -1.0613744 0.119528809
#> 1FON -0.5149328 -0.8435529 -0.8008935 0.4309004 -0.158675600
#> 1VAU -3.3120825 0.1086326 1.1289693 0.1909031 0.426946486
#> 1DAM 1.8159304 0.2803452 0.1624881 0.1965103 0.006922444
#> 2BOU 0.9007130 -0.3683414 -0.2969980 0.7341058 -0.144255380
head(ind$cos2) # cos2 of individuals
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> 2EL 0.006483776 0.0715562736 0.194120803 0.390571644 1.872033e-02
#> 1CHA 0.266929879 0.1772988917 0.118410472 0.260990168 3.310031e-03
#> 1FON 0.102635900 0.2754373265 0.248283393 0.071870722 9.745821e-03
#> 1VAU 0.827795354 0.0008905154 0.096180048 0.002750087 1.375522e-02
#> 1DAM 0.724996881 0.0172792123 0.005804713 0.008490015 1.053553e-05
#> 2BOU 0.315268924 0.0527241292 0.034277982 0.209423600 8.086718e-03
head(ind$contrib) # contributions of individuals
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
#> 2EL 0.03439077 0.82521019 4.6796252 11.6995051 0.867753728
#> 1CHA 2.96561244 4.28278389 5.9790526 16.3754861 0.321379713
#> 1FON 0.68250419 3.98227962 7.5037568 2.6990497 0.566361307
#> 1VAU 28.23621201 0.06604316 14.9105443 0.5297644 4.100342754
#> 1DAM 8.48794523 0.43983856 0.3088675 0.5613423 0.001077934
#> 2BOU 2.08822376 0.75929094 1.0318966 7.8338335 0.468098570
# You can also use the function get_hmfa()
get_hmfa(res.hmfa, "ind") # Results for individuals
#> Hierarchical Multiple Factor Analysis results for individuals
#> ===================================================
#> Name Description
#> 1 "$coord" "Coordinates"
#> 2 "$cos2" "Cos2, quality of representation"
#> 3 "$contrib" "Contributions"
get_hmfa(res.hmfa, "quali.var") # Results for qualitative variable categories
#> Hierarchical Multiple Factor Analysis results for qualitative variable categories
#> ===================================================
#> Name Description
#> 1 "$coord" "Coordinates"
#> 2 "$cos2" "Cos2, quality of representation"
#> 3 "$contrib" "Contributions"