get_mfa.RdExtract all the results (coordinates, squared cosine and contributions)
for the active individuals/quantitative variables/qualitative variable categories/groups/partial axes from Multiple Factor Analysis (MFA) outputs.
get_mfa(): Extract the results for variables and individuals
get_mfa_ind(): Extract the results for individuals only
get_mfa_var(): Extract the results for variables (quantitatives, qualitatives and groups)
get_mfa_partial_axes(): Extract the results for partial axes only
get_mfa(res.mfa, element = c("ind", "quanti.var", "quali.var", "group", "partial.axes")) get_mfa_ind(res.mfa) get_mfa_var(res.mfa, element = c("quanti.var", "quali.var", "group")) get_mfa_partial_axes(res.mfa)
| res.mfa | an object of class MFA [FactoMineR]. |
|---|---|
| element | the element to subset from the output. Possible values are "ind", "quanti.var", "quali.var", "group" or "partial.axes". |
a list of matrices containing the results for the active individuals/quantitative variable categories/qualitative variable categories/groups/partial axes including :
coordinates for the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
cos2 for the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
contributions of the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
inertia of the individuals/quantitative variable categories/qualitative variable categories/groups/partial axes
# Multiple Factor Analysis # ++++++++++++++++++++++++ # Install and load FactoMineR to compute MFA # install.packages("FactoMineR") library("FactoMineR") data(poison) res.mfa <- MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"), name.group=c("desc","desc2","symptom","eat"), num.group.sup=1:2, graph = FALSE) # Extract the results for qualitative variable categories var <- get_mfa_var(res.mfa, "quali.var") print(var)#> Multiple Factor Analysis results for qualitative variable categories #> =================================================== #> Name Description #> 1 "$coord" "Coordinates" #> 2 "$cos2" "Cos2, quality of representation" #> 3 "$contrib" "Contributions"#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Nausea_n 0.2995559 -0.008263233 -0.15087999 -0.05120534 0.02684045 #> Nausea_y -1.0734086 0.029609918 0.54065330 0.18348582 -0.09617827 #> Vomit_n 0.4923055 -0.335833158 0.06315216 0.20249017 0.03344304 #> Vomit_y -0.7384582 0.503749737 -0.09472824 -0.30373526 -0.05016455 #> Abdo_n 1.4594717 -0.253368918 -0.02659215 -0.10539596 -0.03153966 #> Abdo_y -0.7100132 0.123260555 0.01293672 0.05127371 0.01534362#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Nausea_n 0.5675315 0.0004318514 0.143978489 0.016583068 0.0045563154 #> Nausea_y 0.5675315 0.0004318514 0.143978489 0.016583068 0.0045563154 #> Vomit_n 0.5335539 0.2482881886 0.008779814 0.090264467 0.0024621829 #> Vomit_y 0.5335539 0.2482881886 0.008779814 0.090264467 0.0024621829 #> Abdo_n 0.9163124 0.0276158818 0.000304200 0.004778594 0.0004279236 #> Abdo_y 0.9163124 0.0276158818 0.000304200 0.004778594 0.0004279236#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Nausea_n 1.041575 0.002357361 1.112155571 0.1542638 0.05473887 #> Nausea_y 3.732310 0.008447211 3.985224130 0.5527787 0.19614761 #> Vomit_n 2.158985 2.988262782 0.149528552 1.8513432 0.06521886 #> Vomit_y 3.238478 4.482394173 0.224292828 2.7770149 0.09782829 #> Abdo_n 10.349759 0.927762633 0.014461496 0.2735807 0.03163984 #> Abdo_y 5.035018 0.451343984 0.007035322 0.1330933 0.01539236#> Multiple Factor Analysis results for individuals #> =================================================== #> Name Description #> 1 "$coord" "Coordinates" #> 2 "$cos2" "Cos2, quality of representation" #> 3 "$contrib" "Contributions" #> 4 "$coord.partiel" "Partial coordinates" #> 5 "$within.inertia" "Within inertia" #> 6 "$within.partial.inertia" "Within partial inertia"#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> 1 -0.8980293 -0.19968268 0.10527090 -0.06375957 -0.2468778 #> 2 1.6550439 -0.41095346 -0.16606815 -0.49335618 1.4414215 #> 3 -0.8673037 0.09906989 -0.27126101 -0.42418672 -0.2250902 #> 4 1.7839172 -0.56856945 -0.04179541 -0.09829156 -0.6203236 #> 5 -0.8673037 0.09906989 -0.27126101 -0.42418672 -0.2250902 #> 6 -1.1229099 -1.07440938 -4.26919514 3.89647532 0.9498171#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> 1 0.35650962 0.017626699 0.0048989935 0.001797136 0.02694352 #> 2 0.39631064 0.024434418 0.0039901519 0.035215824 0.30060672 #> 3 0.50651695 0.006608988 0.0495480243 0.121161803 0.03411653 #> 4 0.60082570 0.061033205 0.0003298044 0.001824029 0.07265003 #> 5 0.50651695 0.006608988 0.0495480243 0.121161803 0.03411653 #> 6 0.03403476 0.031158209 0.4919544784 0.409804540 0.02435078#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> 1 1.0075082 0.08591005 0.028403241 0.01143425 0.1948150 #> 2 3.4220518 0.36387188 0.070684466 0.68460183 6.6411041 #> 3 0.9397448 0.02114689 0.188593192 0.50609384 0.1619465 #> 4 3.9757303 0.69651464 0.004477221 0.02717374 1.2299713 #> 5 0.9397448 0.02114689 0.188593192 0.50609384 0.1619465 #> 6 1.5752786 2.48715469 46.713592970 42.70323449 2.8836218# You can also use the function get_mfa() get_mfa(res.mfa, "ind") # Results for individuals#> Multiple Factor Analysis results for individuals #> =================================================== #> Name Description #> 1 "$coord" "Coordinates" #> 2 "$cos2" "Cos2, quality of representation" #> 3 "$contrib" "Contributions" #> 4 "$coord.partiel" "Partial coordinates" #> 5 "$within.inertia" "Within inertia" #> 6 "$within.partial.inertia" "Within partial inertia"get_mfa(res.mfa, "quali.var") # Results for qualitative variable categories#> Multiple Factor Analysis results for qualitative variable categories #> =================================================== #> Name Description #> 1 "$coord" "Coordinates" #> 2 "$cos2" "Cos2, quality of representation" #> 3 "$contrib" "Contributions"