Extract all the results (coordinates, squared cosine and contributions) for the active individuals and variables from Factor Analysis of Mixed Date (FAMD) outputs.

  • get_famd(): Extract the results for variables and individuals

  • get_famd_ind(): Extract the results for individuals only

  • get_famd_var(): Extract the results for quantitative and qualitative variables only

get_famd(res.famd, element = c("ind", "var", "quanti.var", "quali.var"))

get_famd_ind(res.famd)

get_famd_var(res.famd, element = c("var", "quanti.var", "quali.var"))

Arguments

res.famd

an object of class FAMD [FactoMineR].

element

the element to subset from the output. Possible values are "ind", "quanti.var" or "quali.var".

Value

a list of matrices containing the results for the active individuals and variables, including :

coord

coordinates of indiiduals/variables.

cos2

cos2 values representing the quality of representation on the factor map.

contrib

contributions of individuals / variables to the principal components.

Examples

# Compute FAMD library("FactoMineR") data(wine) res.famd <- FAMD(wine[,c(1,2, 16, 22, 29, 28, 30,31)], graph = FALSE) # Extract the results for qualitative variable categories quali.var <- get_famd_var(res.famd, "quali.var") print(quali.var)
#> FAMD results for qualitative variable categories #> =================================================== #> Name Description #> 1 "$coord" "Coordinates" #> 2 "$cos2" "Cos2, quality of representation" #> 3 "$contrib" "Contributions"
head(quali.var$coord) # coordinates of qualitative variables
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Saumur 0.08238236 0.2041021 0.9985884 -0.3996689 -0.1453137 #> Bourgueuil 0.72578122 -1.0944657 -0.8193199 1.0187071 -0.4092349 #> Chinon -1.31522332 1.0804177 -1.5171382 -0.4289712 1.0134650 #> Reference 2.06918009 0.6112128 -0.2525731 0.6853384 0.0157536 #> Env1 -0.30736790 -1.6929988 -0.1158917 -0.3395732 0.3152922 #> Env2 -1.40724432 1.2078471 -0.5667802 -1.0387469 -0.6525117
# Extract the results for quantitative variables quanti.var <- get_famd_var(res.famd, "quanti.var") print(quanti.var)
#> FAMD results for quantitative variables #> =================================================== #> Name Description #> 1 "$coord" "Coordinates" #> 2 "$cos2" "Cos2, quality of representation" #> 3 "$contrib" "Contributions"
head(quanti.var$coord) # coordinates
#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Plante -0.8569808 0.2460731 0.32542595 -0.06333481 0.03215674 #> Acidity -0.4162617 0.7007982 -0.35551938 0.33967158 -0.06772218 #> Harmony 0.9457255 0.1537145 0.20031093 -0.06044678 0.09307236 #> Intensity 0.8361705 0.3669322 0.25569950 0.15236464 0.08045522 #> Overall.quality 0.9547617 0.0724343 -0.09662648 -0.07379211 -0.02821680 #> Typical 0.8836634 0.1646035 -0.03936739 -0.28887130 0.02431654
# Extract the results for individuals ind <- get_famd_ind(res.famd) print(ind)
#> FAMD 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 -0.1213241 -1.5797151 0.7615777 -1.1978981 0.28209940 #> 1CHA -0.6536760 -1.6846472 0.7643619 -0.9923990 0.24345633 #> 1FON 0.8701622 -2.2457285 -0.7788402 0.3339757 -0.16854021 #> 1VAU -5.6883455 2.1640314 -2.2984115 0.2968312 -0.13215744 #> 1DAM 2.4441041 1.2242463 0.2751576 0.4694187 -0.06885226 #> 2BOU 2.2703576 -0.0768261 -0.8093529 1.4650543 -0.49582803