Performing Analysis of a Factor in R Programming - factanal() Function

Factor Analysis also known as Exploratory Factor Analysis is a statistical technique used in R programming to identify the inactive relational structure and further, narrowing down a pool of variables to few variables. The main motive to use this technique is to find out which factor is most responsible for influence in the categorization of weights.

Syntax: factanal(x, factors) Parameters: x: represents dataset factors: specifies number of factors to be fitted

Example: Let us suppose, there are number of food available in the dataset with their food texture data points such as Oil, Density, Crispy, Fracture, and Hardness. Python3

# Reading csv file of food textures
food_textures <- read.csv("https://userpage.fu-berlin.de/soga/300/30100_data_sets/food-texture.csv")

food_textures <- food_textures[, 2:6]

factor_analysis <- factanal(food_textures, factors = 2)

print(factor_analysis)

# Output to be present as PNG file 
png(file = "factorAnalysisGFG.png")

# Plot factor 1 by factor 2
load <- factor_analysis$loadings[, 1:2]

# Plot graph
plot(load, type = "n")
text(load, labels = names(food_textures), cex = .9)

# Saving the file
dev.off()

Output:

Call:
factanal(x = food_textures, factors = 2)

Uniquenesses:
     Oil  Density   Crispy Fracture Hardness 
   0.334    0.156    0.042    0.256    0.407 

Loadings:
         Factor1 Factor2
Oil      -0.816         
Density   0.919         
Crispy   -0.745   0.635 
Fracture  0.645  -0.573 
Hardness          0.764 

               Factor1 Factor2
SS loadings      2.490   1.316
Proportion Var   0.498   0.263
Cumulative Var   0.498   0.761

Test of the hypothesis that 2 factors are sufficient.
The chi-square statistic is 0.27 on 1 degree of freedom.
The p-value is 0.603