Skip to content
Related Articles
Get the best out of our app
GeeksforGeeks App
Open App

Related Articles

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

Improve Article
Save Article
Like Article
Improve Article
Save Article
Like Article

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)

x: represents dataset
factors: specifies number of factors to be fitted

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.

# Reading csv file of food textures
food_textures <- food_textures[, 2:6]
factor_analysis <- factanal(food_textures, factors = 2)
# 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


factanal(x = food_textures, factors = 2)

     Oil  Density   Crispy Fracture Hardness 
   0.334    0.156    0.042    0.256    0.407 

         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

My Personal Notes arrow_drop_up
Last Updated : 01 Jun, 2020
Like Article
Save Article
Similar Reads