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.
# 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