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

Last Updated : 01 Jun, 2020

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

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

Factor1 Factor2