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.
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
- Checking if the Object is a Factor in R Programming - is.factor() Function
- Convert a Vector into Factor in R Programming - as.factor() Function
- Convert an Unordered Factor to an Ordered Factor in R Programming - as.ordered() Function
- Check if a Factor is an Ordered Factor in R Programming - is.ordered() Function
- Convert Factor to Numeric and Numeric to Factor in R Programming
- Get or Set Levels of a Factor in R Programming - levels() Function
- Applying User-defined Functions on Factor Levels of Dataset in R Programming - by() Function
- Replace values of a Factor in R Programming - recode_factor() Function
- Removing Levels from a Factor in R Programming - droplevels() Function
- Get the Number of Levels of a Factor in R Programming - nlevels() Function
- Performing different Operations on Two Arrays in R Programming - outer() Function
- Performing Operations on Multiple Lists simultaneously in R Programming - mapply() Function
- The Factor Issue in a DataFrame in R Programming
- Perform Probability Density Analysis on t-Distribution in R Programming - dt() Function
- Perform the Probability Cumulative Density Analysis on t-Distribution in R Programming - pt() Function
- Perform the Inverse Probability Cumulative Density Analysis on t-Distribution in R Programming - qt() Function
- Perform Linear Regression Analysis in R Programming - lm() Function
- Performing Binomial Test in R programming - binom.test() Method
- Performing F-Test in R programming - var.test() Method
- Performing Logarithmic Computations in R Programming - log(), log10(), log1p(), and log2() Functions
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