Skip to content

R Frequently Asked Questions

Statistical Computing and Graphics in R

Menu
  • Learn R
    • R Basics
      • R FAQS about Package
      • R GUI
      • Using R packages
      • Missing Values
    • R Graphics
    • Data Structure
      • Data Frame
      • Matrices
      • List
    • R Programming
    • Statistical Models
  • R Quiz
    • MCQs R Programming
    • R Basic Quiz 7
    • MCQs R Debugging 6
    • MCQs R Vectors 5
    • R History & Basics 4
    • R Language Test 3
    • R Language MCQs 2
    • R Language MCQs 1
  • MCQs
    • MCQs Statistics
      • MCQs Basic Statistics
      • MCQs Probability
      • MCQs Graph & Charts
      • MCQs Sampling
      • MCQs Inference
      • MCQs Correlation & Regression
      • MCQs Time Series
      • MCQs Index Numbers
      • MCQs Quality Control 1
    • MCQS Computer
    • MCQs Mathematics Part-I
  • About ME
  • Contact Us
  • Glossary

Tag: drop() function

Backward Deletion Method Step by Step in R

No Comments
| Statistical Models

When there are many predictor variables, one can create the most statistically significant model from the data. There are two main choices: forward stepwise regression and backward deletion method.
In Forward Stepwise Regression: Start off with the single best variable and add more variables to build your model into a more complex form.
In Backward Deletion (Backward Selection) Regression: put all the variables in the model and reduce the model by removing variables until you are left with only significant terms.

Backward Deletion method
Let start with a big model and trim it down until you get the best (most statistically significant) regression model. To do this drop1() command can be used to examine a linear model and determine the effect of removing each one from the existing model. Complete the following steps to perform a backward deletion. Note that there are different R packages for the Backward and Forward Selection of predictors in the model.

Step 1: To start, create a “full” model (all variables at once in the model). It would be tedious to enter all the variables in the model, one can use the shortcut, the dot notation.

mod <- lm(mpg ~., data = mtcars)

Step 2: Let use the formula() function to see the response and predictor variables used in Step 1.

formula(mod)

Step 3: Let use the drop1() function to see which term (predictor) should be deleted from the model

drop1(mod)

Step 4: Look to remove the term with the lowest AIC value. Re-form the model without the variable which one is non-significant or having the lowest AIC value. The simplest way to do this is to copy the model formula in the clipboard, paste it into a new command and edit out the term you do not want

mod1 <- lm(mpg~ ….., data = mtcars)

Step 5: Examine the effect of dropping another term by running the drop1() command once more:

drop1(mod1)

If you see any variable having the lowest AIC value, if found remove the variable and carry out this process repeatedly until you have a model that you are happy with.

Learn more about lm() function

Share this:

  • Twitter
  • Facebook
  • LinkedIn
  • Skype
  • Tumblr
  • Pinterest
  • Print
  • WhatsApp
  • Telegram
  • Reddit
  • Pocket

Like this:

Like Loading...

Read More »

Subscribe via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 265 other subscribers

Search Form

Facebook

Facebook

Categories

  • Advance R Programming (3)
  • Data Analysis (12)
    • Comparisons Tests (2)
    • Statistical Models (10)
  • Data Structure (9)
    • Data Frame (2)
    • Factors in R (1)
    • List (2)
    • Matrices (2)
    • Vectors in R (1)
  • Importing/ Exporting Data (4)
    • R Data Library (4)
  • R Control Structure (3)
    • For loop in R (1)
    • Switch Statement (1)
  • R FAQS (18)
    • Missing Values (2)
    • R Basics (12)
    • R FAQS about Package (3)
    • R Programming (2)
  • R Graphics (4)
    • Exploring Data in R (1)
    • plot Function (2)
  • R Language Basics (4)
  • R Language Quiz (8)
  • Using R packages (2)
https://www.youtube.com/watch?v=MZpiMyAfnYQ&list=PLB01qg3XnNiMbKkvP2wYzzHkv6ZekaKZx

Posts: itfeature.com: Basic Statistics and Data Analysis

MCQs Chi-Square Association 2

The relationship/ Dependency (also called Association) between the attributes is called relationship/association and the measure of degrees of relationship between the attributes is called the coefficient of association. The Chi-Square Statistic is used to…

Short Questions Sampling and Sampling Distributions 1

The post is about some important Short Questions about sampling and sampling distribution. Q1: Define Sample and Sampling. Answer: Sample: A small portion of the population representing the qualities of the population being sampled…

MCQs IBM SPSS-1

Online MCQs about IBM SPSS with answers.

MCQs Correlation and Regression 6

This Quiz contains MCQs about Correlation and Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, OLS Assumptions,…

Short Questions: Normal and Standard Normal Distribution

The following post is about Short Questions related to Normal and Standard Normal Distribution. Q1: What is a standard normal variable? Ans: The variable $Z=\frac{X-\mu}{\sigma}$ which measures the deviations of variable $X$ from the…

Posts: gmstat.com: GM Statistics

MCQs Number System – 4

MCQs Economics – 3

MCQs Economics – 2

Try MCQs Economics Test 1

MCQs Economics – 1

MCQs Econometrics Quiz 5

This quiz is about Econometrics, which covers the topics of Regression analysis, correlation, dummy variable, multicollinearity, heteroscedasticity, autocorrelation, and many other topics. Let’s start with MCQs Econometrics test An application of different statistical methods applied to the economic data used…

R Frequently Asked Questions 2023 . Powered by WordPress

%d bloggers like this:
    pixel