Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How to add a variable to the model in base R?
If we want to add variables to the model in base R then update function can be used. The update function will update the previous modle by adding the new variable and this variable can be a single variable as well as an interaction of the two or more also any possible transformation of the existing variables.
Example
Consider the below data frame −
x1<-rnorm(20) x2<-rnorm(20,5,1.14) x3<-rnorm(20,5,0.58) y1<-rnorm(20,20,2.25) df1<-data.frame(x1,x2,x3,y1) df1
Output
x1 x2 x3 y1 1 0.23523969 7.577512 5.443941 19.76642 2 0.11106994 7.504542 3.897426 19.65692 3 -0.09726361 7.277049 5.335444 19.27655 4 0.26056059 3.933092 4.203294 22.50656 5 -0.78472270 5.375368 5.480062 19.56555 6 -0.14489152 4.310053 5.704146 17.52129 7 -0.96409135 5.145660 4.753728 22.70288 8 -1.04832947 3.954133 4.820469 21.58309 9 -0.65659070 3.994727 4.791794 19.09328 10 0.88016095 6.480780 4.364470 18.50680 11 0.93215306 4.410714 4.664997 14.50948 12 1.49864968 5.172408 5.121840 21.58837 13 1.63126398 4.313327 4.389091 16.06222 14 0.33486400 4.756670 5.012716 16.63648 15 1.20832732 5.942533 6.097934 24.82682 16 1.27126998 6.753667 3.977962 22.59800 17 -0.42438014 4.766934 4.684150 19.70354 18 0.18121480 6.760182 5.444401 25.38505 19 -2.73192870 5.247787 5.305925 20.75227 20 -0.44498078 5.203272 5.877478 19.10085
Creating a linear regression model to predict y1 by using x1 and x2 −
Example
Model_1<-lm(y1~x1+x2,data=df1) summary(Model_1)
Output
Call: lm(formula = y1 ~ x1 + x2, data = df1) Residuals: Min 1 Q Median 3Q Max -4.4836 -1.8695 -0.5435 2.1606 4.8678 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16.2664 2.9395 5.534 3.64e-05 *** x1 -0.4001 0.6179 -0.647 0.526 x2 0.7027 0.5289 1.329 0.202 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.776 on 17 degrees of freedom Multiple R-squared: 0.1029, Adjusted R-squared: -0.002624 F-statistic: 0.9751 on 2 and 17 DF, p-value: 0.3973
Creating the model by adding x3 −
Example
Model_1<-lm(update(y1~x1+x2,~.+x3,data=df1)) summary(Model_1)
Output
Call: lm(formula = update(y1 ~ x1 + x2, ~. + x3, data = df1)) Residuals: Min 1Q Median 3Q Max -4.4014 -2.0418 -0.6401 2.3419 4.1880 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.5651 5.9847 2.267 0.0376 * x1 -0.3204 0.6498 -0.493 0.6287 x2 0.6838 0.5418 1.262 0.2251 x3 0.5635 1.0796 0.522 0.6089 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.838 on 16 degrees of freedom Multiple R-squared: 0.1179, Adjusted R-squared: -0.04746 F-statistic: 0.7131 on 3 and 16 DF, p-value: 0.5584
Creating the model by adding x3 and the interaction between x1 and x2 −
Example
Model_2<-lm(update(y1~x1+x2,~.+x1*x2+x3,data=df1)) summary(Model_2)
Output
Call: lm(formula = update(y1 ~ x1 + x2, ~. + x1 * x2 + x3, data = df1)) Residuals: Min 1Q Median 3Q Max -3.1970 -1.5739 -0.1827 0.9408 4.5058 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.9974 5.5099 2.540 0.0226 * x1 -8.9403 4.4024 -2.031 0.0604 . x2 0.3321 0.5293 0.627 0.5398 x3 0.7861 0.9996 0.786 0.4439 x1:x2 1.6809 0.8505 1.976 0.0668 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.611 on 15 degrees of freedom Multiple R-squared: 0.3002, Adjusted R-squared: 0.1135 F-statistic: 1.608 on 4 and 15 DF, p-value: 0.2236
Updated on: 07-Dec-2020
800 Views
Advertisements