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 title to regression model using stargazer in R?
To add title to regression model using stargazer, we can use title argument inside stargazer function.
For example, if we have a model called Reg_Model with Output as text then the title to this model using stargazer can be added by using the below mentioned command −
stargazer(Reg_Model,type="text",title="Regression Model between x and y")
Example 1
Following snippet creates a sample data frame −
x<-rnorm(20) y<-rnorm(20) df<-data.frame(x,y) df
Output
The following dataframe is created −
x y 1 0.80296200 1.1413965 2 0.05853869 -1.1227868 3 1.79348142 -0.7212954 4 0.64830308 0.2956645 5 0.28551170 -1.0645189 6 0.50265553 0.9082304 7 0.25883301 0.6513258 8 -0.28277606 0.5892909 9 -1.96142707 0.8310168 10 1.29804865 -0.7780162 11 -0.53807406 0.7256280 12 -0.01142374 0.3550352 13 0.61684358 0.5681672 14 -0.03707776 0.7279025 15 0.14411337 0.7942300 16 0.95380409 0.2789388 17 0.32599974 1.2477048 18 -0.80785235 0.3246518 19 -0.77913184 -0.5227336 20 0.11869989 0.4344650
To create regression model between x and y, add the following code to the above snippet −
Model<-lm(y~x,data=df) summary(Model)
Output
If you execute all the above given snippets as a single program, it generates the following Output −
Call: lm(formula = y ~ x, data = df) Residuals: Min 1Q Median 3Q Max -1.4303 -0.2917 0.1538 0.3906 0.9988 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.3204 0.1645 1.947 0.0673 . x -0.2191 0.2019 -1.085 0.2921 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7196 on 18 degrees of freedom Multiple R-squared: 0.06142, Adjusted R-squared: 0.009276 F-statistic: 1.178 on 1 and 18 DF, p-value: 0.2921
To get the model Output by using stargazer, add the following code to the above snippet −
library(stargazer) stargazer(Model,type="text")
Output
If you execute all the above given snippets as a single program, it generates the following Output −
=============================================== Dependent variable: --------------------------- y ----------------------------------------------- x -0.219 (0.202) Constant 0.320* (0.165) ----------------------------------------------- Observations 20 R2 0.061 Adjusted R2 0.009 Residual Std. Error 0.720 (df = 18) F Statistic 1.178 (df = 1; 18) =============================================== Note: *p<0.1; **p<0.05; ***p<0.01
To get the model Output stargazer with title, add the following code to the above snippet −
stargazer(Model,type="text",title="Regression Model between x and y")
Output
If you execute all the above given snippets as a single program, it generates the following Output −
Regression Model between x and y =============================================== Dependent variable: --------------------------- y ----------------------------------------------- x -0.219 (0.202) Constant 0.320* (0.165) ----------------------------------------------- Observations 20 R2 0.061 Adjusted R2 0.009 Residual Std. Error 0.720 (df = 18) F Statistic 1.178 (df = 1; 18) =============================================== Note: *p<0.1; **p<0.05; ***p<0.01
Example 2
Following snippet creates a sample data frame −
Height<-sample(135:180,20) Weight<-sample(38:80,20) dat<-data.frame(Height,Weight) dat
Output
The following dataframe is created −
Height Weight 1 172 56 2 149 49 3 163 76 4 135 73 5 138 75 6 168 54 7 169 45 8 165 63 9 178 79 10 159 55 11 150 47 12 171 65 13 147 53 14 173 39 15 162 57 16 144 46 17 136 40 18 156 43 19 142 42 20 151 78
Add the following code to the above snippet −
Mod<-lm(Height~Weight,data=dat) summary(Mod)
Output
If you execute all the above given snippets as a single program, it generates the following Output −
Call: lm(formula = Height ~ Weight, data = dat) Residuals: Min 1Q Median 3Q Max -23.007 -9.606 1.867 12.345 19.399 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 150.78696 13.61725 11.073 1.82e-09 *** Weight 0.09891 0.23376 0.423 0.677 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.75 on 18 degrees of freedom Multiple R-squared: 0.009848, Adjusted R-squared: -0.04516 F-statistic: 0.179 on 1 and 18 DF, p-value: 0.6772
Add the following code to the above snippet −
stargazer(Mod,type="text",title="Regression Model between Height and Weight")
Output
If you execute all the above given snippets as a single program, it generates the following Output −
Regression Model between Height and Weight =============================================== Dependent variable: --------------------------- Height ----------------------------------------------- Weight 0.099 (0.234) Constant 150.787*** (13.617) ----------------------------------------------- Observations 20 R2 0.010 Adjusted R2 -0.045 Residual Std. Error 13.746 (df = 18) F Statistic 0.179 (df = 1; 18) =============================================== Note: *p<0.1; **p<0.05; ***p<0.01
959 Views
To Continue Learning Please Login