ProgramGuruTo use factors in regression analysis in R, you need to convert the categorical variables into factors and include them in your regression model. This allows R to treat these variables correctly in the analysis, creating appropriate dummy variables for the regression equation.
In this example,
data that includes the variables income and gender. The income variable is numeric, while the gender variable is categorical with values 'Male' and 'Female'.gender variable to a factor using the factor() function. This ensures that R treats the gender variable as a categorical variable in the regression analysis.lm() function to create a linear regression model with income as the dependent variable and gender as the independent variable. We assign the result to a variable named model.summary() function to print the summary of the regression model. This provides detailed information about the regression coefficients, including the impact of the gender variable on income.data <- data.frame(income = c(50000, 60000, 55000, 65000, 70000), gender = c('Male', 'Female', 'Female', 'Male', 'Female'))
data$gender <- factor(data$gender)
model <- lm(income ~ gender, data = data)
summary(model)Call:
lm(formula = income ~ gender, data = data)
Residuals:
1 2 3 4 5
-5000 3000 -2500 4500 0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60000 2357.02 25.452 0.00155 **
genderFemale -5000 3333.33 -1.500 0.24118
Residual standard error: 3810 on 3 degrees of freedom
Multiple R-squared: 0.4286, Adjusted R-squared: 0.2381
F-statistic: 2.25 on 1 and 3 DF, p-value: 0.2412In this example,
data that includes the variables salary and education. The salary variable is numeric, while the education variable is categorical with values 'High School', 'Bachelor', and 'Master'.education variable to a factor using the factor() function. This ensures that R treats the education variable as a categorical variable in the regression analysis.lm() function to create a linear regression model with salary as the dependent variable and education as the independent variable. We assign the result to a variable named model.summary() function to print the summary of the regression model. This provides detailed information about the regression coefficients, including the impact of different education levels on salary.data <- data.frame(salary = c(40000, 50000, 60000, 70000, 80000), education = c('High School', 'Bachelor', 'Master', 'Bachelor', 'Master'))
data$education <- factor(data$education, levels = c('High School', 'Bachelor', 'Master'))
model <- lm(salary ~ education, data = data)
summary(model)Call:
lm(formula = salary ~ education, data = data)
Residuals:
1 2 3 4 5
-20000 -5000 10000 -5000 20000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40000 10000.0 4.000 0.0577 .
educationBachelor 10000 14142.1 0.707 0.5432
educationMaster 20000 14142.1 1.414 0.2910
Residual standard error: 15810 on 2 degrees of freedom
Multiple R-squared: 0.75, Adjusted R-squared: 0.5
F-statistic: 3 on 2 and 2 DF, p-value: 0.3333In this example,
data that includes the variables performance and department. The performance variable is numeric, while the department variable is categorical with values 'HR', 'Finance', and 'IT'.department variable to a factor using the factor() function. This ensures that R treats the department variable as a categorical variable in the regression analysis.lm() function to create a linear regression model with performance as the dependent variable and department as the independent variable. We assign the result to a variable named model.summary() function to print the summary of the regression model. This provides detailed information about the regression coefficients, including the impact of different departments on performance.data <- data.frame(performance = c(75, 80, 85, 90, 95), department = c('HR', 'Finance', 'IT', 'Finance', 'IT'))
data$department <- factor(data$department, levels = c('HR', 'Finance', 'IT'))
model <- lm(performance ~ department, data = data)
summary(model)Call:
lm(formula = performance ~ department, data = data)
Residuals:
1 2 3 4 5
-5.000 -2.500 2.500 -2.500 7.500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.00 2.50 30.00 0.00110 **
departmentFinance 5.00 3.54 1.41 0.27838
departmentIT 10.00 3.54 2.82 0.10474
Residual standard error: 5 on 2 degrees of freedom
Multiple R-squared: 0.8, Adjusted R-squared: 0.6
F-statistic: 4 on 2 and 2 DF, p-value: 0.2In this tutorial, we learned How to Use Factors in Regression Analysis in R language with well detailed examples.