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Draw a Quantile-Quantile Plot in R Programming – qqline() Function

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The Quantile-Quantile Plot in R Programming Language, or (Q-Q Plot) is defined as a value of two variables that are plotted corresponding to each other and check whether the distributions of two variables are similar or not concerning the locations. qqline() function in R Programming Language is used to draw a Q-Q Line Plot.

QQplot in R

Syntax: qqline(x, y, col)

Parameters: 

  • x, y: X and Y coordinates of plot
  • col: It defines color

Returns: A QQ Line plot of the coordinates provided 

Implementation of Basic QQplot in R using qqline() Function

R
# Set seed for reproducibility
set.seed(500)

# Create random normally distributed values
x <- rnorm(1200)

# QQplot of normally distributed values
qqnorm(x)

# Add qqline to plot
qqline(x, col = "darkgreen")

Output:


gh

QQplot in R


Above is a representation of QQplot of Normally Distributed Random Numbers.

Implementation of QQplot in R of Logistically Distributed Values  

R
# Set seed for reproducibility

# Random values according to logistic distribution
# QQplot of logistic distribution
y <- rlogis(800)

# QQplot of normally distributed values
qqnorm(y)

# Add qqline to plot
qqline(y, col = "darkgreen")

Output: 


gh

QQplot in R


Above is the Q-Q Plot of theoretical quantiles. 

Uniform Distribution of QQplot in R

R
# Set seed for reproducibility
set.seed(500)

# Create a uniform distribution
x_uniform <- runif(1200)

# QQplot of uniform distribution
qqnorm(x_uniform)
qqline(x_uniform, col = "darkgreen")

Output:

gh

QQplot in R

Conclusion

The QQplot in R is a powerful visualization tool in R commonly used to assess whether a given dataset follows a specific theoretical distribution, such as the normal distribution. The QQ plot compares the quantiles of the observed data against the quantiles expected from the theoretical distribution, allowing for a visual inspection of the distributional fit.


Last Updated : 26 Mar, 2024
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