Linear and Logistic Regression R functions


Hi All,

Good day. I understand the Linear Regression is to create a model with Continous Predictions (like predicting the temperature for tomorrow) and Logistic Regression is categorical (Sunny,Rainy etc).

I would like to know what are the R functions to perform these two type of Regression.

Thank you.


Hi @nagu2487

The general framework in R is glm() part of the package stats (default in R). If you want to explore the interactions consider the glmutil() package as well. There is a good explanation about this package under glmutil

Hope this help



Thank you so much Lesaffrea. So you mean to say for both Linear and Logistic Regression we will use glm() ??


Hi @nagu2487

Yes there is one parameter to change, the family parameter , you set it to “family=binomial(logit)” for logistic regression and to gaussian for linear regression

For logistic regression this link will help you glm

Hope this help



Great thank you Lesaffrea.


In R for simple linear regression we use
lm () & cor.test().

As per the best fit line, we plot a curve using spline() for logistic regression.

Take help for the syntax in R


Regression assumes that variables have normal distributions. Non-normally distributed variables (highly skewed or kurtotic variables, or variables with substantial outliers) can distort relationships and significance tests.
So to Overcome i suggest you to use Box-Cox Transformation from ‘car’ Package you can use powerTransform function to get the value of lambda.
[ ]
Then you can go about to use bcPower or yjPower to get the values for your variable which will then be normally distributed.
Note that if you have a large data set then you need not worry( But do check for kurtosis)