extract residual standard error from lm in r

To extract the residual standard error from an lm (linear regression) model in R, you can follow these steps:

1. Fit the linear regression model using the lm() function. For example, if your response variable is "y" and your predictor variable is "x", you can use the following code: model <- lm(y ~ x)

2. Once the model is fitted, you can extract the residual standard error by accessing the "sigma" attribute of the model object. The "sigma" attribute represents the estimated standard deviation of the residuals. To extract it, use the following code: residual_standard_error <- sqrt(attr(model$terms, "sigma")^2)

3. The "sqrt" function is used to take the square root of the squared "sigma" value, resulting in the residual standard error.

By following these steps, you can extract the residual standard error from the lm model in R.