how to creat r output for simple linear regression equation

As far as I know geom_smooth() formulas can't reference variables that aren't already mapped as aesthetics. That said, if you want to accurately plot the regression line for a model that includes variables that don't appear included in the plot, use geom_abline() instead and get the slope and intercept using broom package functions. As pointed out in the comment, the slope we get from modelling mpg as a function of cyl (-2.88) doesn't match the slope we get from modelling mpg as a function of cyl and other variables (-1.29). This approach of plotting a line really only makes sense to visualize the relationship between two variables.

Geom_smooth(formula = y ~ x, method = 'lm', se = FALSE) +Ĭaption = paste('mpg =', get_formula(fit1)) Ggplot(mapping = aes(x = cyl, y = mpg)) + Know how to obtain the estimated MSE of the unknown population variance 2 from Minitabs fitted line plot and regression analysis output. Then use ggplot2 to plot the line and add a caption mtcars %>% Summarize(terms = paste(term, collapse = ' ')) %>% Term = if_else(term = '', paste(sign, estimate), paste(sign, estimate, term)) Term = if_else(term = '(Intercept)', '', term),Įstimate = as.character(round(abs(estimate), digits = 2)), I wrote a function to extract and format the information using dplyr: get_formula % The key is the broom package, whcih simplifies the process of extracting model data. where and represent values 1 of the independent and dependent variables, respectively. The simple linear regression model is of the form. Here's a solution using tidyverse packages. The regression model is similar to the analysis of variance model discussed in Chapter 6 in that it consists of two parts, a deterministic or functional term and a random term.

How to creat r output for simple linear regression equation