Learning Linear Regression with moderndive in R

Moderndive

We will learn linear regression with Moderndive. The moderndive R package consists of datasets and function for tidyverse-friendly introductory linear regressions. Refer to: https://moderndive.github.io/moderndive/

library(moderndive)
score_model <- lm(score ~ age, data = evals)

1. Get a tidy regression table with confidence intervals

get_regression_table(score_model)

## # A tibble: 2 × 7
##   term      estimate std_error statistic p_value lower_ci upper_ci
##   <chr>        <dbl>     <dbl>     <dbl>   <dbl>    <dbl>    <dbl>
## 1 intercept    4.46      0.127     35.2    0        4.21     4.71 
## 2 age         -0.006     0.003     -2.31   0.021   -0.011   -0.001

2. Get information on each point/observation in your regression, including fitted/predicted values and residuals, in a single data frame:

get_regression_points(score_model)

## # A tibble: 463 × 5
##       ID score   age score_hat residual
##    <int> <dbl> <int>     <dbl>    <dbl>
##  1     1   4.7    36      4.25    0.452
##  2     2   4.1    36      4.25   -0.148
##  3     3   3.9    36      4.25   -0.348
##  4     4   4.8    36      4.25    0.552
##  5     5   4.6    59      4.11    0.488
##  6     6   4.3    59      4.11    0.188
##  7     7   2.8    59      4.11   -1.31 
##  8     8   4.1    51      4.16   -0.059
##  9     9   3.4    51      4.16   -0.759
## 10    10   4.5    40      4.22    0.276
## # … with 453 more rows

3. Get scalar summaries of a regression fit including R-squared and R-squared adjusted but also the (root) mean-squared error:

get_regression_summaries(score_model)

## # A tibble: 1 × 9
##   r_squared adj_r_squared   mse  rmse sigma statistic p_value    df  nobs
##       <dbl>         <dbl> <dbl> <dbl> <dbl>     <dbl>   <dbl> <dbl> <dbl>
## 1     0.011         0.009 0.292 0.540 0.541      5.34   0.021     1   463

4. Visualize parallel slopes models using the geom_parallel_slopes() custom ggplot2 geometry: Not completed.