Fast Regression Models

Functions to do fast regression modelling. The functions return a tibble with statistics. Use plot() for an extensive model visualisation.

regression(x, ...)

# Default S3 method
regression(x, y = NULL, type = "lm", family = stats::gaussian, ...)

# S3 method for class 'data.frame'
regression(x, var1, var2 = NULL, type = "lm", family = stats::gaussian, ...)

# S3 method for class 'certestats_reg'
plot(x, ...)

# S3 method for class 'certestats_reg'
autoplot(object, ...)

Arguments

x

vector of values, or a data.frame

...

arguments for lm() or glm()

y

vector of values, optional

type

type of function to use, can be "lm" or "glm"

family

only used for glm()

var1, var2

column to use of x, the var2 argument is optional

object

data to plot

Examples

runif(10) |> regression()
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic p.value
#> * <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#> 1 (Intercept)    5.43       1.96    2.76    0.0245
#> 2 x              0.147      3.56    0.0414  0.968 

data.frame(x = 1:50, y = runif(50)) |>
  regression(x, y)
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic     p.value
#> * <chr>          <dbl>     <dbl>     <dbl>       <dbl>
#> 1 (Intercept) 0.480      0.0781      6.15  0.000000146
#> 2 x           0.000815   0.00266     0.306 0.761      

mrsa_from_blood_years <- c(0, 1, 0, 0, 2, 0, 1, 3, 1, 2, 3, 1, 2)
mrsa_from_blood_years |> plot()


mrsa_from_blood_years |> regression()
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic p.value
#> * <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#> 1 (Intercept)     4.33     1.38       3.14 0.00946
#> 2 x               2.17     0.854      2.54 0.0277 

mrsa_from_blood_years |> regression() |> plot()
#> `geom_smooth()` using formula = 'y ~ x'