Running simple linear regression (slr) on multiple variables across multiple grouping variables.

Source: R/grouped_slr.R

Running simple linear regression (slr) on multiple variables across multiple grouping variables.

grouped_slr(data, dep.vars, indep.vars, grouping.vars)

Arguments

data

Dataframe from which variables are to be taken.
dep.vars

List criterion or dependent variables for simple linear model (y in y ~ x).
indep.vars

List predictor or independent variables for simple linear model (x in y ~ x).
grouping.vars

List of grouping variables.

Value

A tibble dataframe with tidy results from simple linear regression analyses. The estimates are standardized, i.e. the lm model used is scale(y) ~ scale(x), and not y ~ x.

See also

grouped_lm, grouped_tidy

Examples

# for reproducibility
set.seed(123)

# in case of just one grouping variable
groupedstats::grouped_slr(
  data = iris,
  dep.vars = c(Sepal.Length, Petal.Length),
  indep.vars = c(Sepal.Width, Petal.Width),
  grouping.vars = Species
)

#> # A tibble: 6 x 9
#>   Species   formula       t.value estimate conf.low conf.high std.error  p.value
#>   <fct>     <chr>           <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#> 1 setosa    Sepal.Length…    7.68    0.743   0.548      0.937    0.0967 6.71e-10
#> 2 versicol… Sepal.Length…    4.28    0.526   0.279      0.773    0.123  8.77e- 5
#> 3 virginica Sepal.Length…    3.56    0.457   0.199      0.715    0.128  8.43e- 4
#> 4 setosa    Petal.Length…    2.44    0.332   0.0578     0.605    0.136  1.86e- 2
#> 5 versicol… Petal.Length…    8.83    0.787   0.607      0.966    0.0891 1.27e-11
#> 6 virginica Petal.Length…    2.36    0.322   0.0474     0.597    0.137  2.25e- 2
#> # … with 1 more variable: significance <chr>