`lm_model.Rd`

Fit a linear regression using `lm()`

. Linear regression is used to explore the effect of continuous variables / categorical variables in predicting a normally-distributed continuous variables.
If you are using a categorical predictor to predict a continuous variable, some may call it a ANOVA / ANCOVA while it is just a special form of linear regression).
In this package, I will not build separate function for ANOVA & ANCOVA since they are the same as linear regression

```
lm_model(
data,
response_variable,
predictor_variable,
two_way_interaction_factor = NULL,
three_way_interaction_factor = NULL,
quite = FALSE
)
```

- data
data frame

- response_variable
response variable. Support

`dplyr::select()`

syntax.- predictor_variable
predictor variable. Support

`dplyr::select()`

syntax. It will automatically remove the response variable from predictor variable, so you can use`contains()`

or`start_with()`

safely.- two_way_interaction_factor
two-way interaction factors. You need to pass 2+ factor. Support

`dplyr::select()`

syntax.- three_way_interaction_factor
three-way interaction factor. You need to pass exactly 3 factors. Specifying three-way interaction factors automatically included all two-way interactions, so please do not specify the two_way_interaction_factor argument. Support

`dplyr::select()`

syntax.- quite
suppress printing output

an object class of `lm`

representing the linear regression fit

```
fit <- lm_model(
data = iris,
response_variable = "Sepal.Length",
predictor_variable = tidyselect::everything(),
two_way_interaction_factor = c(Sepal.Width, Species)
)
#> Warning: The following columns are coerced into numeric: Species
#> Fitting Model with lm:
#> Formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species + Sepal.Width*Species
```