linear_model: Linear Models
In haisx/basiclm: Linear_model

Description Usage Arguments Value Examples

View source: R/linear_model.R

linear_model function is used to fit numeric linear models. It can be seen as a basic version of lm() function.

1	linear_model(formula, data, digit = 3, detailed = FALSE)

`formula`	regression formula of interests; an object of class "formula" (or one that can be coerced to that class). The formula should only contain numeric variables.
`data`	data used for regression model; an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.
`digit`	digit precision of output (except p-value); an optional int value. The default value in this function is 3.
`detailed`	whether the function output a detailed table; a optional boolean that indicates whether output the relevant detailed table. The default value of "detailed" is FALSE.

linear_model() will return the fitted model's information

It returns a list containing at least the following components:

call: the matched call.
coefficients: a named vector of coefficients.

If the option "detailed = TRUE", it would also return:

Significant_table: the table showing the whether the coefficients are significant.
Df: the degree of freedom of the model.
Rsq: the R squared.
Adjusted_Rsq: the adjusted R squared.
F_statistics: the F statistic of the model.
P_value: the P value of the model.

x = c(1,2,5,7,1,4)
y = c(3,4,7,1,8,8)
linear_model(y~x, detailed = TRUE)
linear_model(y~x + I(x^2), digit = 3, detailed = FALSE)
linear_model(Petal.Length~Sepal.Width, data = iris, digit = 3, detailed = TRUE)