Creating new Functions with Linear Regression in R

Question

Having problem when creating a function that calls the lm() function:

regresionLineal <- function (vardep, varindep1, varindep2, DATA) {
  lm(vardep ~ varindep1 + varindep2, data = DATA)
  }

Then I call it using data from a data frame I created previously (DATOS)...

regresionLineal(Estatura, Largo, Ancho, DATOS)

Error in eval(expr, envir, enclos) : object 'Estatura' not found Called from: eval(expr, envir, enclos)

Answer 1

When we want to create a model with an arbitrary number of independent variables, we can use the below:

create_lm <- function(data, dep, covs) {
# Create the first part of the formula with the dependent variable
  form_base <- paste(dep, "~")
# Create a string that concatenates your covs vector with a "+" between each variable
  form_vars <- paste(covs, collapse = " + ")
# Paste the two parts together
  formula <- paste(form_base, form_vars)
# Call the lm function on your formula
  lm(formula, data = data)
}

For example, using the built-in mtcars dataset:

create_lm(mtcars, "mpg", c("wt", "cyl"))

Call:
lm(formula = formula, data = data)

Coefficients:
(Intercept)           wt          cyl  
     39.686       -3.191       -1.508  

The downside is that the printed output from the model doesn't reflect the particular call you made to lm, not sure if there is any way around this.

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