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fn_dfa

fn_dfa.Rd

The `fn_dfa()` function uses the `augment()` function from the `broom` package to extract the observed values of the independent variable (x) and dependent variable (y), along with the weights (wi), fitted values (fitt), and residuals (ei) from the summary of the fitted model. It then turns these components into tidy tibbles.

Usage

fn_dfa(eq)

Arguments

eq

Summary of the equation fitted.

Value

A tibble data frame provides the original data along with information about the model.

Details

The function `augment()` does not provide the weights column for the `lmrob()` function. The function `fn_dfa()` contains a conditional statement that includes this variable in the output data frame of the linear adjustments.

In order to homogenizes the results, the columns names were renamed as "y", "wi","x","fitt",and "ei".

An additional column has been included that codes errors using a scale based on weighted values: unweighted (u), weighted (w), and outliers (o) `dfa$scale <- ifelse(df$wi < 0.25, "o", ifelse(df$wi<1, "w", "u"))`.

For more information, please refer to the help documentation for `broom::augment()`.

See also

broom::augment

Examples

## Example 1: robust linear regression
 if (FALSE) { # \dontrun{
df <- df
eq <- robustbase::lmrob(y1 ~ x1, data= df,setting = "KS2014")

dfa <- fn_dfa(eq)
} # }

## Example 2: robust nonlinear regression
if (FALSE) { # \dontrun{
df <- df
a <- 0.01
b <- 3
eq <- nlrob(y1 ~ a*x1^b, data= datos,
start = list(a= a, b= b),
trace = FALSE)

dfa <- fn_dfa(eq)
} # }