This function takes in a data table and a predictor column. A user can either create
their own formula using the .formula parameter or, if they leave the default of
NULL then the user must enter a .degree AND .pred_col column.
Usage
hai_polynomial_augment(
.data,
.formula = NULL,
.pred_col = NULL,
.degree = 1,
.new_col_prefix = "nt_"
)Arguments
- .data
The data being passed that will be augmented by the function.
- .formula
This should be a valid formula like 'y ~ .^2' or NULL.
- .pred_col
This is passed
rlang::enquo()to capture the vector that you designate as the 'y' column.- .degree
This should be an integer and is used to set the degree in the poly function. The degree must be less than the unique data points or it will error out.
- .new_col_prefix
The default is "nt_" which stands for "new_term". You can set this to whatever you like, as long as it is a quoted string.
Details
A valid data.frame/tibble must be passed to this function. It is required that
a user either enter a .formula or a .degree AND .pred_col otherwise this
function will stop and error out.
Under the hood this function will create a stats::poly() function if the
.formula is left as NULL. For example:
.formula = A ~ .^2
OR .degree = 2, .pred_col = A
There is also a parameter .new_col_prefix which will add a character string
to the column names so that they are easily identified further down the line.
The default is 'nt_'
See also
Other Augment Function:
hai_fourier_augment(),
hai_fourier_discrete_augment(),
hai_hyperbolic_augment(),
hai_scale_zero_one_augment(),
hai_scale_zscore_augment(),
hai_winsorized_move_augment(),
hai_winsorized_truncate_augment()
Examples
suppressPackageStartupMessages(library(dplyr))
data_tbl <- data.frame(
A = c(0, 2, 4),
B = c(1, 3, 5),
C = c(2, 4, 6)
)
hai_polynomial_augment(.data = data_tbl, .pred_col = A, .degree = 2, .new_col_prefix = "n")
#> The formula used is: A ~ poly(B, 2) + poly(C, 2)
#> # A tibble: 3 × 8
#> A B C nintercept npoly_b_2_1 npoly_b_2_2 npoly_c_2_1 npoly_c_2_2
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1 2 1 -7.07e- 1 0.408 -7.07e- 1 0.408
#> 2 2 3 4 1 -7.85e-17 -0.816 -7.85e-17 -0.816
#> 3 4 5 6 1 7.07e- 1 0.408 7.07e- 1 0.408
hai_polynomial_augment(.data = data_tbl, .formula = A ~ .^2, .degree = 1)
#> The formula used is: A ~ .^2
#> # A tibble: 3 × 7
#> A B C nt_intercept nt_b nt_c nt_b_c
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1 2 1 1 2 2
#> 2 2 3 4 1 3 4 12
#> 3 4 5 6 1 5 6 30