Internal function to identify the type of response variable. Supported types are:
"continuous-binary": decimal numbers and two unique values; results in a warning, as this type is difficult to model.
"continuous-low": decimal numbers and 3 to 5 unique values; results in a message, as this type is difficult to model.
"continuous-high": decimal numbers and more than 5 unique values.
"integer-binomial": integer with 0s and 1s, suitable for binomial models.
"integer-binary": integer with 2 unique values other than 0 and 1; returns a warning, as this type is difficult to model.
"integer-low": integer with 3 to 5 unique values or meets specified thresholds.
"integer-high": integer with more than 5 unique values suitable for count modelling.
"categorical": character or factor with 2 or more levels.
"unknown": when the response type cannot be determined.
Arguments
- df
(required; data frame, tibble, or sf) A data frame with responses and predictors. Default: NULL.
- response
(optional; character string or vector) Name/s of response variable/s in
df. Used in target encoding when it names a numeric variable and there are categorical predictors, and to compute preference order. Default: NULL.- quiet
(optional; logical) If FALSE, messages generated during the execution of the function are printed to the console Default: FALSE
See also
Other data_types:
identify_predictors(),
identify_predictors_categorical(),
identify_predictors_numeric(),
identify_predictors_type(),
identify_predictors_zero_variance()
Examples
identify_response_type(
df = vi,
response = "vi_numeric"
)
#> [1] "continuous-high"
identify_response_type(
df = vi,
response = "vi_counts"
)
#> [1] "integer-high"
identify_response_type(
df = vi,
response = "vi_binomial"
)
#> [1] "integer-binomial"
identify_response_type(
df = vi,
response = "vi_categorical"
)
#> [1] "categorical"
identify_response_type(
df = vi,
response = "vi_factor"
)
#> [1] "categorical"