Identify Zero and Near-Zero Variance Predictors
Source:R/identify.R
identify_predictors_zero_variance.Rd
Variables with a variance of zero or near-zero are highly problematic for multicollinearity analysis and modelling in general. This function identifies these variables with a level of sensitivity defined by the 'decimals' argument. Smaller number of decimals increase the number of variables detected as near zero variance. Recommended values will depend on the range of the numeric variables in 'df'.
Arguments
- df
(required; data frame, tibble, or sf) A data frame with responses and predictors. Default: NULL.
- predictors
(optional; character vector) Names of the predictors to select from
df
. If omitted, all numeric columns indf
are used instead. If argumentresponse
is not provided, non-numeric variables are ignored. Default: NULL- decimals
(required, integer) Number of decimal places for the zero variance test. Smaller numbers will increase the number of variables detected as near-zero variance. Recommended values will depend on the range of the numeric variables in 'df'. Default: 4
See also
Other data_types:
identify_predictors()
,
identify_predictors_categorical()
,
identify_predictors_numeric()
,
identify_predictors_type()
,
identify_response_type()
Examples
data(
vi,
vi_predictors
)
#create zero variance predictors
vi$zv_1 <- 1
vi$zv_2 <- runif(n = nrow(vi), min = 0, max = 0.0001)
#add to vi predictors
vi_predictors <- c(
vi_predictors,
"zv_1",
"zv_2"
)
#identify zero variance predictors
zero.variance.predictors <- identify_predictors_zero_variance(
df = vi,
predictors = vi_predictors
)
zero.variance.predictors
#> [1] "zv_1" "zv_2"