Check and complete argument preference_order

Internal function to validate the argument preference_order in cor_select(), vif_select(), collinear_select(), collinear(), and collinear(). Predictors not in preference_order are ranked from lower to higher sum of Pearson correlations with all other predictors.

Usage

validate_arg_preference_order(
  df = NULL,
  response = NULL,
  predictors = NULL,
  preference_order = NULL,
  quiet = FALSE,
  function_name = NULL,
  ...
)

Arguments

df

(required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. Default: NULL.

response

(optional, character string) Name of a numeric response variable in df. Default: NULL.

predictors

(optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. Default: NULL.

preference_order

(optional; character vector, dataframe from preference_order, or NULL) Prioritizes predictors to preserve.

quiet

(optional; logical) If FALSE, messages are printed. Default: FALSE.

function_name

(optional, character string) Name of the function performing the argument check. Default: NULL

...

(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

Value

character vector: ranked variable names

See also

Other argument_validation: drop_geometry_column(), validate_arg_df(), validate_arg_df_not_null(), validate_arg_encoding_method(), validate_arg_f(), validate_arg_function_name(), validate_arg_max_cor(), validate_arg_max_vif(), validate_arg_predictors(), validate_arg_quiet(), validate_arg_responses()

Examples

data(
  vi_smol,
  vi_predictors_numeric
  )

#input arguments must be validated first
df <- validate_arg_df(
  df = vi_smol,
  response = "vi_numeric",
  predictors = vi_predictors_numeric,
  quiet = TRUE
)

response <- validate_arg_responses(
  df = df,
  responses = "vi_numeric"
)

predictors <- validate_arg_predictors(
  df = df,
  response = response,
  predictors = vi_predictors_numeric[1:10]
)



#no preference order
#no response
#ranks predictor from lower to higher multicollinearity
y <- validate_arg_preference_order(
  df = df,
  predictors = predictors,
  preference_order = NULL
)
#> 
#> collinear::validate_arg_preference_order()
#> └── collinear::preference_order(): ranking 10 'predictors' from lower to higher multicollinearity.

y
#>    response             predictor            f        metric  score rank
#> 1      none        topo_elevation stats::cor() 1 - R-squared 0.8184    1
#> 2      none        topo_diversity stats::cor() 1 - R-squared 0.8183    2
#> 3      none            topo_slope stats::cor() 1 - R-squared 0.8001    3
#> 4      none  soil_temperature_min stats::cor() 1 - R-squared 0.7434    4
#> 5      none             swi_range stats::cor() 1 - R-squared 0.7387    5
#> 6      none soil_temperature_mean stats::cor() 1 - R-squared 0.6534    6
#> 7      none              swi_mean stats::cor() 1 - R-squared 0.6272    7
#> 8      none               swi_max stats::cor() 1 - R-squared 0.6259    8
#> 9      none               swi_min stats::cor() 1 - R-squared 0.6170    9
#> 10     none  soil_temperature_max stats::cor() 1 - R-squared 0.6021   10
attributes(y)$validated
#> [1] TRUE


#validate character vector
y <- validate_arg_preference_order(
  df = df,
  predictors = predictors,
  preference_order = c(
    "swi_max",
    "swi_min",
    "swi_deviance" #does not exist
  )
)
#> 
#> collinear::validate_arg_preference_order()
#> └── collinear::preference_order(): ranking 8 'predictors' from lower to higher multicollinearity.

y
#>    response             predictor            f          metric  score rank
#> 1      none               swi_max         <NA> user_preference 1.0000    1
#> 2      none               swi_min         <NA> user_preference 0.0000    2
#> 3      none        topo_diversity stats::cor()   1 - R-squared 0.8049    3
#> 4      none             swi_range stats::cor()   1 - R-squared 0.7981    4
#> 5      none        topo_elevation stats::cor()   1 - R-squared 0.7859    5
#> 6      none            topo_slope stats::cor()   1 - R-squared 0.7855    6
#> 7      none  soil_temperature_min stats::cor()   1 - R-squared 0.7452    7
#> 8      none              swi_mean stats::cor()   1 - R-squared 0.7229    8
#> 9      none soil_temperature_mean stats::cor()   1 - R-squared 0.7057    9
#> 10     none  soil_temperature_max stats::cor()   1 - R-squared 0.6854   10
attributes(y)$validated
#> [1] TRUE

#validate output of preference order
x <- preference_order(
  df = df,
  responses = response,
  predictors = predictors
)
#> 
#> collinear::preference_order()
#> └── collinear::f_auto(): selected function 'f_numeric_glm()' to compute preference order.

x
#>      response             predictor             f    metric  score rank
#> 1  vi_numeric              swi_mean f_numeric_glm R-squared 0.7182    1
#> 2  vi_numeric               swi_max f_numeric_glm R-squared 0.5549    2
#> 3  vi_numeric  soil_temperature_max f_numeric_glm R-squared 0.5462    3
#> 4  vi_numeric             swi_range f_numeric_glm R-squared 0.4225    4
#> 5  vi_numeric               swi_min f_numeric_glm R-squared 0.2130    5
#> 6  vi_numeric  soil_temperature_min f_numeric_glm R-squared 0.0972    6
#> 7  vi_numeric        topo_diversity f_numeric_glm R-squared 0.0663    7
#> 8  vi_numeric        topo_elevation f_numeric_glm R-squared 0.0351    8
#> 9  vi_numeric            topo_slope f_numeric_glm R-squared 0.0176    9
#> 10 vi_numeric soil_temperature_mean f_numeric_glm R-squared 0.0151   10

y <- validate_arg_preference_order(
  df = df,
  response = response,
  predictors = predictors,
  preference_order = x
)

y
#>      response             predictor             f    metric  score rank
#> 1  vi_numeric              swi_mean f_numeric_glm R-squared 0.7182    1
#> 2  vi_numeric               swi_max f_numeric_glm R-squared 0.5549    2
#> 3  vi_numeric  soil_temperature_max f_numeric_glm R-squared 0.5462    3
#> 4  vi_numeric             swi_range f_numeric_glm R-squared 0.4225    4
#> 5  vi_numeric               swi_min f_numeric_glm R-squared 0.2130    5
#> 6  vi_numeric  soil_temperature_min f_numeric_glm R-squared 0.0972    6
#> 7  vi_numeric        topo_diversity f_numeric_glm R-squared 0.0663    7
#> 8  vi_numeric        topo_elevation f_numeric_glm R-squared 0.0351    8
#> 9  vi_numeric            topo_slope f_numeric_glm R-squared 0.0176    9
#> 10 vi_numeric soil_temperature_mean f_numeric_glm R-squared 0.0151   10
attributes(y)$validated
#> [1] TRUE