Validate categorical data as dichotomous

inspect_data_cat_as_dichotom checks if an object contains valid categorical data that is eligible to be used as dichotomous data. This can be useful to validate inputs in user-defined functions.

inspect_data_cat_as_dichotom(
  data,
  success,
  allow_nas = TRUE,
  warning_nas = FALSE
)

Arguments

data, success	Arbitrary objects. success is meant to indicate the value of data that corresponds to a success.
allow_nas	Logical value. If TRUE then NA and NaN values in data are allowed. If FALSE, execution is stopped and an error message is thrown in case there are NA or NaN values in data.
warning_nas	Logical value. If TRUE then the presence of NA or NaN values in data generates a warning message. NA and NaN values pass silently otherwise (if allow_nas is set toTRUE).

Value

inspect_data_cat_as_dichotom does not return any output. There are three possible outcomes:

• The call is silent if:

  • data contains valid categorical data that is eligible to be used as dichotomous data and there are no NA or NaN values in data.

  • data contains valid categorical data that is eligible to be used as dichotomous data, there are some NA or NaN values in data, allow_nas is set to TRUE and warning_nas is set to FALSE.

• An informative warning message is thrown if:

  • data contains valid categorical data that is eligible to be used as dichotomous data and success is not observed in data.

  • data contains valid categorical data that is eligible to be used as dichotomous data, there are NA or NaN values in data and both allow_nas and warning_nas are set to TRUE.

• An informative error message is thrown and the execution is stopped if:

  • data does not contain valid categorical data that is eligible to be used as dichotomous data.

  • data contains valid categorical data that is eligible to be used as dichotomous data, there are some NA or NaN values in data and allow_nas is set to FALSE.

Details

inspect_data_cat_as_dichotom conducts a series of tests to check if data contains valid categorical data that is eligible to be used as dichotomous data. Namely, inspect_data_cat_as_dichotom checks if:

• data and success are NULL or empty.

• data and success are atomic and have an eligible data type (logical, integer, double, character).

• data and success have NA or NaN values.

• success has length 1.

• success is observed in data.

See also

• inspect_data_categorical to validate categorical.

• inspect_par_multinomial to validate vectors of Multinomial proportions.

• inspect_data_dichotomous to validate dichotomous data.

• inspect_par_bernoulli to validate Bernoulli/Binomial proportions.

Examples

# Calls that pass silently:
x1 <- c(1, 0, 0, 1, 0)
x2 <- c(FALSE, FALSE, TRUE)
x3 <- c("yes", "no", "yes")
x4 <- factor(c("yes", "no", "yes"))
x5 <- c(1, 0, 0, 1, 0, NA)
inspect_data_cat_as_dichotom(x1, success = 1)
inspect_data_cat_as_dichotom(x2, success = TRUE)
inspect_data_cat_as_dichotom(x3, success = "yes")
inspect_data_cat_as_dichotom(x4, success = "yes")
inspect_data_cat_as_dichotom(x5, success = 1)

# Calls that throw an informative warning message:
y1 <- c(1, 1, NA, 0, 0)
y2 <- c(0, 0)
success <- 1
try(inspect_data_cat_as_dichotom(y1, success = 1, warning_nas = TRUE))
#> Warning: There are NA or NaN values in y1.
try(inspect_data_cat_as_dichotom(y2, success = success))
#> Warning: success not observed in y2.

# Calls that throw an informative error message:
try(inspect_data_cat_as_dichotom(y1, 1, allow_nas = FALSE))
#> Error in inspect_data_cat_as_dichotom(y1, 1, allow_nas = FALSE) : 
#>   Invalid argument: there are NA or NaN values in  y1.
try(inspect_data_cat_as_dichotom(NULL, 1))
#> Error in inspect_data_cat_as_dichotom(NULL, 1) : 
#>   Invalid argument: NULL is NULL.
try(inspect_data_cat_as_dichotom(c(1, 0), NULL))
#> Error in inspect_data_cat_as_dichotom(c(1, 0), NULL) : 
#>   Invalid argument: NULL is NULL.
try(inspect_data_cat_as_dichotom(list(1, 0), 1))
#> Error in inspect_data_cat_as_dichotom(list(1, 0), 1) : 
#>   Invalid argument: list(1, 0) must be atomic.
try(inspect_data_cat_as_dichotom(c(1, 0), list(1)))
#> Error in inspect_data_cat_as_dichotom(c(1, 0), list(1)) : 
#>   Invalid argument: list(1) must be atomic and have length 1.
try(inspect_data_cat_as_dichotom(numeric(0), 0))
#> Error in inspect_data_cat_as_dichotom(numeric(0), 0) : 
#>   Invalid argument: numeric(0) is empty.
try(inspect_data_cat_as_dichotom(1, numeric(0)))
#> Error in inspect_data_cat_as_dichotom(1, numeric(0)) : 
#>   Invalid argument: numeric(0) must be atomic and have length 1.
try(inspect_data_cat_as_dichotom(NaN, 1))
#> Error in inspect_data_cat_as_dichotom(NaN, 1) : 
#>   Invalid argument: all elements of NaN are NA or NaN.
try(inspect_data_cat_as_dichotom(NA, 1))
#> Error in inspect_data_cat_as_dichotom(NA, 1) : 
#>   Invalid argument: all elements of NA are NA or NaN.
try(inspect_data_cat_as_dichotom(c(1, 0), NA))
#> Error in inspect_data_cat_as_dichotom(c(1, 0), NA) : 
#>   Invalid argument: NA is NA or NaN.
try(inspect_data_cat_as_dichotom(c(1, 0), NaN))
#> Error in inspect_data_cat_as_dichotom(c(1, 0), NaN) : 
#>   Invalid argument: NaN is NA or NaN.
try(inspect_data_cat_as_dichotom(c(1, 0), 2))
#> Warning: 2 not observed in c(1, 0).