inspect_bfactor: Validate vectors of Bayes factors
In inspector: Validation of Arguments and Objects in User-Defined Functions

Description Usage Arguments Details Value See Also Examples

inspect_bfactor checks if an object is a numeric vector of valid Bayes factor values. This can be useful to validate inputs, intermediate calculations or outputs in user-defined functions.

1	inspect_bfactor(x, allow_nas = TRUE, warning_nas = TRUE)

`x`	An arbitrary object.
`allow_nas`	Logical value. If `TRUE` then `NA` and `NaN` values in `x` are allowed. If `FALSE`, execution is stopped and an error message is thrown in case there are `NA` or `NaN` values in `x`.
`warning_nas`	Logical value. If `TRUE` then the presence of `NA` or `NaN` values in `x` generates a warning message. `NA` and `NaN` values pass silently otherwise (if `allow_nas` is `TRUE`).

inspect_bfactor conducts a series of tests to check if x is a numeric vector of valid Bayes factor values. Namely, inspect_bfactor checks if:

x is NULL or empty.
x is an atomic vector.
x is numeric.
x has NA or NaN values.
The values of x are non-negative.

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

The call is silent if:
- x is a numeric vector of valid Bayes factor values and there are no NA or NaN values in x.
- x is a numeric vector of valid Bayes factor values, there are some NA or NaN values in x, allow_nas is set to TRUE and warning_nas is set to FALSE.
An informative warning message is given if x is a numeric vector of valid Bayes factor values, there are some NA or NaN values in x and both allow_nas and warning_nas are set to TRUE.
An informative error message is thrown and the execution is stopped if:
- x is not a numeric vector of valid Bayes factor values.
- x is a numeric vector of valid Bayes factor values, there are some in NA or NaN values in x and allow_nas is set to FALSE.

inspect_bfactor_log to check if an object is a numeric vector of valid logarithmic Bayes factor values.
bfactor_interpret for the interpretation of Bayes factors.
inspect_bfactor_scale to check if an object is a valid Bayes factor interpretation scale.

# Calls that pass silently:
x1 <- c(0, 0.5, 1, 10, 50, 100)
x2 <- c(NA, 0.5, 1, 10, 50, 100)
inspect_bfactor(x1)
inspect_bfactor(x2, warning_nas = FALSE)
inspect_bfactor(x2, allow_nas = TRUE, warning_nas = FALSE)

# Call that throws an informative warning message:
y <- c(0.1, 0.2, NA, 0.4, 0.5)
try(inspect_bfactor(y))
try(inspect_bfactor(y, warning_nas = TRUE))
try(inspect_bfactor(y, allow_nas = TRUE, warning_nas = TRUE))

# Calls that throw informative error messages:
z <- c(-0.9, 0, 0.1, 0.2, 0.3, 0.4, 0.5)
try(inspect_bfactor(z))
mylist <- list(
  NULL, TRUE, factor(.5), matrix(0.5),
  "0.5", list(0.5), NA, NaN, numeric(0), -0.5, -5
)
try(inspect_bfactor(mylist[[1]]))
try(inspect_bfactor(mylist[[2]]))
try(inspect_bfactor(mylist[[3]]))
try(inspect_bfactor(mylist[[4]]))
try(inspect_bfactor(mylist[[5]]))
try(inspect_bfactor(mylist[[6]]))
try(inspect_bfactor(mylist[[7]]))
try(inspect_bfactor(mylist[[8]]))
try(inspect_bfactor(mylist[[9]]))
try(inspect_bfactor(mylist[[10]]))
try(inspect_bfactor(mylist[[11]]))