Description Usage Arguments Details Value See Also Examples
View source: R/inspect_data_.R
inspect_data_dichotomous
checks if an object contains data
that is eligible to have been generated by a series of Bernoulli trials. This
can be useful to validate inputs in user-defined functions.
1 | inspect_data_dichotomous(data, success, allow_nas = TRUE, warning_nas = FALSE)
|
data, success |
Arbitrary objects. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
inspect_data_dichotomous
conducts a series of tests to check if
data
is eligible to have been generated by a series of Bernoulli trials.
Namely, inspect_data_dichotomous
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.
The number of unique values in data
and success
are adequate.
success
has length
1.
success
is observed in data
.
inspect_data_dichotomous
does not return any output. There are
three possible outcomes:
The call is silent if:
data
is eligible to have been generated by a series of Bernoulli trials
and there are no NA
or NaN
values in data
.
data
is eligible to have been generated by a series of Bernoulli
trials, 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
is eligible to have been generated by a series of Bernoulli trials
and success
is not observed in data
.
data
is eligible to have been generated by a series of Bernoulli
trials, 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
is not eligible to have been generated by a series of Bernoulli
trials.
data
is eligible to have been generated by a series of Bernoulli
trials, there are some NA
or NaN
values in data
and allow_nas
is
set to FALSE
.
inspect_par_bernoulli
to validate
Bernoulli/Binomial proportions.
inspect_data_categorical
and
inspect_data_cat_as_dichotom
to validate categorical
data.
inspect_par_multinomial
to validate vectors of
Multinomial proportions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # 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_dichotomous(x1, success = 1)
inspect_data_dichotomous(x2, success = TRUE)
inspect_data_dichotomous(x3, success = "yes")
inspect_data_dichotomous(x4, success = "yes")
inspect_data_dichotomous(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_dichotomous(y1, success = 1, warning_nas = TRUE))
try(inspect_data_dichotomous(y2, success = success))
# Calls that throw an informative error message:
try(inspect_data_dichotomous(NULL, 1))
try(inspect_data_dichotomous(c(1, 0), NULL))
try(inspect_data_dichotomous(list(1, 0), 1))
try(inspect_data_dichotomous(c(1, 0), list(1)))
try(inspect_data_dichotomous(numeric(0), 0))
try(inspect_data_dichotomous(1, numeric(0)))
try(inspect_data_dichotomous(NaN, 1))
try(inspect_data_dichotomous(NA, 1))
try(inspect_data_dichotomous(c(1, 0), NA))
try(inspect_data_dichotomous(c(1, 0), NaN))
try(inspect_data_dichotomous(c(1, 0), 2))
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