,
FCT [fnd]| Title: | Validation of Arguments and Objects in User-Defined Functions |
|---|---|
| Description: | Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions. |
| Authors: | Pedro Fonseca [aut, cre] ,
FCT [fnd] |
| Maintainer: | Pedro Fonseca <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.3 |
| Built: | 2025-02-26 03:30:05 UTC |
| Source: | https://github.com/ptfonseca/inspector |
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.
inspect_bfactor(x, allow_nas = TRUE, warning_nas = TRUE)inspect_bfactor(x, allow_nas = TRUE, warning_nas = TRUE)
x |
An arbitrary object. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
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]]))# 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]]))
inspect_bfactor_log checks if an object is a numeric vector
of valid logarithmic Bayes factor values. This can be useful to validate
inputs, intermediate calculations or outputs in user-defined functions.
inspect_bfactor_log(x, allow_nas = TRUE, warning_nas = TRUE)inspect_bfactor_log(x, allow_nas = TRUE, warning_nas = TRUE)
x |
An arbitrary object. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
inspect_bfactor_log conducts a series of tests to check if x is
a numeric vector of valid logarithmic Bayes factor values. Namely,
inspect_bfactor_log checks if:
x is NULL or empty.
x is an atomic vector.
x is numeric.
x has NA or NaN values.
inspect_bfactor_log does not return any output. There are three
possible outcomes:
The call is silent if:
x is a numeric vector of valid logarithmic Bayes factor values and
there are no NA or NaN values in x.
x is a numeric vector of valid logarithmic 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
logarithmic 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 logarithmic Bayes factor values.
x is a numeric vector of valid logarithmic Bayes factor values, there
are some NA or NaN values in x and allow_nas is set to FALSE.
inspect_bfactor to check if an object is a numeric
vector of valid Bayes factor values.
bfactor_log_interpret for the interpretation of the
logarithms of Bayes factors.
inspect_bfactor_scale to check if an object is a
Bayes factor interpretation scale.
inspect_log_base to check if an object is an
eligible logarithmic base.
# Calls that pass silently: x1 <- c(0, 0.5, 1, 10, 50, 100) x2 <- c(NA, 0.5, 1, 10, 50, 100) inspect_bfactor_log(x1) inspect_bfactor_log(x2, warning_nas = FALSE) inspect_bfactor_log(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_log(y)) try(inspect_bfactor_log(y, warning_nas = TRUE)) try(inspect_bfactor_log(y, allow_nas = TRUE, warning_nas = TRUE)) # Calls that throw informative error messages: mylist <- list( NULL, TRUE, factor(.5), matrix(0.5), "0.5", list(0.5), numeric(0), NA, NaN ) try(inspect_bfactor_log(mylist[[1]])) try(inspect_bfactor_log(mylist[[2]])) try(inspect_bfactor_log(mylist[[3]])) try(inspect_bfactor_log(mylist[[4]])) try(inspect_bfactor_log(mylist[[5]])) try(inspect_bfactor_log(mylist[[6]])) try(inspect_bfactor_log(mylist[[7]])) try(inspect_bfactor_log(mylist[[8]])) try(inspect_bfactor_log(mylist[[9]]))# Calls that pass silently: x1 <- c(0, 0.5, 1, 10, 50, 100) x2 <- c(NA, 0.5, 1, 10, 50, 100) inspect_bfactor_log(x1) inspect_bfactor_log(x2, warning_nas = FALSE) inspect_bfactor_log(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_log(y)) try(inspect_bfactor_log(y, warning_nas = TRUE)) try(inspect_bfactor_log(y, allow_nas = TRUE, warning_nas = TRUE)) # Calls that throw informative error messages: mylist <- list( NULL, TRUE, factor(.5), matrix(0.5), "0.5", list(0.5), numeric(0), NA, NaN ) try(inspect_bfactor_log(mylist[[1]])) try(inspect_bfactor_log(mylist[[2]])) try(inspect_bfactor_log(mylist[[3]])) try(inspect_bfactor_log(mylist[[4]])) try(inspect_bfactor_log(mylist[[5]])) try(inspect_bfactor_log(mylist[[6]])) try(inspect_bfactor_log(mylist[[7]])) try(inspect_bfactor_log(mylist[[8]])) try(inspect_bfactor_log(mylist[[9]]))
inspect_bfactor_scale checks if an object is a character
vector of length 1 that is eligible to represent one of
the Bayes factor interpretation scales available in the pcal package. This
can be useful to validate inputs in user-defined functions.
inspect_bfactor_scale(x)inspect_bfactor_scale(x)
x |
An arbitrary object. |
inspect_bfactor_scale conducts a series of tests to check if x
is a character vector of length 1 that is eligible to
represent one of the Bayes factor interpretation scales available in the
pcal package. Namely, inspect_bfactor_scale checks if:
x is NULL or empty.
x is NA or NaN.
x is an atomic vector of length 1
The typeof x is character
The value of x is either "Jeffreys" or "Kass-Raftery" (not case
sensitive).
inspect_bfactor_scale does not return any output. There are two
possible scenarios:
The call is silent if x is a character vector of
length 1 that is eligible to represent one of the Bayes
factor interpretation scales available in the pcal package.
An informative error message is thrown otherwise.
bfactor_interpret for the interpretation of Bayes
factors.
bfactor_log_interpret for the interpretation of the
logarithms of Bayes factors.
inspect_bfactor to check if an object is a numeric
vector of valid Bayes factor values.
inspect_bfactor_log to check if an object is a
numeric vector of valid logarithmic Bayes factor values.
# Calls that pass silently: x1 <- "Jeffreys" x2 <- "jeffreys" x3 <- "kass-raftery" x4 <- "Kass-Raftery" inspect_bfactor_scale(x1) inspect_bfactor_scale(x2) inspect_bfactor_scale(x3) inspect_bfactor_scale(x4) # Calls that throw informative error messages: mylist <- list( NULL, NA, NaN, 10, "Bayes", "Jeff", "kassraftery", c("jeffreys", "kass-raftery") ) try(inspect_bfactor_scale(mylist[[1]])) try(inspect_bfactor_scale(mylist[[2]])) try(inspect_bfactor_scale(mylist[[3]])) try(inspect_bfactor_scale(mylist[[4]])) try(inspect_bfactor_scale(mylist[[5]])) try(inspect_bfactor_scale(mylist[[6]])) try(inspect_bfactor_scale(mylist[[7]])) try(inspect_bfactor_scale(mylist[[8]]))# Calls that pass silently: x1 <- "Jeffreys" x2 <- "jeffreys" x3 <- "kass-raftery" x4 <- "Kass-Raftery" inspect_bfactor_scale(x1) inspect_bfactor_scale(x2) inspect_bfactor_scale(x3) inspect_bfactor_scale(x4) # Calls that throw informative error messages: mylist <- list( NULL, NA, NaN, 10, "Bayes", "Jeff", "kassraftery", c("jeffreys", "kass-raftery") ) try(inspect_bfactor_scale(mylist[[1]])) try(inspect_bfactor_scale(mylist[[2]])) try(inspect_bfactor_scale(mylist[[3]])) try(inspect_bfactor_scale(mylist[[4]])) try(inspect_bfactor_scale(mylist[[5]])) try(inspect_bfactor_scale(mylist[[6]])) try(inspect_bfactor_scale(mylist[[7]])) try(inspect_bfactor_scale(mylist[[8]]))
inspect_categories checks if an object is eligible to be used
as the levels of a factor. This can be useful to validate inputs in
user-defined functions.
inspect_categories(x)inspect_categories(x)
x |
An arbitrary object. |
inspect_categories conducts a series of tests to check if x
is eligible to be used as the levels of a factor. Namely,
inspect_categories checks if:
x is NULL or empty.
x is atomic.
x has an eligible data type (logical, integer, double, character).
There are NA or NaN values in x.
There are repeated values in x.
inspect_categories does not return any output. There are two
possible outcomes:
The call is silent if x is eligible to be used as the levels of a factor.
An informative error message is thrown otherwise.
inspect_data_dichotomous to validate dichotomous
data.
inspect_data_categorical and
inspect_data_cat_as_dichotom to validate categorical
data.
inspect_par_bernoulli to validate
Bernoulli/Binomial proportions.
inspect_par_multinomial to validate vectors of
Multinomial proportions.
inspect_character to validate character vectors.
inspect_character_match to validate character
vectors with predefined allowed values.
# Calls that pass silently: x1 <- 1:5 x2 <- c("yes", "no") x3 <- c(TRUE, FALSE) x4 <- factor(c("smoker", "non-smoker")) x5 <- factor(c("yes", "no", "yes")) inspect_categories(x1) inspect_categories(x2) inspect_categories(x3) inspect_categories(x4) inspect_categories(levels(x5)) # Calls that throw informative error messages: y1 <- c(1, 1:5) y2 <- c("yes", "no", "yes") y3 <- factor(c("yes", "no", "yes")) try(inspect_categories(y1)) try(inspect_categories(y2)) try(inspect_categories(y3)) try(mylist <- list( NULL, numeric(0), complex(1), list(10), NaN, NA )) try(inspect_categories(mylist[[1]])) try(inspect_categories(mylist[[2]])) try(inspect_categories(mylist[[3]])) try(inspect_categories(mylist[[4]])) try(inspect_categories(mylist[[5]])) try(inspect_categories(mylist[[6]]))# Calls that pass silently: x1 <- 1:5 x2 <- c("yes", "no") x3 <- c(TRUE, FALSE) x4 <- factor(c("smoker", "non-smoker")) x5 <- factor(c("yes", "no", "yes")) inspect_categories(x1) inspect_categories(x2) inspect_categories(x3) inspect_categories(x4) inspect_categories(levels(x5)) # Calls that throw informative error messages: y1 <- c(1, 1:5) y2 <- c("yes", "no", "yes") y3 <- factor(c("yes", "no", "yes")) try(inspect_categories(y1)) try(inspect_categories(y2)) try(inspect_categories(y3)) try(mylist <- list( NULL, numeric(0), complex(1), list(10), NaN, NA )) try(inspect_categories(mylist[[1]])) try(inspect_categories(mylist[[2]])) try(inspect_categories(mylist[[3]])) try(inspect_categories(mylist[[4]])) try(inspect_categories(mylist[[5]])) try(inspect_categories(mylist[[6]]))
inspect_character checks if an object is a character vector.
This can be useful to validate inputs in user-defined functions.
inspect_character(x, allow_nas = TRUE, warning_nas = FALSE)inspect_character(x, allow_nas = TRUE, warning_nas = FALSE)
x |
An arbitrary object. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
inspect_character conducts a series of tests to check if x is a
character vector. Namely, inspect_character checks if:
x is NULL or empty.
x is an atomic vector.
The typeof x is character.
There are NA or NaN values in x.
inspect_character does not return any output. There are three
possible outcomes:
The call is silent if:
x is a character vector and there are no NA or NaN values in x.
x is a character vector, 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 thrown if x is a character vector,
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 if:
x is not a character vector.
x is a character vector, there are some NA or NaN values in x
and allow_nas is set to FALSE.
inspect_character_match to validate character
vectors with predefined allowed values.
inspect_true_or_false to check if an object is a
non-missing logical value.
# Calls that pass silently: x1 <- "Kass" x2 <- c("Kass", "Raftery") x3 <- c("Kass", "Raftery", NA) x4 <- letters inspect_character(x1) inspect_character(x2) inspect_character(x3) inspect_character(x4) # Call that throws an informative warning message y <- c("Kass", "Raftery", NA) try(inspect_character(y, warning_nas = TRUE)) # Calls that throw informative error messages try(inspect_character(y, allow_nas = FALSE)) mylist <- list( NULL, character(0), 1, c(1, 2), factor(c(1, 2)), list(c(1, 2)), NaN, NA ) try(inspect_character(mylist[[1]])) try(inspect_character(mylist[[2]])) try(inspect_character(mylist[[3]])) try(inspect_character(mylist[[4]])) try(inspect_character(mylist[[5]])) try(inspect_character(mylist[[6]])) try(inspect_character(mylist[[7]])) try(inspect_character(mylist[[8]]))# Calls that pass silently: x1 <- "Kass" x2 <- c("Kass", "Raftery") x3 <- c("Kass", "Raftery", NA) x4 <- letters inspect_character(x1) inspect_character(x2) inspect_character(x3) inspect_character(x4) # Call that throws an informative warning message y <- c("Kass", "Raftery", NA) try(inspect_character(y, warning_nas = TRUE)) # Calls that throw informative error messages try(inspect_character(y, allow_nas = FALSE)) mylist <- list( NULL, character(0), 1, c(1, 2), factor(c(1, 2)), list(c(1, 2)), NaN, NA ) try(inspect_character(mylist[[1]])) try(inspect_character(mylist[[2]])) try(inspect_character(mylist[[3]])) try(inspect_character(mylist[[4]])) try(inspect_character(mylist[[5]])) try(inspect_character(mylist[[6]])) try(inspect_character(mylist[[7]])) try(inspect_character(mylist[[8]]))
inspect_character_match checks if an object is a character
vector of length 1 that belongs to a set of allowed
values. This can be useful to validate inputs in user-defined functions.
inspect_character_match(x, allowed, case_sensitive = FALSE)inspect_character_match(x, allowed, case_sensitive = FALSE)
x |
An arbitrary object. |
allowed |
A character vector. |
case_sensitive |
A non-missing logical value. |
inspect_character_match conducts a series of tests to check if x
is a character vector of length 1 whose value belongs to
the set of allowed values. Namely, inspect_character_match checks if:
x is NULL or empty.
x is an atomic vector of length 1.
The typeof x is character.
x is NA or NaN.
x is one of the allowed values (as specified in the allowed argument).
By default, the comparison of x with allowed is not case sensitive. If
you only want case sensitive matches of x to allowed set case_sensitive
to TRUE.
inspect_character_match does not return any output. There are two
possible outcomes:
The call is silent if x is a character vector of
length 1 whose value belongs to the set of allowed
values.
An informative error message is thrown otherwise.
inspect_character to validate character vectors
with arbitrary allowed values.
inspect_true_or_false to check if an object is a
non-missing logical value.
# Calls that pass silently: x1 <- "Kass" x2 <- "kass" inspect_character_match(x1, allowed = c("Kass", "Raftery")) inspect_character_match(x2, allowed = c("Kass", "Raftery")) # Calls that throw informative error messages: y1 <- "kasss" y2 <- "kass" try(inspect_character_match(y1, allowed = c("Kass", "Raftery"))) try(inspect_character_match(y2, allowed = c("Kass", "Raftery"), case_sensitive = TRUE )) mylist <- list( NULL, character(0), c("abc", "abcd"), c("abc", "abc"), "ab", list("abc"), factor("abc"), NaN, NA ) try(inspect_character_match(mylist[[1]], "abc")) try(inspect_character_match(mylist[[2]], "abc")) try(inspect_character_match(mylist[[3]], "abc")) try(inspect_character_match(mylist[[4]], "abc")) try(inspect_character_match(mylist[[5]], "abc")) try(inspect_character_match(mylist[[6]], "abc")) try(inspect_character_match(mylist[[7]], "abc")) try(inspect_character_match(mylist[[8]], "abc")) try(inspect_character_match(mylist[[9]], "abc"))# Calls that pass silently: x1 <- "Kass" x2 <- "kass" inspect_character_match(x1, allowed = c("Kass", "Raftery")) inspect_character_match(x2, allowed = c("Kass", "Raftery")) # Calls that throw informative error messages: y1 <- "kasss" y2 <- "kass" try(inspect_character_match(y1, allowed = c("Kass", "Raftery"))) try(inspect_character_match(y2, allowed = c("Kass", "Raftery"), case_sensitive = TRUE )) mylist <- list( NULL, character(0), c("abc", "abcd"), c("abc", "abc"), "ab", list("abc"), factor("abc"), NaN, NA ) try(inspect_character_match(mylist[[1]], "abc")) try(inspect_character_match(mylist[[2]], "abc")) try(inspect_character_match(mylist[[3]], "abc")) try(inspect_character_match(mylist[[4]], "abc")) try(inspect_character_match(mylist[[5]], "abc")) try(inspect_character_match(mylist[[6]], "abc")) try(inspect_character_match(mylist[[7]], "abc")) try(inspect_character_match(mylist[[8]], "abc")) try(inspect_character_match(mylist[[9]], "abc"))
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 )inspect_data_cat_as_dichotom( data, success, allow_nas = TRUE, warning_nas = FALSE )
data, success
|
Arbitrary objects. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
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.
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.
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.
# 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)) try(inspect_data_cat_as_dichotom(y2, success = success)) # Calls that throw an informative error message: try(inspect_data_cat_as_dichotom(y1, 1, allow_nas = FALSE)) try(inspect_data_cat_as_dichotom(NULL, 1)) try(inspect_data_cat_as_dichotom(c(1, 0), NULL)) try(inspect_data_cat_as_dichotom(list(1, 0), 1)) try(inspect_data_cat_as_dichotom(c(1, 0), list(1))) try(inspect_data_cat_as_dichotom(numeric(0), 0)) try(inspect_data_cat_as_dichotom(1, numeric(0))) try(inspect_data_cat_as_dichotom(NaN, 1)) try(inspect_data_cat_as_dichotom(NA, 1)) try(inspect_data_cat_as_dichotom(c(1, 0), NA)) try(inspect_data_cat_as_dichotom(c(1, 0), NaN)) try(inspect_data_cat_as_dichotom(c(1, 0), 2))# 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)) try(inspect_data_cat_as_dichotom(y2, success = success)) # Calls that throw an informative error message: try(inspect_data_cat_as_dichotom(y1, 1, allow_nas = FALSE)) try(inspect_data_cat_as_dichotom(NULL, 1)) try(inspect_data_cat_as_dichotom(c(1, 0), NULL)) try(inspect_data_cat_as_dichotom(list(1, 0), 1)) try(inspect_data_cat_as_dichotom(c(1, 0), list(1))) try(inspect_data_cat_as_dichotom(numeric(0), 0)) try(inspect_data_cat_as_dichotom(1, numeric(0))) try(inspect_data_cat_as_dichotom(NaN, 1)) try(inspect_data_cat_as_dichotom(NA, 1)) try(inspect_data_cat_as_dichotom(c(1, 0), NA)) try(inspect_data_cat_as_dichotom(c(1, 0), NaN)) try(inspect_data_cat_as_dichotom(c(1, 0), 2))
inspect_data_categorical checks if an object contains data
that is eligible to have been generated by a Multinomial distribution. This
can be useful to validate inputs in user-defined functions.
inspect_data_categorical(data, allow_nas = TRUE, warning_nas = FALSE)inspect_data_categorical(data, allow_nas = TRUE, warning_nas = FALSE)
data |
An arbitrary object. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
inspect_data_categorical conducts a series of tests to check if
data is eligible to have been generated by a Multinomial distribution.
Namely, inspect_data_categorical checks if:
data is NULL or empty.
data is atomic and have an eligible data type (logical, integer, double,
character).
data has NA or NaN values.
inspect_data_categorical does not return any output. There are
three possible outcomes:
The call is silent if:
data is eligible to have been generated by a Multinomial distribution
and there are no NA or NaN values in data.
data is eligible to have been generated by a Multinomial distribution,
there are some NA or NaN values in data and warning_nas is set to
FALSE.
An informative warning message is thrown if: data is eligible to have
been generated by a Multinomial distribution, there are some NA or NaN
values in data and warning_nas is 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 Multinomial
distribution.
data is eligible to have been generated by a Multinomial distribution,
there are some NA or NaN values in data and allow_nas is set to
TRUE.
inspect_data_cat_as_dichotom to validate
categorical data as dichotomous.
inspect_par_multinomial to validate vectors of
Multinomial proportions.
inspect_data_dichotomous to validate dichotomous
data.
inspect_par_bernoulli to validate
Bernoulli/Binomial proportions.
# Calls that pass silently: x1 <- c(1, 0, 0, 1, 2) x2 <- c(FALSE, FALSE, TRUE, NA) x3 <- c("yes", "no", "yes", "maybe") x4 <- factor(c("yes", "no", "yes", "maybe")) x5 <- c(1, 0, 0, 1, 0, NA, 2) inspect_data_categorical(x1) inspect_data_categorical(x2) inspect_data_categorical(x3) inspect_data_categorical(x4) inspect_data_categorical(x5) inspect_data_categorical(x5) # Call that throws an informative warning message: y1 <- c(1, 1, NA, 0, 0, 2) try(inspect_data_categorical(y1, warning_nas = TRUE)) # Calls that throw an informative error message: z <- c(1, 1, NA, 0, 0, 2) try(inspect_data_categorical(z, allow_nas = FALSE)) try(inspect_data_categorical(NULL)) try(inspect_data_categorical(list(1, 0))) try(inspect_data_categorical(numeric(0))) try(inspect_data_categorical(NaN)) try(inspect_data_categorical(NA))# Calls that pass silently: x1 <- c(1, 0, 0, 1, 2) x2 <- c(FALSE, FALSE, TRUE, NA) x3 <- c("yes", "no", "yes", "maybe") x4 <- factor(c("yes", "no", "yes", "maybe")) x5 <- c(1, 0, 0, 1, 0, NA, 2) inspect_data_categorical(x1) inspect_data_categorical(x2) inspect_data_categorical(x3) inspect_data_categorical(x4) inspect_data_categorical(x5) inspect_data_categorical(x5) # Call that throws an informative warning message: y1 <- c(1, 1, NA, 0, 0, 2) try(inspect_data_categorical(y1, warning_nas = TRUE)) # Calls that throw an informative error message: z <- c(1, 1, NA, 0, 0, 2) try(inspect_data_categorical(z, allow_nas = FALSE)) try(inspect_data_categorical(NULL)) try(inspect_data_categorical(list(1, 0))) try(inspect_data_categorical(numeric(0))) try(inspect_data_categorical(NaN)) try(inspect_data_categorical(NA))
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.
inspect_data_dichotomous(data, success, allow_nas = TRUE, warning_nas = FALSE)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.
# 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))# 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))
inspect_log_base checks if an object is a valid a logarithmic
base. This can be useful to validate inputs in user-defined functions.
inspect_log_base(x)inspect_log_base(x)
x |
An arbitrary object. |
inspect_log_base conducts a series of tests to check if x is a
valid logarithmic base. Namely, inspect_log_base checks if:
x is NULL or empty.
x is an atomic vector of length 1.
x is numeric.
x is NA or NaN.
x is positive.
inspect_log_base does not return any output. There are two possible
outcomes:
The call is silent if x is a numeric vector of length
1 that is a valid logarithmic base.
An informative error message is thrown otherwise.
bfactor_log_interpret for the interpretation of the
logarithms of Bayes factors.
inspect_bfactor_log to check if an object is a
numeric vector of valid logarithmic Bayes factor values.
# Calls that pass silently: x1 <- 10 x2 <- exp(1) x3 <- 0.5 inspect_log_base(x1) inspect_log_base(x2) inspect_log_base(x3) # Calls that throw informative error messages: mylist <- list( NULL, numeric(0), TRUE, factor(10), list(10), matrix(10), NaN, NA, -1, 0 ) try(inspect_log_base(mylist[[1]])) try(inspect_log_base(mylist[[2]])) try(inspect_log_base(mylist[[3]])) try(inspect_log_base(mylist[[4]])) try(inspect_log_base(mylist[[5]])) try(inspect_log_base(mylist[[6]])) try(inspect_log_base(mylist[[7]])) try(inspect_log_base(mylist[[8]])) try(inspect_log_base(mylist[[9]])) try(inspect_log_base(mylist[[10]]))# Calls that pass silently: x1 <- 10 x2 <- exp(1) x3 <- 0.5 inspect_log_base(x1) inspect_log_base(x2) inspect_log_base(x3) # Calls that throw informative error messages: mylist <- list( NULL, numeric(0), TRUE, factor(10), list(10), matrix(10), NaN, NA, -1, 0 ) try(inspect_log_base(mylist[[1]])) try(inspect_log_base(mylist[[2]])) try(inspect_log_base(mylist[[3]])) try(inspect_log_base(mylist[[4]])) try(inspect_log_base(mylist[[5]])) try(inspect_log_base(mylist[[6]])) try(inspect_log_base(mylist[[7]])) try(inspect_log_base(mylist[[8]])) try(inspect_log_base(mylist[[9]])) try(inspect_log_base(mylist[[10]]))
inspect_par_bernoulli checks if an object is an eligible
Bernoulli/Binomial proportion. This can be useful to validate inputs,
intermediate calculations or outputs in user-defined functions.
inspect_par_bernoulli(x)inspect_par_bernoulli(x)
x |
An arbitrary object. |
inspect_par_bernoulli conducts a series of tests to check if x
is an eligible Bernoulli/Binomial proportion. Namely, inspect_par_bernoulli
checks if:
x is NULL or empty.
x is an atomic vector
x is numeric
x has length 1
x is NA or NaN.
x is in the (0, 1) interval.
inspect_par_bernoulli does not return any output. There are two
possible outcomes:
The call is silent if x is an eligible Bernoulli/Binomial proportion.
An informative error message is thrown otherwise.
inspect_par_multinomial to validate parameters for
the Multinomial distribution.
inspect_par_beta to validate parameters for the
Beta distribution.
inspect_par_dirichlet to validate parameters for
the Dirichlet distribution.
inspect_par_haldane to validate parameters for the
Haldane distribution.
inspect_data_dichotomous to validate dichotomous
data.
inspect_prob to check if an object is a numeric
vector of valid probability values.
# Calls that pass silently: x <- 0.5 inspect_par_bernoulli(x) inspect_par_bernoulli(0.1) # Calls that throw an informative error message: mylist <- list( NULL, TRUE, factor(.5), matrix(0.5), "0.5", list(0.5), NA, NaN, numeric(0), c(0.1, 0.5), -0.5, 1.1 ) try(inspect_par_bernoulli(mylist[[1]])) try(inspect_par_bernoulli(mylist[[2]])) try(inspect_par_bernoulli(mylist[[3]])) try(inspect_par_bernoulli(mylist[[4]])) try(inspect_par_bernoulli(mylist[[5]])) try(inspect_par_bernoulli(mylist[[6]])) try(inspect_par_bernoulli(mylist[[7]])) try(inspect_par_bernoulli(mylist[[8]])) try(inspect_par_bernoulli(mylist[[9]])) try(inspect_par_bernoulli(mylist[[10]])) try(inspect_par_bernoulli(mylist[[11]])) try(inspect_par_bernoulli(mylist[[12]]))# Calls that pass silently: x <- 0.5 inspect_par_bernoulli(x) inspect_par_bernoulli(0.1) # Calls that throw an informative error message: mylist <- list( NULL, TRUE, factor(.5), matrix(0.5), "0.5", list(0.5), NA, NaN, numeric(0), c(0.1, 0.5), -0.5, 1.1 ) try(inspect_par_bernoulli(mylist[[1]])) try(inspect_par_bernoulli(mylist[[2]])) try(inspect_par_bernoulli(mylist[[3]])) try(inspect_par_bernoulli(mylist[[4]])) try(inspect_par_bernoulli(mylist[[5]])) try(inspect_par_bernoulli(mylist[[6]])) try(inspect_par_bernoulli(mylist[[7]])) try(inspect_par_bernoulli(mylist[[8]])) try(inspect_par_bernoulli(mylist[[9]])) try(inspect_par_bernoulli(mylist[[10]])) try(inspect_par_bernoulli(mylist[[11]])) try(inspect_par_bernoulli(mylist[[12]]))
inspect_par_beta checks if an object is an eligible vector of
parameters for the Beta distribution. This can be useful to validate inputs,
intermediate calculations or outputs in user-defined functions.
inspect_par_beta(x)inspect_par_beta(x)
x |
An arbitrary object. |
inspect_par_beta conducts a series of tests to check if x is an
eligible vector of parameters for the Beta distribution. Namely,
inspect_par_beta checks if:
x is NULL or empty.
x is an atomic vector
x is numeric
x has length 2
x has NA or NaN values.
All elements of x are positive.
inspect_par_beta does not return any output. There are two possible
outcomes:
The call is silent if x is an eligible vector of parameters for the Beta
distribution.
An informative error message is thrown otherwise.
inspect_par_bernoulli to validate parameters for
the Bernoulli/Binomial distribution.
inspect_par_multinomial to validate parameters for
the Multinomial distribution.
inspect_par_dirichlet to validate parameters for
the Dirichlet distribution.
inspect_par_haldane to validate parameters for the
Haldane distribution.
# Calls that pass silently: x1 <- c(1, 1) x2 <- c(2, 5) inspect_par_beta(x1) inspect_par_beta(x2) # Calls that throw an informative error message: mylist <- list( NULL, 1, factor(1, 1), matrix(c(1, 1)), c("1", "1"), list(1, 1), c(1, NA), c(1, NaN), c(TRUE, FALSE), numeric(0), c(-1, 1) ) try(inspect_par_beta(mylist[[1]])) try(inspect_par_beta(mylist[[2]])) try(inspect_par_beta(mylist[[3]])) try(inspect_par_beta(mylist[[4]])) try(inspect_par_beta(mylist[[5]])) try(inspect_par_beta(mylist[[6]])) try(inspect_par_beta(mylist[[7]])) try(inspect_par_beta(mylist[[8]])) try(inspect_par_beta(mylist[[9]])) try(inspect_par_beta(mylist[[10]])) try(inspect_par_beta(mylist[[11]]))# Calls that pass silently: x1 <- c(1, 1) x2 <- c(2, 5) inspect_par_beta(x1) inspect_par_beta(x2) # Calls that throw an informative error message: mylist <- list( NULL, 1, factor(1, 1), matrix(c(1, 1)), c("1", "1"), list(1, 1), c(1, NA), c(1, NaN), c(TRUE, FALSE), numeric(0), c(-1, 1) ) try(inspect_par_beta(mylist[[1]])) try(inspect_par_beta(mylist[[2]])) try(inspect_par_beta(mylist[[3]])) try(inspect_par_beta(mylist[[4]])) try(inspect_par_beta(mylist[[5]])) try(inspect_par_beta(mylist[[6]])) try(inspect_par_beta(mylist[[7]])) try(inspect_par_beta(mylist[[8]])) try(inspect_par_beta(mylist[[9]])) try(inspect_par_beta(mylist[[10]])) try(inspect_par_beta(mylist[[11]]))
inspect_par_dirichlet checks if an object is an eligible
vector of parameters for the Dirichlet distribution. This can be useful to
validate inputs, intermediate calculations or outputs in user-defined
functions.
inspect_par_dirichlet(x)inspect_par_dirichlet(x)
x |
An arbitrary object. |
inspect_par_dirichlet conducts a series of tests to check if x
is an eligible vector of parameters for the Dirichlet distribution. Namely,
inspect_par_dirichlet checks if:
x is NULL or empty.
x is an atomic vector
x is numeric
x has NA or NaN values.
All elements of x are positive.
inspect_par_dirichlet does not return any output. There are two
possible outcomes:
The call is silent if x is an eligible vector of parameters for the
Dirichlet distribution.
An informative error message is thrown otherwise.
inspect_par_bernoulli to validate parameters for
the Bernoulli/Binomial distribution.
inspect_par_multinomial to validate parameters for
the Multinomial distribution.
inspect_par_beta to validate parameters for the
Beta distribution.
inspect_par_haldane to validate parameters for the
Haldane distribution.
# Calls that pass silently: x1 <- c(1, 1, 1) x2 <- c(2, 5) inspect_par_dirichlet(x1) inspect_par_dirichlet(x2) # Calls that throw an informative error message: mylist <- list( NULL, factor(1, 1, 1), matrix(c(1, 1, 1)), c("1", "1", "1"), list(1, 1, 1), c(1, NA), c(1, NaN, 1), c(TRUE, FALSE), numeric(0), c(-1, 1, 1) ) try(inspect_par_dirichlet(mylist[[1]])) try(inspect_par_dirichlet(mylist[[2]])) try(inspect_par_dirichlet(mylist[[3]])) try(inspect_par_dirichlet(mylist[[4]])) try(inspect_par_dirichlet(mylist[[5]])) try(inspect_par_dirichlet(mylist[[6]])) try(inspect_par_dirichlet(mylist[[7]])) try(inspect_par_dirichlet(mylist[[8]])) try(inspect_par_dirichlet(mylist[[9]])) try(inspect_par_dirichlet(mylist[[10]]))# Calls that pass silently: x1 <- c(1, 1, 1) x2 <- c(2, 5) inspect_par_dirichlet(x1) inspect_par_dirichlet(x2) # Calls that throw an informative error message: mylist <- list( NULL, factor(1, 1, 1), matrix(c(1, 1, 1)), c("1", "1", "1"), list(1, 1, 1), c(1, NA), c(1, NaN, 1), c(TRUE, FALSE), numeric(0), c(-1, 1, 1) ) try(inspect_par_dirichlet(mylist[[1]])) try(inspect_par_dirichlet(mylist[[2]])) try(inspect_par_dirichlet(mylist[[3]])) try(inspect_par_dirichlet(mylist[[4]])) try(inspect_par_dirichlet(mylist[[5]])) try(inspect_par_dirichlet(mylist[[6]])) try(inspect_par_dirichlet(mylist[[7]])) try(inspect_par_dirichlet(mylist[[8]])) try(inspect_par_dirichlet(mylist[[9]])) try(inspect_par_dirichlet(mylist[[10]]))
inspect_par_haldane checks if an object is an eligible vector
of parameters for the Haldane distribution. This can be useful to validate
inputs, intermediate calculations or outputs in user-defined functions.
inspect_par_haldane(x)inspect_par_haldane(x)
x |
An arbitrary object. |
inspect_par_haldane conducts a series of tests to check if x is
an eligible vector of parameters for the Haldane distribution. Namely,
inspect_par_haldane checks if:
x is NULL or empty.
x is an atomic vector
x is numeric
x has NA or NaN values.
All elements of x equal to 0.
inspect_par_haldane does not return any output. There are two
possible outcomes:
The call is silent if x is an eligible vector of parameters for the
Haldane distribution.
An informative error message is thrown otherwise.
inspect_par_bernoulli to validate parameters for
the Bernoulli/Binomial distribution.
inspect_par_multinomial to validate parameters for
the Multinomial distribution.
inspect_par_beta to validate parameters for the
Beta distribution.
inspect_par_dirichlet to validate parameters for
the Dirichlet distribution.
# Calls that pass silently: x1 <- c(0, 0, 0) x2 <- c(0, 0) inspect_par_haldane(x1) inspect_par_haldane(x2) # Calls that throw an informative error message: mylist <- list( NULL, factor(0, 0, 0), matrix(c(0, 0, 0)), c("0", "0", "0"), list(0, 0, 0), c(0, NA), c(0, NaN, 0), c(TRUE, FALSE), numeric(0), c(1, 0, 0) ) try(inspect_par_haldane(mylist[[1]])) try(inspect_par_haldane(mylist[[2]])) try(inspect_par_haldane(mylist[[3]])) try(inspect_par_haldane(mylist[[4]])) try(inspect_par_haldane(mylist[[5]])) try(inspect_par_haldane(mylist[[6]])) try(inspect_par_haldane(mylist[[7]])) try(inspect_par_haldane(mylist[[8]])) try(inspect_par_haldane(mylist[[9]])) try(inspect_par_haldane(mylist[[10]]))# Calls that pass silently: x1 <- c(0, 0, 0) x2 <- c(0, 0) inspect_par_haldane(x1) inspect_par_haldane(x2) # Calls that throw an informative error message: mylist <- list( NULL, factor(0, 0, 0), matrix(c(0, 0, 0)), c("0", "0", "0"), list(0, 0, 0), c(0, NA), c(0, NaN, 0), c(TRUE, FALSE), numeric(0), c(1, 0, 0) ) try(inspect_par_haldane(mylist[[1]])) try(inspect_par_haldane(mylist[[2]])) try(inspect_par_haldane(mylist[[3]])) try(inspect_par_haldane(mylist[[4]])) try(inspect_par_haldane(mylist[[5]])) try(inspect_par_haldane(mylist[[6]])) try(inspect_par_haldane(mylist[[7]])) try(inspect_par_haldane(mylist[[8]])) try(inspect_par_haldane(mylist[[9]])) try(inspect_par_haldane(mylist[[10]]))
inspect_par_multinomial checks if an object is an eligible
vector of Multinomial proportions. This can be useful to validate inputs,
intermediate calculations or outputs in user-defined functions.
inspect_par_multinomial(x)inspect_par_multinomial(x)
x |
An arbitrary object. |
inspect_par_multinomial conducts a series of tests to check if
x is an eligible vector of Multinomial proportions. Namely,
inspect_par_multinomial checks if:
x is NULL or empty.
x is an atomic vector
x is numeric
x has NA or NaN values.
All elements of x are in the (0, 1) interval.
x sums to 1.
inspect_par_multinomial does not return any output.
There are two possible outcomes:
The call is silent if x is an eligible vector of Multinomial proportions.
An informative error message is thrown otherwise.
inspect_par_bernoulli to validate parameters for
the Bernoulli/Binomial distribution.
inspect_par_beta to validate parameters for the
Beta distribution.
inspect_par_dirichlet to validate parameters for
the Dirichlet distribution.
inspect_par_haldane to validate parameters for the
Haldane distribution.
inspect_data_categorical and
inspect_data_cat_as_dichotom to validate categorical
data.
inspect_prob to check if an object is a numeric
vector of valid probability values.
# Calls that pass silently: x1 <- c(0.5, 0.5) x2 <- rep(1 / 5, 5) inspect_par_multinomial(x1) inspect_par_multinomial(x2) # Calls that throw an informative error message: mylist <- list( NULL, TRUE, factor(0.5, 0.5), matrix(c(0.5, 0.5)), c("0.5", "0.5"), list(0.5, 0.5), c(0.9, NA), c(0.9, NaN), numeric(0), NA, c(0.9, 0.6), c(-0.1, 0.9) ) try(inspect_par_multinomial(mylist[[1]])) try(inspect_par_multinomial(mylist[[2]])) try(inspect_par_multinomial(mylist[[3]])) try(inspect_par_multinomial(mylist[[4]])) try(inspect_par_multinomial(mylist[[5]])) try(inspect_par_multinomial(mylist[[6]])) try(inspect_par_multinomial(mylist[[7]])) try(inspect_par_multinomial(mylist[[8]])) try(inspect_par_multinomial(mylist[[9]])) try(inspect_par_multinomial(mylist[[10]])) try(inspect_par_multinomial(mylist[[11]])) try(inspect_par_multinomial(mylist[[12]]))# Calls that pass silently: x1 <- c(0.5, 0.5) x2 <- rep(1 / 5, 5) inspect_par_multinomial(x1) inspect_par_multinomial(x2) # Calls that throw an informative error message: mylist <- list( NULL, TRUE, factor(0.5, 0.5), matrix(c(0.5, 0.5)), c("0.5", "0.5"), list(0.5, 0.5), c(0.9, NA), c(0.9, NaN), numeric(0), NA, c(0.9, 0.6), c(-0.1, 0.9) ) try(inspect_par_multinomial(mylist[[1]])) try(inspect_par_multinomial(mylist[[2]])) try(inspect_par_multinomial(mylist[[3]])) try(inspect_par_multinomial(mylist[[4]])) try(inspect_par_multinomial(mylist[[5]])) try(inspect_par_multinomial(mylist[[6]])) try(inspect_par_multinomial(mylist[[7]])) try(inspect_par_multinomial(mylist[[8]])) try(inspect_par_multinomial(mylist[[9]])) try(inspect_par_multinomial(mylist[[10]])) try(inspect_par_multinomial(mylist[[11]])) try(inspect_par_multinomial(mylist[[12]]))
inspect_prob checks if an object is a numeric vector of valid
probability values. This can be useful to validate inputs, intermediate
calculations or outputs in user-defined functions.
inspect_prob(x, allow_nas = TRUE, warning_nas = TRUE)inspect_prob(x, allow_nas = TRUE, warning_nas = TRUE)
x |
An arbitrary object. |
allow_nas |
Logical value. If |
warning_nas |
Logical value. If |
inspect_prob conducts a series of tests to check if x is a
numeric vector of valid probability values. Namely, inspect_prob 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 in the [0, 1] interval.
inspect_prob does not return any output. There are three possible
outcomes:
The call is silent if:
x is a numeric vector of valid probability values and there are no NA
or NaN values in x.
x is a numeric vector of valid probability 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 thrown if x is a numeric vector of
valid probability 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 probability values.
x is a numeric vector of valid probability values, there are some NA
or NaN values in x and allow_nas is set to FALSE.
inspect_par_bernoulli to check if an object is a
valid Bernoulli/Binomial proportion.
inspect_par_multinomial to check if an object is
a numeric vector of valid Multinomial proportions.
# Calls that pass silently: x1 <- c(0.1, 0.2, 0.3, 0.4, 0.5) x2 <- c(0.1, 0.2, 0.3, 0.4, 0.5, NA) inspect_prob(x1) inspect_prob(x2, warning_nas = FALSE) inspect_prob(x2, allow_nas = TRUE, warning_nas = FALSE) # Calls that throw an informative warning message: y <- c(0.1, 0.2, NA, 0.4, 0.5) try(inspect_prob(y)) try(inspect_prob(y, allow_nas = TRUE)) try(inspect_prob(y, allow_nas = TRUE, warning_nas = TRUE)) # Calls that throw an informative error message: z1 <- c(-0.9, 0, 0.1, 0.2, 0.3, 0.4, 0.5) try(inspect_prob(z1)) z2 <- c(NA, 0, 0.1, 0.2, 0.3, 0.4, 0.5) try(inspect_prob(z2, allow_nas = FALSE)) mylist <- list( NULL, TRUE, factor(.5), matrix(0.5), "0.5", list(0.5), NA, NaN, numeric(0), 1.1, -0.5 ) try(inspect_prob(mylist[[1]])) try(inspect_prob(mylist[[2]])) try(inspect_prob(mylist[[3]])) try(inspect_prob(mylist[[4]])) try(inspect_prob(mylist[[5]])) try(inspect_prob(mylist[[6]])) try(inspect_prob(mylist[[7]])) try(inspect_prob(mylist[[8]])) try(inspect_prob(mylist[[9]])) try(inspect_prob(mylist[[10]])) try(inspect_prob(mylist[[11]]))# Calls that pass silently: x1 <- c(0.1, 0.2, 0.3, 0.4, 0.5) x2 <- c(0.1, 0.2, 0.3, 0.4, 0.5, NA) inspect_prob(x1) inspect_prob(x2, warning_nas = FALSE) inspect_prob(x2, allow_nas = TRUE, warning_nas = FALSE) # Calls that throw an informative warning message: y <- c(0.1, 0.2, NA, 0.4, 0.5) try(inspect_prob(y)) try(inspect_prob(y, allow_nas = TRUE)) try(inspect_prob(y, allow_nas = TRUE, warning_nas = TRUE)) # Calls that throw an informative error message: z1 <- c(-0.9, 0, 0.1, 0.2, 0.3, 0.4, 0.5) try(inspect_prob(z1)) z2 <- c(NA, 0, 0.1, 0.2, 0.3, 0.4, 0.5) try(inspect_prob(z2, allow_nas = FALSE)) mylist <- list( NULL, TRUE, factor(.5), matrix(0.5), "0.5", list(0.5), NA, NaN, numeric(0), 1.1, -0.5 ) try(inspect_prob(mylist[[1]])) try(inspect_prob(mylist[[2]])) try(inspect_prob(mylist[[3]])) try(inspect_prob(mylist[[4]])) try(inspect_prob(mylist[[5]])) try(inspect_prob(mylist[[6]])) try(inspect_prob(mylist[[7]])) try(inspect_prob(mylist[[8]])) try(inspect_prob(mylist[[9]])) try(inspect_prob(mylist[[10]])) try(inspect_prob(mylist[[11]]))
inspect_true_or_false checks if an object is a non-missing
logical vector of length 1. This can be useful to
validate inputs in user-defined functions.
inspect_true_or_false(x)inspect_true_or_false(x)
x |
An arbitrary object. |
inspect_true_or_false conducts a series of tests to check if x
is a non-missing logical vector of length 1. Namely,
inspect_true_or_false checks if:
inspect_true_or_false does not return any output. There are two
possible scenarios:
The call is silent if x is a non-missing logical vector of
length 1.
An informative error message is thrown otherwise.
inspect_character to validate character vectors.
inspect_character_match to validate character
vectors with predefined allowed values.
# Calls that pass silently: x <- TRUE y <- FALSE inspect_true_or_false(x) inspect_true_or_false(y) # Calls that throw informative error messages: mylist <- list(NULL, NA, NaN, 1, 0, "TRUE") try(inspect_true_or_false(mylist[[1]])) try(inspect_true_or_false(mylist[[2]])) try(inspect_true_or_false(mylist[[3]])) try(inspect_true_or_false(mylist[[4]])) try(inspect_true_or_false(mylist[[5]])) try(inspect_true_or_false(mylist[[6]]))# Calls that pass silently: x <- TRUE y <- FALSE inspect_true_or_false(x) inspect_true_or_false(y) # Calls that throw informative error messages: mylist <- list(NULL, NA, NaN, 1, 0, "TRUE") try(inspect_true_or_false(mylist[[1]])) try(inspect_true_or_false(mylist[[2]])) try(inspect_true_or_false(mylist[[3]])) try(inspect_true_or_false(mylist[[4]])) try(inspect_true_or_false(mylist[[5]])) try(inspect_true_or_false(mylist[[6]]))