tern (version 0.9.12)

assertions: Additional assertions to use with checkmate

Description

Additional assertion functions which can be used together with the checkmate package.

Usage

assert_list_of_variables(x, .var.name = checkmate::vname(x), add = NULL)
assert_df_with_variables(
  df,
  variables,
  na_level = NULL,
  .var.name = checkmate::vname(df),
  add = NULL
)
assert_valid_factor(
  x,
  min.levels = 1,
  max.levels = NULL,
  null.ok = TRUE,
  any.missing = TRUE,
  n.levels = NULL,
  len = NULL,
  .var.name = checkmate::vname(x),
  add = NULL
)
assert_df_with_factors(
  df,
  variables,
  min.levels = 1,
  max.levels = NULL,
  any.missing = TRUE,
  na_level = NULL,
  .var.name = checkmate::vname(df),
  add = NULL
)
assert_proportion_value(x, include_boundaries = FALSE)
assert_proportion_data(rsp, grp, strata = NULL)
assert_stratification_compatibility(method, stratified_methods, strata_vars)

Value

Nothing if assertion passes, otherwise prints the error message.

Arguments

x

(any)
object to test.

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

df

(data.frame)
data set to test.

variables

(named list of character)
list of variables to test.

na_level

(string)
the string you have been using to represent NA or missing data. For NA values please consider using directly is.na() or similar approaches.

min.levels

[integer(1)]
Minimum number of factor levels. Default is NULL (no check).

max.levels

[integer(1)]
Maximum number of factor levels. Default is NULL (no check).

null.ok

[logical(1)]
If set to TRUE, x may also be NULL. In this case only a type check of x is performed, all additional checks are disabled.

any.missing

[logical(1)]
Are vectors with missing values allowed? Default is TRUE.

n.levels

[integer(1)]
Exact number of factor levels. Default is NULL (no check).

len

[integer(1)]
Exact expected length of x.

include_boundaries

(flag)
whether to include boundaries when testing for proportions.

rsp

(logical)
Indicates whether each observation is a responder (TRUE) or a non-responder (FALSE). Missing values are not allowed.

grp

(factor)
Assigns each observation to one of two groups, such as a reference and a treatment group. Must have exactly two levels and the same length as rsp. Missing values are not allowed.

strata

(factor or NULL)
Defines the stratification variable. If not NULL, it must have the same length as rsp and must not contain any missing values.

method

(character(1))
Specifies the statistical method.

stratified_methods

(character)
Names of the methods that require stratified data.

strata_vars

(character or NULL)
Names of the variables defining the strata, or NULL if no stratification is used.

Functions

• assert_list_of_variables(): Checks whether x is a valid list of variable names. NULL elements of the list x are dropped with Filter(Negate(is.null), x).

• assert_df_with_variables(): Check whether df is a data frame with the analysis variables. Please notice how this produces an error when not all variables are present in the data.frame while the opposite is not required.

• assert_valid_factor(): Check whether x is a valid factor (i.e. has levels and no empty string levels). Note that NULL and NA elements are allowed.

• assert_df_with_factors(): Check whether df is a data frame where the analysis variables are all factors. Note that the creation of NA by direct call of factor() will trim NA levels out of the vector list itself.

• assert_proportion_value(): Check whether x is a proportion: number between 0 and 1.

• assert_proportion_data(): Validates the data required for a proportion analysis, including responder status, group assignment, and optional stratification.

• assert_stratification_compatibility(): Assert compatibility between a method and stratification. Checks that the selected method is compatible with the presence or absence of stratification variables. Methods included in stratified_methods require at least one variable defining the strata, while unstratified methods must not use stratification variables.

Examples

x <- data.frame(
  a = 1:10,
  b = rnorm(10)
)
assert_df_with_variables(x, variables = list(a = "a", b = "b"))

x <- ex_adsl
assert_df_with_variables(x, list(a = "ARM", b = "USUBJID"))

x <- ex_adsl
assert_df_with_factors(x, list(a = "ARM"))

assert_proportion_value(0.95)
assert_proportion_value(1.0, include_boundaries = TRUE)

rsp <- c(TRUE, TRUE, FALSE, TRUE, FALSE, FALSE)
grp <- factor(c(rep("Placebo", 3), rep("X", 3)))
strata <- factor(c("A", "A", "B", "A", "B", "B"))

assert_proportion_data(rsp, grp, strata)

if (FALSE) {
# An error is raised when `grp` has only one level.
grp <- factor(rep("X", 6))
assert_proportion_data(rsp, grp, strata)
}