# is_suff_prob_set: Verify a sufficient set of probability inputs. In riskyr: Rendering Risk Literacy more Transparent

## Description

`is_suff_prob_set` is a function that takes 3 to 5 probabilities as inputs and verifies that they are sufficient to compute all derived probabilities and combined frequencies for a population of `N` individuals.

## Usage

 `1` ```is_suff_prob_set(prev, sens = NA, mirt = NA, spec = NA, fart = NA) ```

## Arguments

 `prev` The condition's prevalence `prev` (i.e., the probability of condition being `TRUE`). `sens` The decision's sensitivity `sens` (i.e., the conditional probability of a positive decision provided that the condition is `TRUE`). `sens` is optional when its complement `mirt` is provided. `mirt` The decision's miss rate `mirt` (i.e., the conditional probability of a negative decision provided that the condition is `TRUE`). `mirt` is optional when its complement `sens` is provided. `spec` The decision's specificity value `spec` (i.e., the conditional probability of a negative decision provided that the condition is `FALSE`). `spec` is optional when its complement `fart` is provided. `fart` The decision's false alarm rate `fart` (i.e., the conditional probability of a positive decision provided that the condition is `FALSE`). `fart` is optional when its complement `spec` is provided.

## Details

While no alternative input option for frequencies is provided, specification of the essential probability `prev` is always necessary.

However, for two other essential probabilities there is a choice:

1. Either `sens` or `mirt` is necessary (as both are complements).

2. Either `spec` or `fart` is necessary (as both are complements).

`is_suff_prob_set` does not verify the type, range, or consistency of its arguments. See `is_prob` and `is_complement` for this purpose.

## Value

A Boolean value: `TRUE` if the probabilities provided are sufficient, otherwise `FALSE`.

`num` contains basic numeric variables; `init_num` initializes basic numeric variables; `prob` contains current probability information; `comp_prob` computes current probability information; `freq` contains current frequency information; `comp_freq` computes current frequency information; `is_valid_prob_set` verifies the validity of probability inputs; `as_pc` displays a probability as a percentage; `as_pb` displays a percentage as probability.
Other verification functions: `is_complement`, `is_extreme_prob_set`, `is_freq`, `is_perc`, `is_prob`, `is_valid_prob_pair`, `is_valid_prob_set`, `is_valid_prob_triple`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# ways to work: is_suff_prob_set(prev = 1, sens = 1, spec = 1) # => TRUE is_suff_prob_set(prev = 1, mirt = 1, spec = 1) # => TRUE is_suff_prob_set(prev = 1, sens = 1, fart = 1) # => TRUE is_suff_prob_set(prev = 1, mirt = 1, fart = 1) # => TRUE # watch out for: is_suff_prob_set(prev = 1, sens = 2, spec = 3) # => TRUE, but is_prob is FALSE is_suff_prob_set(prev = 1, mirt = 2, fart = 4) # => TRUE, but is_prob is FALSE is_suff_prob_set(prev = 1, sens = 2, spec = 3, fart = 4) # => TRUE, but is_prob is FALSE ## ways to fail: # is_suff_prob_set() # => FALSE + warning (prev missing) # is_suff_prob_set(prev = 1) # => FALSE + warning (sens or mirt missing) # is_suff_prob_set(prev = 1, sens = 1) # => FALSE + warning (spec or fart missing) ```