pub fn ratio_events_greater_pval(
            num_events_one_baseline:usize,
            num_events_two_baseline:usize,
            num_baseline_group:usize,
            num_events_one_treatment:usize,
            num_events_two_treatment:usize,
            num_treatment_group:usize,
        ) -> Result<f64, &'static str>
        {
            return  ratio_events_greater_pval_n(num_events_one_baseline,
            num_events_two_baseline, num_baseline_group,
            num_events_one_treatment, num_events_two_treatment,
            num_treatment_group, 1000);
        }
/// Hypothesis test R1/R2 under condition 1 > R1/R2 without constraint 
/// and return the p-value.  where R1 = #events of type 1 / #trials and
/// R2 = #events of type 2 / #trials 
/// 
/// We assume two populations, one of which had some difference, and
/// we're trying to see if it affected the ratio of the occurances of
/// these two events.
/// 
/// We're using boostrap methods (computationally more expensive, but
/// not bad)
/// 
/// One caveat: If # events_1 and # events_2 under treatment are both 0
/// (neither events occurred under treatment), that is considered a
/// viable event and is compared to the occurances of 0/0 in baseline
/// samples.
/// 
/// 
/// *Example*
/// ```rust
/// use claim::{assert_lt,assert_gt};
/// let base_a = vec![1,1,1,1, 1,1,2,1];
/// let base_b = vec![1,0,1,1, 0,1,1,1];
/// let treat_a = vec![1,1,1,1, 1,1,2,1];
/// let treat_b = vec![1,0,0,0, 0,0,0,0];
/// //Did treatment increase ratio of a/b?
/// let p = poisson_rate_test::bootstrap::param::ratio_events_greater_pval(
///     base_a.iter().sum::<usize>(),
///     base_b.iter().sum::<usize>(),
///     base_a.len() as usize,
///     treat_a.iter().sum::<usize>(),
///     treat_b.iter().sum::<usize>(),
///     treat_a.len() as usize,
/// );
/// assert_lt!(p.unwrap(),0.05); //<--confidently yes
/// assert_gt!(p.unwrap(),0.001);
/// ```
Hypothesis test R1/R2 under condition 1 > R1/R2 without constraint
and return the p-value.  where R1 = #events of type 1 / #trials and
R2 = #events of type 2 / #trials

We assume two populations, one of which had some difference, and
we're trying to see if it affected the ratio of the occurances of
these two events.

We're using boostrap methods (computationally more expensive, but
not bad)

One caveat: If # events_1 and # events_2 under treatment are both 0
(neither events occurred under treatment), that is considered a
viable event and is compared to the occurances of 0/0 in baseline
samples.


Hypothesis test R1 / R2 under condition 1 > R1 / R2 without constraint and return the p - value . where R1 = #events of type 1 / #trials and R2 = #events of type 2 / #trials We assume two populations one of which had some difference and we ' re trying to see if it affected the ratio of the occurances of these two events . We ' re using boostrap methods ( computationally more expensive but not bad ) One caveat : If # events_1 and # events_2 under treatment are both 0 ( neither events occurred under treatment ) that is considered a viable event and is compared to the occurances of 0 / 0 in baseline samples .
pub fn ratio_events_greater_pval(
            num_events_one_baseline:usize,
            num_events_two_baseline:usize,
            num_baseline_group:usize,
            num_events_one_treatment:usize,
            num_events_two_treatment:usize,
            num_treatment_group:usize,
        ) -> Result<f64, &'static str>
        {
            return  ratio_events_greater_pval_n(num_events_one_baseline,
            num_events_two_baseline, num_baseline_group,
            num_events_one_treatment, num_events_two_treatment,
            num_treatment_group, 1000);
        }
pub fn ratio_events_greater_pval ( num_events_one_baseline : usize , num_events_two_baseline : usize , num_baseline_group : usize , num_events_one_treatment : usize , num_events_two_treatment : usize , num_treatment_group : usize , ) -> Result < f64 , & ' static str > { return ratio_events_greater_pval_n ( num_events_one_baseline , num_events_two_baseline , num_baseline_group , num_events_one_treatment , num_events_two_treatment , num_treatment_group , 1000 ) ; }
Hypothesis test R1/R2 under condition 1 > R1/R2 without constraint
and return the p-value.
Hypothesis test R1 / R2 under condition 1 > R1 / R2 without constraint and return the p - value .
num_events_one_baseline
usize
num_events_two_baseline
usize
num_baseline_group
usize
num_events_one_treatment
usize
num_events_two_treatment
usize
num_treatment_group
usize
num_events_one_baseline
usize
num_events_two_baseline
usize
num_baseline_group
usize
num_events_one_treatment
usize
num_events_two_treatment
usize
num_treatment_group
usize