Internal functions, not to be called by user
validAbparms(object) validBound(object) validBoundEst(object) validBoundNBF(object)abBindBothCalcK(object) abtoBound(from)pCalc(S,N,K,order,theta0=.5,alternative="two.sided",ponly=FALSE) ciCalc(S,N,K,order,type="upper",alpha=0.025)missNAbparms(ab,missN=NULL,...)
- object, usually a boundary of some class
- an object of class abparms
- vector of number of successes
- vector of number of trials
- vector of number of ways to reach each bounary point
- vector of ordering of boundary points
- null value of probability of success each binary random variable
- character, either 'two.sided', 'less', or 'greater'
- logical, should only the specific p-value type given by alternative be calculated
- character, type of one-sided confidence interval to calculate, either 'upper' or 'lower'
- numeric, amount of error to allow on the one side of the confidence interval
- object of class 'abparms'
- numeric vector, the N values where assessments are missed
- arguments passed to other functions, not used
The validXX functions check that the object is a valid member of the class XX. For example, validBound checks that a bound object is OK by sum the probability distribution using the N,S, and K values and checking that it is within computer error of 1. The validity checks are run automatically by the new() function as part of the S4 implementation.
abBindBothCalcK takes an abparms object and creates a bound object. It requires calculating K, which is the number of ways
to reach each boundary point. It ignores the
binding argument and assumes all boundaries are binding. The
binding argument to create either a
bound object (for
binding='both') or a
boundNBF object otherwise.
Users can use the
as function to coerce an
abparms object to a
pCalc takes a boundary and calculates p-values, and outputs a vector of p-values (ponly=TRUE) or list of 3 vectors (plower,pupper, pval).
cCalc takes a boundary and calculates one of the one sided confidence intervals as directed by the type argument (either 'upper' or 'lower').
analyzeBoundNBF take objects of the
boundNBF classes and create ones of the
boundNBFEst classes. This means basically that the confidence intervals and p-values
are calculated that go with those bounds.
getTSalpha get those parameters from the inputs.
missNAbparms modifies abparms objects to reflect missing assessments. This is the working function for the missN option in