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.
The function 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 abtoBound function
uses 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 bound object.
The function 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).
The function cCalc takes a boundary and calculates one of the one sided confidence intervals as directed by the type argument (either 'upper' or 'lower').
The functions analyzeBound and analyzeBoundNBF take objects of the
bound and boundNBF classes and create ones of the boundEst and
boundNBFEst classes. This means basically that the confidence intervals and p-values
are calculated that go with those bounds.
The functions getAlternative and getTSalpha get those parameters from the inputs.
The function missNAbparms modifies abparms objects to reflect missing assessments. This is the working function for the missN option in modify.