binseqtest-internal {binseqtest} | R Documentation |
Internal functions
Description
Internal functions, not to be called by user
Usage
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,...)
Arguments
object |
object, usually a boundary of some class |
from |
an object of class abparms |
S |
vector of number of successes |
N |
vector of number of trials |
K |
vector of number of ways to reach each bounary point |
order |
vector of ordering of boundary points |
theta0 |
null value of probability of success each binary random variable |
alternative |
character, either 'two.sided', 'less', or 'greater' |
ponly |
logical, should only the specific p-value type given by alternative be calculated |
type |
character, type of one-sided confidence interval to calculate, either 'upper' or 'lower' |
alpha |
numeric, amount of error to allow on the one side of the confidence interval |
ab |
object of class 'abparms' |
missN |
numeric vector, the N values where assessments are missed |
... |
arguments passed to other functions, not used |
Details
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
.