coverage {binomSamSize} | R Documentation |
For a given true value of the proportion compute the coverage probability of the confidence interval
coverage(ci.fun, n, alpha=0.05, p.grid=NULL,interval=c(0,1), pmfX=function(k,n,p) dbinom(k,size=n,p=p), ...) ## S3 method for class 'coverage': plot(x, y=NULL, ...)
ci.fun |
binom.confint like function which
computes confidence intervals for a binomial proportion. |
n |
Sample size of the binomial distribution. |
alpha |
Level of significance, 1-α is the confidence level. |
p.grid |
Vector of proportions where to evaluate the confidence
interval function. If NULL all those values where the minimum
coverage probability can occur is taken. If not NULL then the
union between p.grid and these values is taken. |
interval |
Vector of length two specifying lower and upper border of an interval of interest for the proportion. The intersection of the above grid and this interval is taken. |
pmfX |
A function based on the arguments k , n and
p , giving the probability mass function (pmf)
f(x;n,p)=P(X=k;n,p) of X. Typically, this will be
the pmf of the binomial distribution. |
x |
An object of class coverage |
y |
Not used |
... |
Further arguments to be sent to ci.fun or the plot
function |
Compute coverage probabilities for each proportion in
p.grid
. See actual function code for the exact details, which
p.grid
is actually chosen.
An object of class coverage
containing coverage probabilities,
coverage coefficient and more.
M. Höhle
Agresti, A. and Coull, B.A. (1998), Approximate is Better than "Exact" for Interval Estimation of Binomial Proportions, The American Statistician, 52(2):119-126.
#Show coverage of Liu & Bailey interval cov <- coverage( binom.liubailey, n=100, alpha=0.05, p.grid=seq(0,1,length=1000), interval=c(0,1), lambda=0, d=0.1) plot(cov, type="l") #Now for some more advanced stuff. Investigate coverage of pooled #sample size estimators kk <- 10 nn <- 20 ci.funs <- list(poolbinom.wald, poolbinom.logit, poolbinom.lrt) covs <- lapply( ci.funs, function(f) { coverage( f, n=nn, k=kk, alpha=0.05, p.grid=seq(0,1,length=100), pmfX=function(k,n,p) dbinom(k,size=n, p=1-(1-p)^kk)) }) par(mfrow=c(3,1)) plot(covs[[1]],type="l",main="Wald",ylim=c(0.8,1)) lines(c(0,1),rep(0.95,2),lty=2,col=2) plot(covs[[2]],type="l",main="Logit")#,ylim=c(0.8,1)) lines(c(0,1),rep(0.95,2),lty=2,col=2) plot(covs[[3]],type="l",main="LRT",ylim=c(0.8,1)) lines(c(0,1),rep(0.95,2),lty=2,col=2) poolbinom.wald(x=1,n=nn,k=kk) poolbinom.logit(x=1,n=nn,k=kk) poolbinom.lrt(x=1,n=nn,k=kk)