Package 'binom'

Title: Binomial Confidence Intervals for Several Parameterizations
Description: Constructs confidence intervals on the probability of success in a binomial experiment via several parameterizations.
Authors: Sundar Dorai-Raj <[email protected]>
Maintainer: Sundar Dorai-Raj <[email protected]>
License: GPL
Version: 1.1-1.1
Built: 2024-11-11 07:26:55 UTC
Source: CRAN

Help Index

Binomial confidence intervals using Bayesian inference

Description

Uses a beta prior on the probability of success for a binomial distribution, determines a two-sided confidence interval from a beta posterior. A plotting function is also provided to show the probability regions defined by each confidence interval.

Usage

binom.bayes(x, n,
            conf.level = 0.95,
            type = c("highest", "central"),
            prior.shape1 = 0.5,
            prior.shape2 = 0.5,
            tol = .Machine$double.eps^0.5,
            maxit = 1000, ...)

binom.bayes.densityplot(bayes,
                        npoints = 500,
                        fill.central = "lightgray",
                        fill.lower = "steelblue",
                        fill.upper = fill.lower,
                        alpha = 0.8, ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
type

The type of confidence interval (see Details).
prior.shape1

The value of the first shape parameter to be used in the prior beta.
prior.shape2

The value of the second shape parameter to be used in the prior beta.
tol

A tolerance to be used in determining the highest probability density interval.
maxit

Maximum number of iterations to be used in determining the highest probability interval.
bayes

The output data.frame from binom.bayes.
npoints

The number of points to use to draw the density curves. Higher numbers give smoother densities.
fill.central

The color for the central density.
fill.lower, fill.upper

The color(s) for the upper and lower density.
alpha

The alpha value for controlling transparency.
...

Ignored.

Details

Using the conjugate beta prior on the distribution of p (the probability of success) in a binomial experiment, constructs a confidence interval from the beta posterior. From Bayes theorem the posterior distribution of p given the data x is:

p|x ~ Beta(x + prior.shape1, n - x + prior.shape2)

The default prior is Jeffrey's prior which is a Beta(0.5, 0.5) distribution. Thus the posterior mean is (x + 0.5)/(n + 1).

The default type of interval constructed is "highest" which computes the highest probability density (hpd) interval which assures the shortest interval possible. The hpd intervals will achieve a probability that is within tol of the specified conf.level. Setting type to "central" constructs intervals that have equal tail probabilities.

If 0 or n successes are observed, a one-sided confidence interval is returned.

Value

For binom.bayes, a data.frame containing the observed proportions and the lower and upper bounds of the confidence interval.

For binom.bayes.densityplot, a ggplot object that can printed to a graphics device, or have additional layers added.

Author(s)

Sundar Dorai-Raj ([email protected])

References

Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (1997) Bayesian Data Analysis, London, U.K.: Chapman and Hall.

See Also

binom.confint, binom.cloglog, binom.logit, binom.probit

Examples

# Example using highest probability density.
hpd <- binom.bayes(
  x = 0:10, n = 10, type = "highest", conf.level = 0.8, tol = 1e-9)
print(hpd)
binom.bayes.densityplot(hpd)
# Remove the extremes from the plot since they make things hard
# to see.
binom.bayes.densityplot(hpd[hpd$x != 0 & hpd$x != 10, ])

# Example using central probability.
central <- binom.bayes(
  x = 0:10, n = 10, type = "central", conf.level = 0.8, tol = 1e-9)
print(central)
binom.bayes.densityplot(central)
# Remove the extremes from the plot since they make things hard
# to see.
binom.bayes.densityplot(central[central$x != 0 & central$x != 10, ])

Binomial confidence intervals using the cloglog parameterization

Description

Uses the complementary log (cloglog) parameterization on the observed proportion to construct confidence intervals.

Usage

binom.cloglog(x, n, conf.level = 0.95, ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
...

ignored

Details

For derivations see doc/binom.pdf.

Value

A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.logit, binom.probit, binom.coverage

Examples

binom.cloglog(x = 0:10, n = 10)

Binomial confidence intervals

Description

Uses eight different methods to obtain a confidence interval on the binomial probability.

Usage

binom.confint(x, n, conf.level = 0.95, methods = "all", ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
methods

Which method to use to construct the interval. Any combination of c("exact", "ac", "asymptotic", "wilson", "prop.test", "bayes", "logit", "cloglog", "probit") is allowed. Default is "all".

...

Additional arguments to be passed to binom.bayes.

Details

Nine methods are allowed for constructing the confidence interval(s):

  • exact - Pearson-Klopper method. See also binom.test.

  • asymptotic - the text-book definition for confidence limits on a single proportion using the Central Limit Theorem.

  • agresti-coull - Agresti-Coull method. For a 95% confidence interval, this method does not use the concept of "adding 2 successes and 2 failures," but rather uses the formulas explicitly described in the following link: http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Agresti-Coull_Interval.

  • wilson - Wilson method.

  • prop.test - equivalent to prop.test(x = x, n = n, conf.level = conf.level)$conf.int.

  • bayes - see binom.bayes.

  • logit - see binom.logit.

  • cloglog - see binom.cloglog.

  • probit - see binom.probit.

  • profile - see binom.profile.

By default all eight are estimated for each value of x and/or n. For the "logit", "cloglog", "probit", and "profile" methods, the cases where x == 0 or x == n are treated separately. Specifically, the lower bound is replaced by (alpha/2)^n and the upper bound is replaced by (1-alpha/2)^n.

Value

A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval for all the methods in "methods".

Author(s)

Sundar Dorai-Raj ([email protected])

References

A. Agresti and B.A. Coull (1998), Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician, 52:119-126.

R.G. Newcombe, Logit confidence intervals and the inverse sinh transformation (2001), American Statistician, 55:200-202.

L.D. Brown, T.T. Cai and A. DasGupta (2001), Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101-133.

Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (1997) Bayesian Data Analysis, London, U.K.: Chapman and Hall.

See Also

binom.bayes, binom.logit, binom.probit, binom.cloglog, binom.coverage, prop.test, binom.test for comparison to method "exact"

Examples

binom.confint(x = c(2, 4), n = 100, tol = 1e-8)

Probability coverage for binomial confidence intervals

Description

Determines the probability coverage for a binomial confidence interval.

Usage

binom.coverage(p, n, conf.level = 0.95, method = "all", ...)

Arguments

p

The (true) probability of success in a binomial experiment.
n

Vector of number of independent trials in the binomial experiment.

conf.level

The level of confidence to be used in the confidence interval.

method

Either a character string to be passed to binom.confint or a function that computes the upper and lower confidence bound for a binomial proportion. If a function is supplied, the first three arguments must be the same as binom.confint and the return value of the function must be a data.frame with column headers "method", "lower", and "upper". See binom.confint for available methods. Default is "all".

...

Additional parameters to be passed to binom.confint. Only used when method is either "bayes" or "profile"

Details

Derivations are based on the results given in the references. Methods whose coverage probabilities are consistently closer to 0.95 are more desireable. Thus, Wilson's, logit, and cloglog appear to be good for this sample size, while Jeffreys, asymptotic, and prop.test are poor. Jeffreys is a variation of Bayes using prior shape parameters of 0.5 and having equal probabilities in the tail. The Jeffreys' equal-tailed interval was created using binom.bayes using (0.5,0.5) as the prior shape parameters and type = "central".

Value

A data.frame containing the "method" used, "n", "p", and the coverage probability, C(p,n).

Author(s)

Sundar Dorai-Raj ([email protected])

References

L.D. Brown, T.T. Cai and A. DasGupta (2001), Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101-133.

L.D. Brown, T.T. Cai and A. DasGupta (2002), Confidence Intervals for a Binomial Proportion and Asymptotic Expansions, Annals of Statistics, 30:160-201.

See Also

binom.confint, binom.length

Examples

binom.coverage(p = 0.5, n = 50)

Expected length for binomial confidence intervals

Description

Determines the expected length for a binomial confidence interval.

Usage

binom.length(p, n, conf.level = 0.95, method = "all", ...)

Arguments

p

The (true) probability of success in a binomial experiment.
n

Vector of number of independent trials in the binomial experiment.

conf.level

The level of confidence to be used in the confidence interval.

method

Either a character string to be passed to binom.confint or a function that computes the upper and lower confidence bound for a binomial proportion. If a function is supplied, the first three arguments must be the same as binom.confint and the return value of the function must be a data.frame with column headers "method", "lower" and "upper". See binom.confint for available methods. Default is "all".

...

Additional parameters to be passed to binom.confint. Only used when method is either "bayes" or "profile"

Details

Derivations are based on the results given in the references. Methods whose length probabilities are consistently closer to 0.95 are more desireable. Thus, Wilson's, logit, and cloglog appear to be good for this sample size, while Jeffreys, asymptotic, and prop.test are poor. Jeffreys is a variation of Bayes using prior shape parameters of 0.5 and having equal probabilities in the tail. The Jeffreys' equal-tailed interval was created using binom.bayes using (0.5,0.5) as the prior shape parameters and type = "central".

Value

A data.frame containing the "method" used, "n", "p", and the average length, L(p,n).

Author(s)

Sundar Dorai-Raj ([email protected])

References

L.D. Brown, T.T. Cai and A. DasGupta (2001), Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101-133.

L.D. Brown, T.T. Cai and A. DasGupta (2002), Confidence Intervals for a Binomial Proportion and Asymptotic Expansions, Annals of Statistics, 30:160-201.

See Also

binom.confint, binom.coverage

Examples

binom.length(p = 0.5, n = 50)

Binomial confidence intervals using the logit parameterization

Description

Uses the logistic (logit) parameterization on the observed proportion to construct confidence intervals.

Usage

binom.logit(x, n, conf.level = 0.95, ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
...

ignored

Details

For derivations see doc/binom.pdf.

Value

A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.logit, binom.probit, binom.coverage

Examples

binom.logit(x = 0:10, n = 10)

Binomial confidence intervals using the lrt likelihood

Description

Uses the lrt likelihood on the observed proportion to construct confidence intervals.

Usage

binom.lrt(x, n, conf.level = 0.95, bayes = FALSE, conf.adj = FALSE, plot
= FALSE, ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
bayes

logical; if TRUE use a Bayesian correction at the edges. Specfically, a beta prior with shape parameters 0.5 is used. If bayes is numeric, it is assumed to be the parameters to beta distribution.
conf.adj

logical; if TRUE 0 or 100% successes return a one-sided confidence interval
plot

logical; if TRUE a plot showing the the square root of the binomial deviance with reference lines for mean, lower, and upper bounds. This argument can also be a list of plotting parameters to be passed to xyplot.
...

ignored

Details

Confidence intervals are based on profiling the binomial deviance in the neighbourhood of the MLE. If x == 0 or x == n and bayes is TRUE, then a Bayesian adjustment is made to move the log-likelihood function away from Inf. Specifically, these values are replaced by (x + 0.5)/(n + 1), which is the posterier mode of f(p|x) using Jeffrey's prior on p. Furthermore, if conf.adj is TRUE, then the upper (or lower) bound uses a 1 - alpha confidence level. Typically, the observed mean will not be inside the estimated confidence interval. If bayes is FALSE, then the Clopper-Pearson exact method is used on the endpoints. This tends to make confidence intervals at the end too conservative, though the observed mean is guaranteed to be within the estimated confidence limits.

Value

A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.cloglog, binom.logit, binom.probit, binom.coverage, confint in package MASS, family, glm

Examples

binom.lrt(x = 0:10, n = 10)

Optimal binomial confidence intervals

Description

Uses optimization to minimize the integrated mean squared error between the calculated coverage and the desired confidence level for a given binomial confidence interval.

Usage

binom.optim(n, conf.level = 0.95, method = binom.lrt,
            k = n%/%2 + 1, p0 = 0, transform = TRUE,
            plot = FALSE, tol = .Machine$double.eps^0.5,
            start = NULL, ...)

Arguments

n

The number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
method

The method used to estimate the confidence interval.
k

See Details.
p0

The minimum probability of success to allow in the optimization. See Details.
transform

logical; If TRUE the optimizer will do an unconstrained optimization on the signficance probability in the logit space.
plot

logical; If TRUE the results are sent to binom.plot.
tol

The minimum significance level to allow in the optimization. See Details.
start

A starting value on the optimal confidence level.
...

Additional arguments to pass to optim.

Details

This function minimizes the squared error between the expected coverage probability and the desired confidence level.

αopt=arg⁡min⁡α∫01[C(p,n)−(1−α)2dp\alpha_{opt}=\arg\min_{\alpha}\int_{0}^{1}[C(p,n)-(1-\alpha)^2dp

The optimizer will adjust confidence intervals for all x = 0 to n depending on the value of k provided. If k is one, only the confidence levels for x = 0 and n are adjusted. If k = [n/2] then all confidence intervals are adjusted. This assumes the confidence intervals are the same length for x = x[k] and x[n - k + 1], which is the case for all methods provided in this package except binom.cloglog.

Value

A list with the following elements:

par

Final confidence levels. The length of this vector is k.
value

The final minimized value from optim.
counts

The number of function and gradient calls from optim.
convergence

Convergence code from optim.
message

Any message returned by the L-BFGS-B or BFGS optimizer.
confint

A data.frame returned from a call to method using the optimized confidence levels.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.plot, binom.coverage, optim

Examples

binom.optim(10, k = 1) ## determine optimal significance for x = 0, 10 only
binom.optim(3, method = binom.wilson) ## determine optimal significance for all x

Coverage plots for binomial confidence intervals

Description

Constructs coverage plots for binomial confidence intervals.

Usage

binom.plot(n, method = binom.lrt, np = 500,
           conf.level = 0.95, actual = conf.level,
           type = c("xyplot", "levelplot"),
           tol = .Machine$double.eps^0.5, ...)

Arguments

n

The number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
np

Number of points to use in the plot.
method

The method used to estimate the confidence interval.
actual

The actual confidence interval used in the confidence interval. See Details.
type

See Details.
tol

The minimum probability of success to use in the plot.
...

Additional arguments to pass to panel.xyplot or panel.levelplot.

Details

If type is "xyplot", a line plot is created with coverage on the y-axis and binomial probability on the x-axis. A separate panel for every n is provided. If actual is provided then a horizontal reference line is added to the plot. This is only useful when actual is different from conf.level, as is the case when calling binom.optim.

If type is "levelplot", a image plot is created with x = 0 to n on the vertical axis and binomial probability on the horizontal axis. Each row in the plot will be the confidence level for a given x. The color of the confidence interval is determined by the coverage probability. The argument n must only be of length one. If not, only the first n will be used and a warning is issued.

In either plot type, the number of points at which the coverage probability is determined is specified by np. Increasing np gives a finer granularity but performance will suffer.

Value

An object of class trellis.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.optim, xyplot, levelplot

Examples

binom.plot(5, type = "levelplot")
binom.plot(c(3, 5, 10, 25), type = "xyplot")

Power curves for binomial parameterizations

Description

Uses Wald statistics to compute power curves for several parameterizations.

Usage

binom.power(p.alt,
            n = 100,
            p = 0.5,
            alpha = 0.05,
            phi = 1,
            alternative = c("two.sided", "greater", "less"),
            method = c("cloglog", "logit", "probit", "asymp", "lrt", "exact"))

Arguments

p.alt

A vector of success probabilities under the alternative hypothesis.
n

A vector representing the number of independent trials in the binomial experiment.
p

A vector of success probabilities under the null hypothesis.
alpha

A vector of type-I error rates.
phi

A vector determining the overdispersion parameter for each binomial experiment.
alternative

Type of alternative hypothesis.
method

The method used to compute power.

Details

For derivations see doc/binom.pdf. p.alt, n, p, alpha, and phi can all be vectors. The length of each argument will be expanded to the longest length. The function assumes the lengths are equal or can be wrapped for multiple values.

Value

The estimated probability of detecting the difference between p.alt and p.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.logit, binom.probit, binom.coverage

Examples

binom.power(0.95, alternative = "greater")

Binomial confidence intervals using the probit parameterization

Description

Uses the probit parameterization on the observed proportion to construct confidence intervals.

Usage

binom.probit(x, n, conf.level = 0.95, ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
...

ignored

Details

For derivations see doc/binom.pdf.

Value

A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.probit, binom.logit, binom.coverage

Examples

binom.probit(x = 0:10, n = 10)

Binomial confidence intervals using the profile likelihood

Description

Uses the profile likelihood on the observed proportion to construct confidence intervals.

Usage

binom.profile(x, n, conf.level = 0.95, maxsteps = 50,
              del = zmax/5, bayes = TRUE, plot = FALSE, ...)

Arguments

x

Vector of number of successes in the binomial experiment.
n

Vector of number of independent trials in the binomial experiment.
conf.level

The level of confidence to be used in the confidence interval.
maxsteps

The maximum number of steps to take in the profiles.
del

The size of the step to take
bayes

logical; if TRUE use a Bayesian correction at the edges.
plot

logical; if TRUE plot the profile with a spline fit.
...

ignored

Details

Confidence intervals are based on profiling the binomial deviance in the neighbourhood of the MLE. If x == 0 or x == n and bayes is TRUE, then a Bayesian adjustment is made to move the log-likelihood function away from Inf. Specifically, these values are replaced by (x + 0.5)/(n + 1), which is the posterier mode of f(p|x) using Jeffrey's prior on p. Typically, the observed mean will not be inside the estimated confidence interval. If bayes is FALSE, then the Clopper-Pearson exact method is used on the endpoints. This tends to make confidence intervals at the end too conservative, though the observed mean is guaranteed to be within the estimated confidence limits.

Value

A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.cloglog, binom.logit, binom.probit, binom.coverage, confint in package MASS, family, glm

Examples

binom.profile(x = 0:10, n = 10)

Simulates confidence intervals for binomial data

Description

Simulates binomial data for testing confidence interval coverage.

Usage

binom.sim(M = 200, n = 100, p = 0.5, conf.level = 0.95, methods = "all",  ...)

Arguments

M

Number of simulations to create.
n

Vector of number of independent trials in the binomial experiment.
p

Probability of success under the null hypothesis.
conf.level

The level used in computing the confidence interval.
methods

The method used to compute power.
...

Additional arguments to pass to binom.confint

Details

M binomial observations are created using rbinom(M, n, p). The average number of times a confidence interval covers p is returned.

Value

The estimated coverage based on which method is requested.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint, binom.bayes, binom.logit, binom.probit, binom.coverage

Examples

binom.sim(p = 0.95, method = "lrt")

Power and sample size

Description

Power and sample size for a binomial proportion using the cloglog parameterization.

Usage

cloglog.sample.size(p.alt, n = NULL, p = 0.5, power = 0.8, alpha = 0.05,
                    alternative = c("two.sided", "greater", "less"), exact.n = FALSE,
                    recompute.power = FALSE, phi = 1)

Arguments

p.alt

The alternative proportion in a one-sample test.
n

The sample size in a one-sample test.

p

The null proportion in a one-sample test. Default is 0.5.

power

The desired power level. Default is 0.80.
alpha

The desired alpha level - probability of a Type I error. Default is 0.05.

alternative

Nature of alternative hypothesis. One of "two.sided", "greater", "less".

exact.n

logical; If TRUE, the computed sample size will not be rounded up. Default is FALSE.

recompute.power

logical; If TRUE, after the sample size is computed, the power will be recomputed. This is only advantageous when the sample size is rounded up. Default is FALSE.

phi

Dispersion parameter by which to inflate (phi > 1) or deflate (phi < 1) variance. Default is 1.

Details

This function can be used to calculate sample size, power or minimum detectable difference. It determines what to compute base on the arguments provided. If p.alt is given, but n is not, then sample size is computed. If p.alt is given along with n, then the power is computed. If only n is provided, the minimum detectable difference is computed using the default power of 0.80.

Value

A data.frame containing the power, sample size and all of the input which was used to perform the computations.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.confint

Examples

cloglog.sample.size(p.alt = 0.8)
cloglog.sample.size(n = 20)
cloglog.sample.size(n = 20, power = 0.9)

Power curves for binomial parameterizations

Description

A Tcl/Tk graphics wrapper for binom.power.

Usage

tkbinom.power()

Details

A wrapper for binom.power that creates power curves based on user input.

Author(s)

Sundar Dorai-Raj ([email protected])

See Also

binom.power, binom.confint, binom.bayes, binom.logit, binom.probit, binom.coverage

Examples

binom.power(0.95, alternative = "greater")