Type: | Package |
Title: | Bayesian Modeling via Frequentist Goodness-of-Fit |
Version: | 5.2 |
Date: | 2018-10-09 |
Author: | Subhadeep Mukhopadhyay, Douglas Fletcher |
Maintainer: | Doug Fletcher <tug25070@temple.edu> |
Description: | A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >). |
Depends: | orthopolynom, VGAM, Bolstad2, nleqslv |
Suggests: | knitr, rmarkdown |
VignetteBuilder: | knitr |
License: | GPL-2 |
NeedsCompilation: | no |
Packaged: | 2018-10-09 19:42:06 UTC; dougf |
Repository: | CRAN |
Date/Publication: | 2018-10-09 21:50:09 UTC |
Bayesian Modeling via Frequentist Goodness-of-Fit
Description
A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference.
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Number of claims on an insurance policy
Description
The number of claims on an automobile insurance policy made by k = 9461
individuals during a single year.
Usage
data("AutoIns")
Format
A vector of length 9461.
value
number of auto insurance claims by the
i^{th}
person
Source
Efron, B. and Hastie, T., 2016. Computer Age Statistical Inference (Vol. 5). Cambridge University Press.
Frequency of child illness
Description
Results of a study that followed k = 602
pre-school children in north-east Thailand from June 1982 through September 1985. Researchers recorded the number of times a child became ill during every 2-week period.
Usage
data("ChildIll")
Format
A vector of length k=602
.
value
number of times the
i^{th}
child became ill during the study
Source
Bohning, D., 2000. Computer-assisted Analysis of Mixtures and Applications: Meta-analysis, Disease Mapping, and Others (Vol. 81). CRC press.
Corbet's Butterfly data
Description
The number of times Alexander Corbet captured a species of butterfly during a two-year period in Malaysia.
Usage
data("CorbBfly")
Format
A vector of length k = 501
.
value
number of times Corbet captured the
i^{th}
species
Source
Fisher, R.A., Corbet, A.S. and Williams, C.B., 1943. "The relation between the number of species and the number of individuals in a random sample of an animal population." The Journal of Animal Ecology, pp.42-58.
References
Efron, B. and Hastie, T., 2016. Computer Age Statistical Inference (Vol. 5). Cambridge University Press.
Conduct Finite Bayes Inference on a DS object
Description
A function that generates the finite Bayes prior and posterior distribution, along with the Bayesian credible interval for the posterior mean.
Usage
DS.Finite.Bayes(DS.GF.obj, y.0, n.0 = NULL,
cred.interval = 0.9, iters = 25)
Arguments
DS.GF.obj |
Object from |
y.0 |
For Binomial family, number of success |
n.0 |
For the Binomial family, the total number of trials for the new study. In the Normal family, |
cred.interval |
The desired probability for the credible interval of the posterior mean; the default is 0.90 ( |
iters |
Integer value of total number of iterations. |
Value
prior.fit |
Fitted values for the estimated parametric, DS, and finite Bayes prior distributions. |
post.fit |
Dataframe with |
interval |
The |
post.vec |
Vector containing the PEB posterior mean ( |
Author(s)
Doug Fletcher, Subhadeep Mukhopadhyay
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Efron, B., 2018. "Bayes, Oracle Bayes, and Empirical Bayes," Technical Report.
Examples
## Not run:
### Finite Bayes: Rat with theta_71 (y_71 = 4, n_71 = 14)
data(rat)
rat.start <- gMLE.bb(rat$y, rat$n)$estimate
rat.ds <- DS.prior(rat, max.m = 4, rat.start. family = "Binomial")
rat.FB <- DS.FiniteBayes(rat.ds, y.0 = 4, n.0 = 14)
plot(rat.FB)
## End(Not run)
Full and Excess Entropy of DS(G,m) prior
Description
A function that calculates the full entropy of a DS(G,m) prior. For DS(G,m) with m > 0
, also returns the excess entropy q
LP.
Usage
DS.entropy(DS.GF.obj)
Arguments
DS.GF.obj |
Object resulting from running DS.prior function on a data set. |
Value
ent |
The total entropy of the DS(G,m) prior where |
qLP |
The excess entropy when |
Author(s)
Doug Fletcher
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Examples
data(rat)
rat.start <- gMLE.bb(rat$y, rat$n)$estimate
rat.ds <- DS.prior(rat, max.m = 4, rat.start, family = "Binomial")
DS.entropy(rat.ds)
Execute MacroInference (mean or mode) on a DS object
Description
A function that generates macro-estimates with their uncertainty (standard error).
Usage
DS.macro.inf(DS.GF.obj, num.modes = 1,
method = c("mean", "mode"),
iters = 25, exposure = NULL)
Arguments
DS.GF.obj |
Object from |
num.modes |
The number of modes indicated by |
method |
Returns mean or mode(s) (based on user choice) along with the associated standard error(s). |
iters |
Integer value of total number of iterations. |
exposure |
In the case where |
Value
DS.GF.macro.obj |
Object of class |
model.modes |
For |
mode.sd |
For |
boot.modes |
For |
model.mean |
For |
mean.sd |
For |
boot.mean |
For |
prior.fit |
Fitted values of estimated prior imported from the |
Author(s)
Doug Fletcher, Subhadeep Mukhopadhyay
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Examples
## Not run:
### MacroInference: Mode
data(rat)
rat.start <- gMLE.bb(rat$y, rat$n)$estimate
rat.ds <- DS.prior(rat, max.m = 4, rat.start. family = "Binomial")
rat.ds.macro <- DS.macro.inf(rat.ds, num.modes = 2, method = "mode", iters = 5)
rat.ds.macro
plot(rat.ds.macro)
### MacroInference: Mean
data(ulcer)
ulcer.start <- gMLE.nn(ulcer$y, ulcer$se)$estimate
ulcer.ds <- DS.prior(ulcer, max.m = 4, ulcer.start)
ulcer.ds.macro <- DS.macro.inf(ulcer.ds, num.modes = 1, method = "mean", iters = 5)
ulcer.ds.macro
plot(ulcer.ds.macro)
## End(Not run)
MicroInference for DS Prior Objects
Description
Provides DS nonparametric adaptive Bayes and parametric estimate for a specific observation y_0
.
Usage
DS.micro.inf(DS.GF.obj, y.0, n.0, e.0 = NULL)
Arguments
DS.GF.obj |
Object resulting from running DS.prior function on a data set. |
y.0 |
For Binomial family, number of success |
n.0 |
For the Binomial family, the total number of trials for the new study. In the Normal family, |
e.0 |
In the case of the Poisson family with exposure, represents the exposure value for a given count value |
Details
Returns an object of class DS.GF.micro
that can be used in conjunction with plot command to display the DS posterior distribution for the new study.
Value
DS.mean |
Posterior mean for |
DS.mode |
Posterior mode for |
PEB.mean |
Posterior mean for |
PEB.mode |
Posterior mode for |
post.vec |
Vector containing |
study |
User-provided |
post.fit |
Dataframe with |
Author(s)
Doug Fletcher, Subhadeep Mukhopadhyay
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Examples
### MicroInference for Naval Shipyard Data: sample where y = 0 and n = 5
data(ship)
ship.ds <- DS.prior(ship, max.m = 2, c(.5,.5), family = "Binomial")
ship.ds.micro <- DS.micro.inf(ship.ds, y.0 = 0, n.0 = 5)
ship.ds.micro
plot(ship.ds.micro)
Posterior Expectation and Modes of DS object
Description
A function that determines the posterior expectations E(\theta_0 | y_0)
and posterior modes for a set of observed data.
Usage
DS.posterior.reduce(DS.GF.obj, exposure)
Arguments
DS.GF.obj |
Object resulting from running DS.prior function on a data set. |
exposure |
In the case of the Poisson family with exposure, represents the exposure values for the count data. |
Value
Returns k \times 4
matrix with the columns indicating PEB mean, DS mean, PEB mode, and DS modes for k
observations in the data set.
Author(s)
Doug Fletcher
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Examples
data(rat)
rat.start <- gMLE.bb(rat$y, rat$n)$estimate
rat.ds <- DS.prior(rat, max.m = 4, rat.start, family = "Binomial")
DS.posterior.reduce(rat.ds)
Prior Diagnostics and Estimation
Description
A function that generates the uncertainty diagnostic function (U-function
) and estimates DS(G,m)
prior model.
Usage
DS.prior(input, max.m = 8, g.par,
family = c("Normal","Binomial", "Poisson"),
LP.type = c("L2", "MaxEnt"),
smooth.crit = "BIC", iters = 200, B = 1000,
max.theta = NULL)
Arguments
input |
For |
max.m |
The truncation point |
g.par |
Vector with estimated parameters for specified conjugate prior distribution |
family |
The distribution of |
LP.type |
User selects either |
smooth.crit |
User selects either |
iters |
Integer value that gives the maximum number of iterations allowed for convergence; default is 200. |
B |
Integer value for number of grid points used for distribution output; default is 1000. |
max.theta |
For |
Details
Function can take m=0
and will return the Bayes estimate with given starting parameters. Returns an object of class DS.GF.obj
; this object can be used with plot command to plot the U-function (Ufunc
), Deviance Plots (mDev
), and DS-G comparison (DS_G
).
Value
LP.par |
|
g.par |
Parameters for |
LP.max.uns |
Vector of all LP-Fourier coefficients prior to smoothing, where the length is the same as |
LP.max.smt |
Vector of all smoothed LP-Fourier coefficients, where the length is the same as |
prior.fit |
Fitted values for the estimated prior. |
UF.data |
Dataframe that contains values required for plotting the U-function. |
dev.df |
Dataframe that contains deviance values for values of |
m.val |
The value of |
sm.crit |
Smoothing criteria; either |
fam |
The user-selected family. |
LP.type |
User-selected representation of |
obs.data |
Observed data provided by user for |
Author(s)
Doug Fletcher, Subhadeep Mukhopadhyay
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Mukhopadhyay, S., 2017. "Large-Scale Mode Identification and Data-Driven Sciences," Electronic Journal of Statistics, 11(1), pp.215-240.
Examples
data(rat)
rat.start <- gMLE.bb(rat$y, rat$n)$estimate
rat.ds <- DS.prior(rat, max.m = 4, rat.start, family = "Binomial")
rat.ds
plot(rat.ds, plot.type = "Ufunc")
plot(rat.ds, plot.type = "DSg")
plot(rat.ds, plot.type = "mDev")
Samples data from DS(G,m) distribution.
Description
Generates samples of size k
from DS(G,m)
prior distribution.
Usage
DS.sampler(k, g.par, LP.par, con.prior, LP.type, B)
DS.sampler.post(k, g.par, LP.par, y.0, n.0,
con.prior, LP.type, B)
Arguments
k |
Total number of samples requested. |
g.par |
Estimated parameters for specified conjugate prior distribution (i.e beta prior: |
LP.par |
LP coefficients for DS prior. |
con.prior |
The distribution type of conjugate prior |
LP.type |
The type of LP means, either |
y.0 |
Depending on |
n.0 |
Depending on |
B |
The number of grid points, default is 250. |
Details
DS.sampler.post
uses the same type of sampling as DS.sampler
to generate random values from a DS posterior distribution.
Value
Vector of length k
containing sampled values from DS prior or DS posterior.
Author(s)
Doug Fletcher, Subhadeep Mukhopadhyay
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Mukhopadhyay, S., 2017. "Large-Scale Mode Identification and Data-Driven Sciences," Electronic Journal of Statistics, 11(1), pp.215-240.
Examples
##Extracted parameters from rat.ds object
rat.g.par <- c(2.3, 14.1)
rat.LP.par <- c(0, 0, -0.5)
samps.prior <- DS.sampler(25, rat.g.par, rat.LP.par, con.prior = "Beta")
hist(samps.prior,15)
##Posterior for rat data
samps.post <- DS.sampler.post(25, rat.g.par, rat.LP.par,
y.0 = 4, n.0 = 14, con.prior = "Beta")
hist(samps.post, 15)
Norberg life insurance data
Description
The number of claims y_i
on a life insurance policy for each of k=72
Norwegian occupational categories and the total number of years the workers in each category were exposed to risk (E_i
).
Usage
data("NorbergIns")
Format
A data frame of the occupational group number (group
), the number of deaths (deaths
), and the years of exposure (exposure
) for i = 1,...,72
.
group
Occupational group number
deaths
The number of deaths in the occupational group resulting in a claim on a life insurance policy.
exposure
The total number of years of exposure to risk for those who passed.
Source
Norberg, R., 1989. "Experience rating in group life insurance," Scandinavian Actuarial Journal, 1989(4), pp. 194-224.
References
Koenker, R. and Gu, J., 2017. "REBayes: An R Package for Empirical Bayes Mixture Methods," Journal of Statistical Software, Articles, 82(8), pp. 1-26.
Arsenic levels in oyster tissue
Description
Results from an inter-laboratory study involving k = 28
measurements for the level of arsenic in oyster tissue. y
is the mean level of arsenic from a lab and se
is the standard error of the measurement.
Usage
data("arsenic")
Format
A data frame of (y_i, se_i)
for i = 1,...,28
.
y
mean level of arsenic in the tissue measured by the
i^{th}
labse
the standard error of the measurement by
i^{th}
lab
Source
Wille, S. and Berman, S., 1995. "Ninth round intercomparison for trace metals in marine sediments and biological tissues," NRC/NOAA.
Determine LP basis functions for prior distribution g
Description
Determines the LP basis for a given parametric prior distribution.
Usage
gLP.basis(x, g.par, m, con.prior, ind)
Arguments
x |
|
g.par |
Estimated parameters for specified prior distribution (i.e beta prior: |
m |
Number of LP-Polynomial basis. |
con.prior |
Specified conjugate prior distribution for basis functions. Options are |
ind |
Default is NULL which returns matrix with |
Value
Matrix with m
columns of values for the LP-Basis functions evaluated at x
-values.
Author(s)
Subhadeep Mukhopadhyay, Doug Fletcher
References
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Mukhopadhyay, S., 2017. "Large-Scale Mode Identification and Data-Driven Sciences," Electronic Journal of Statistics, 11(1), pp.215-240.
Mukhopadhyay, S. and Parzen, E., 2014. "LP Approach to Statistical Modeling," arXiv: 1405.2601.
Beta-Binomial Parameter Estimation
Description
Computes type-II Maximum likelihood estimates \hat{\alpha}
and \hat{\beta}
for Beta prior g\sim
Beta(\alpha,\beta)
.
Usage
gMLE.bb(success, trials, start = NULL, optim.method = "default",
lower = 0, upper = Inf)
Arguments
success |
Vector containing the number of successes. |
trials |
Vector containing the total number of trials that correspond to the successes. |
start |
initial parameters; default is NULL which allows function to determine MoM estimates as initial parameters. |
optim.method |
optimization method in |
lower |
lower bound for parameters; default is 0. |
upper |
upper bound for parameters; default is infinity. |
Value
estimate |
MLE estimate for beta parameters. |
convergence |
Convergence code from |
loglik |
Loglikelihood that corresponds with MLE estimated parameters. |
initial |
Initial parameters, either user-defined or determined from method of moments. |
hessian |
Estimated Hessian matrix at the given solution. |
Author(s)
Aleksandar Bradic
References
https://github.com/SupplyFrame/EmpiricalBayesR/blob/master/EmpiricalBayesEstimation.R
Examples
data(rat)
### MLE estimate of alpha and beta
rat.mle <- gMLE.bb(rat$y, rat$N)$estimate
rat.mle
### MoM estimate of alpha and beta
rat.mom <- gMLE.bb(rat$y, rat$N)$initial
rat.mom
Normal-Normal Parameter Estimation
Description
Computes type-II Maximum likelihood estimates \hat{\mu}
and \hat{\tau}^2
for Normal prior g\sim
Normal(\mu, \tau^2)
.
Usage
gMLE.nn(value, se, fixed = FALSE, method = c("DL","SJ","REML","MoM"))
Arguments
value |
Vector of values. |
se |
Standard error for each value. |
fixed |
When |
method |
Determines the method to find |
Value
estimate |
Vector with both estimated |
mu.hat |
Estimated |
tau.sq |
Estimated |
method |
User-selected method. |
Author(s)
Doug Fletcher
References
Marin-Martinez, F. and Sanchez-Meca, J., 2010. "Weighting by inverse variance or by sample size in random-effects meta-analysis," Educational and Psychological Measurement, 70(1), pp. 56-73.
Brown, L.D., 2008. "In-season prediction of batting averages: A field test of empirical Bayes and Bayes methodologies," The Annals of Applied Statistics, pp. 113-152.
Sidik, K. and Jonkman, J.N., 2005. "Simple heterogeneity variance estimation for meta-analysis," Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(2), pp. 367-384.
Examples
data(ulcer)
### MLE estimate of alpha and beta
ulcer.mle <- gMLE.nn(ulcer$y, ulcer$se, method = "DL")$estimate
ulcer.mle
ulcer.reml <- gMLE.nn(ulcer$y, ulcer$se, method = "REML")$estimate
ulcer.reml
Negative-Binomial Parameter Estimation
Description
Computes Type-II Maximum likelihood estimates \hat{\alpha}
and \hat{\beta}
for gamma prior g\sim
Gamma(\alpha, \beta)
.
Usage
gMLE.pg(cnt.vec, exposure = NULL, start.par = c(1,1))
Arguments
cnt.vec |
Vector containing Poisson counts. |
exposure |
Vector containing exposures for each count. The default is no exposure, thus |
start.par |
Initial values that will pass to |
Value
Returns a vector where the first component is \alpha
and the second component is the scale parameter \beta
for the gamma distribution: \frac{1}{\Gamma(\alpha)\beta^\alpha} \theta^{\alpha-1}e^{-\frac{\theta}{\beta}}.
Author(s)
Doug Fletcher
References
Koenker, R. and Gu, J., 2017. "REBayes: An R Package for Empirical Bayes Mixture Methods," Journal of Statistical Software, Articles, 82(8), pp. 1-26.
Examples
### without exposure
data(ChildIll)
ill.start <- gMLE.pg(ChildIll)
ill.start
### with exposure
data(NorbergIns)
X <- NorbergIns$deaths
E <- NorbergIns$exposure/344
norb.start <- gMLE.pg(X, exposure = E)
norb.start
Galaxy Data
Description
The observed rotation velocities and their uncertainties of Low Surface Brightness (LSB) galaxies, along with the physical radius of the galaxy.
Usage
data("galaxy")
Format
A data frame of (y_i, se_i, X_i)
for i = 1,...,318
.
y
actual observed (smoothed) velocity
se
uncertainty of observed velocity
X
physical radius of the galaxy
Source
De Blok, W.J.G., McGaugh, S.S., and Rubin, V. C., 2001. "High-resolution rotation curves of low surface brightness galaxies. II. Mass models," The Astronomical Journal, 122(5), p. 2396.
Rat Tumor Data
Description
Incidence of endometrial stromal polyps in k=70
studys of female rats in control group of a 1977 study on the carcinogenic effects of a diabetic drug phenformin. For each of the k
groups, y
represents the number of rats who developed the tumors out of n
total rats in the group.
Usage
data("rat")
Format
A data frame of (y_i, n_i)
for i = 1,...,70
.
y
number of female rats in the
i^{th}
study who developed polyps/tumorsn
total number of rats in the
i^{th}
study
Source
National Cancer Institute (1977), "Bioassay of phenformin for possible carcinogenicity," Technical Report No. 7.
References
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., and Rubin, D.B., 2014. Bayesian Data Analysis (Vol. 3). Boca Raton, FL: CRC press.
Tarone, R.E., 1982. "The use of historical control information in testing for a trend in proportions," Biometrics, pp. 215-220.
Portsmouth Navy Shipyard Data
Description
Data represents results of quality-control inspections executed by Portsmouth Naval Shipyard on lots of welding materials. The data has k=5
observations of number of defects y
out of the total number of tested n=5
.
Usage
data("ship")
Format
A data frame of (y_i, n_i)
for i = 1,...,5
.
y
number of defects found in the
i^{th}
inspectionn
total samples tested in the
i^{th}
inspection
Source
Martz, H.F. and Lian, M.G., 1974. "Empirical Bayes estimation of the binomial parameter," Biometrika, 61(3), pp. 517-523.
Nasal Steroid Data
Description
The standardized mean difference y_i
and standard errors se_i
for seven randomised studies on the use of topical steroids in treatment of chronic rhinosinusitis with nasal polyps.
Usage
data("steroid")
Format
A data frame of (y_i, se_i)
for i = 1,...,7
.
y
standard mean difference of clinical trials for topical steroids found in the
i^{th}
studyse
standard error of the standard mean difference for the
i^{th}
study
Source
IntHout, J., Ioannidis, J. P., Rovers, M. M., & Goeman, J. J., 2016. "Plea for routinely presenting prediction intervals in meta-analysis," BMJ open, 6(7), e010247.
Intestinal surgery data
Description
Data involves the number of malignant lymph nodes removed during intestinal surgery for k=844
cancer patients. For each patient, n
is the total number of satellite nodes removed during surgery from a patient and y
is the number of malignant nodes.
Usage
data("surg")
Format
A data frame of (y_i, n_i)
for i = 1,...,844
.
y
number of malignant lymph nodes removed from the
i^{th}
patientn
total number of lymph nodes removed from the
i^{th}
patient
Source
Efron, B., 2016. "Empirical Bayes deconvolution estimates," Biometrika, 103(1), pp. 1-20.
Rolling Tacks Data
Description
An experiment that requires a common thumbtack to be "flipped" n=9
times. Out of these total number of flips, y
is the total number of times that the thumbtack landed point up.
Usage
data("tacks")
Format
A data frame of (y_i, n_i)
for i = 1,...,320
.
y
number of times a thumbtack landed point up in the
i^{th}
trialn
total number of flips for the thumbtack in the
i^{th}
trial
Source
Beckett, L. and Diaconis, P., 1994. "Spectral analysis for discrete longitudinal data," Advances in Mathematics, 103(1), pp. 107-128.
Terbinafine trial data
Description
During several studies of the oral antifungal agent terbinafine, a proportion of the patients in the trial terminated treatment due to some adverse effects. In the data set, y_i
is the number of terminated treatments and n_i
is the total number of patients in the in the i^{th}
trial.
Usage
data("terb")
Format
A data frame of (y_i, n_i)
for i = 1,...,41
.
y
number of patients who terminated treatment early in the
i^{th}
trialn
total number of patients in the
i^{th}
clinical trial
Source
Young-Xu, Y. and Chan, K.A., 2008. "Pooling overdispersed binomial data to estimate event rate," BMC Medical Research Methodology, 8(1), p. 58.
Recurrent Bleeding of Ulcers
Description
The data consist of k=40
randomized trials between 1980 and 1989 of a surgical treatment for stomach ulcers. Each of the trials has an estimated log-odds ratio that measures the rate of occurrence of recurrent bleeding given the surgical treatment.
Usage
data("ulcer")
Format
A data frame of (y_i,
se_i)
for i = 1,...,40
.
y
log-odds of the occurrence of recurrent bleeding in the
i^{th}
studyse
standard error of the log-odds for the
i^{th}
study
Source
Sacks, H.S., Chalmers, T.C., Blum, A.L., Berrier, J., and Pagano, D., 1990. "Endoscopic hemostasis: an effective therapy for bleeding peptic ulcers," Journal of the American Medical Association, 264(4), pp. 494-499.
References
Efron, B., 1996. "Empirical Bayes methods for combining likelihoods," Journal of the American Statistical Association, 91(434), pp. 538-550.