| Type: | Package |
| Title: | Adaptive Multi-Wave Sampling for Efficient Chart Validation |
| Version: | 1.0 |
| Date: | 2025-01-14 |
| Author: | Georg Hahn [aut, cre], Sebastian Schneeweiss [ctb], Shirley Wang [ctb] |
| Maintainer: | Georg Hahn <ghahn@bwh.harvard.edu> |
| Description: | Functionality to perform adaptive multi-wave sampling for efficient chart validation. Code allows one to define strata, adaptively sample using several types of confidence bounds for the quantity of interest (Lai's confidence bands, Bayesian credible intervals, normal confidence intervals), and sampling strategies (random sampling, stratified random sampling, Neyman's sampling, see Neyman (1934) <doi:10.2307/2342192> and Neyman (1938) <doi:10.1080/01621459.1938.10503378>). |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 7.3.2 |
| Encoding: | UTF-8 |
| Imports: | Rdpack, anesrake, weights, grDevices, graphics, methods, stats |
| NeedsCompilation: | no |
| Packaged: | 2025-01-16 15:52:10 UTC; acer |
| Repository: | CRAN |
| Date/Publication: | 2025-01-17 09:20:02 UTC |
Bayesian credible interval for binomial quantity
Description
Bayesian credible interval for binomial quantity
Usage
credibleinterval(k, S, alpha)
Arguments
k |
Number of experiments. |
S |
Observed number of successes. |
alpha |
Level. |
Value
Bayesian credible interval.
References
.
Examples
require(chartreview)
print(credibleinterval(10,5,0.05))
Adaptive sampling algorithm which implements several types of sampling strategies
Description
Adaptive sampling algorithm which implements several types of sampling strategies
Usage
fullrun(
dat1,
S,
dat2,
mode = 1,
batchsize = 100,
raking = TRUE,
rakingmode = 3,
rakingthreshold = 0.05,
sdEstimate = mad,
minSamples = 10
)
Arguments
dat1 |
First dataset on which the strata are computed. |
S |
Matrix defining the strata. |
dat2 |
Second dataset on which confidence intervals are computed. |
mode |
Sampling mode (1 for random sampling, 2 for stratified random sampling, 3 for Neyman's sampling). |
batchsize |
Batch size in each wave. |
raking |
Boolean flag to switch on raking. |
rakingmode |
Option for raking (1 for random sampling, 2 for deterministic allocation, 3 for residual resampling). |
rakingthreshold |
Threshold for applying raking to a stratum. |
sdEstimate |
The estimate of the standard deviation as a function handle (usually sd or mad). |
minSamples |
Minimum number of samples used in each iteration. |
Value
List with the resampled datasets per wave.
References
.
Examples
require(chartreview)
Lai confidence sequence for binomial quantity
Description
Lai confidence sequence for binomial quantity
Usage
lai(n, x, alpha)
Arguments
n |
Number of experiments |
x |
Observed number of successes. |
alpha |
Error probability. |
Value
Binomial confidence interval.
References
Lai, TL (1976). On Confidence Sequences. Ann Statist 4(2):265-280.
Examples
require(chartreview)
print(lai(10,5,0.05))
Generate plots on confidence intervals and prediction
Description
Generate plots on confidence intervals and prediction
Usage
makeplot(
dataset2,
dat2,
optionCI = 1,
stopCI = NULL,
alpha = 0.05,
stoppingoption = 2,
xlim = NULL,
ylim = NULL,
main = NULL,
makePlot = TRUE
)
Arguments
dataset2 |
The output dataset of the function 'fullrun'. |
dat2 |
Second dataset on which confidence intervals are computed, see function 'fullrun'. |
optionCI |
Parameter to switch between confidence intervals (1 for Lai's confidence bands, 2 for Bayesian credible intervals, 3 for normal confidence intervals). |
stopCI |
The stopping bounds. |
alpha |
The error used to compute confidence bands. |
stoppingoption |
Type of stopping criterion (1 for confidence interval included in stopCI, 2 for upper bound below or lower bound above stopCI, 3 for length restriction on confidence interval). |
xlim |
Optional parameter to set x-axis in plots. |
ylim |
Optional parameter to set y-axis in plots. |
main |
Optional parameter to set title of plots. |
makePlot |
Parameter to control plot output. |
Value
List with confidence intervals (slot CIs), the stopping point (slot stopline), and the reason for stopping (stopreason, see function 'stoppingcriterion').
References
.
Examples
require(chartreview)
Normal confidence interval for continuous quantity
Description
Normal confidence interval for continuous quantity
Usage
normalci(x, a)
Arguments
x |
Vector of samples. |
a |
Error probability. |
Value
Normal confidence interval.
References
.
Examples
require(chartreview)
x <- rnorm(10)
print(normalci(x,0.05))
Different options for the stopping criterion
Description
Different options for the stopping criterion
Usage
stoppingcriterion(ci, stopCI, stoppingoption = 2)
Arguments
ci |
Confidence interval as tuple vector. |
stopCI |
Either a confidence interval for stoppingoption=1 and stoppingoption=2, or a scalar for stoppingoption=3. |
stoppingoption |
Option to determine if the stopping criterion is satisfied (1 for confidence interval included in stopCI, 2 for upper bound below or lower bound above stopCI, 3 for length restriction on confidence interval). |
Value
Boolean answer if stopping criterion reached.
References
.
Examples
require(chartreview)
stoppingcriterion(c(0.5,0.6), c(0.7,0.8), stoppingoption=1)
Statification of input data matrix into given strata
Description
Statification of input data matrix into given strata
Usage
stratum(x, S, index)
Arguments
x |
Input data matrix. |
S |
Strata by row in matrix S, with 2 columns per variable aka startpoint [included] and endpoint [excluded]. |
index |
Index of the stratum in S. |
Value
Vector of indices belong to the given stratum
References
.
Examples
require(chartreview)
x <- matrix(runif(10),ncol=1)
strata <- (0:10)/10
S <- cbind(strata[-length(strata)],strata[-1])
print(stratum(x,S,1))
Check if some interval is a subset of another interval
Description
Check if some interval is a subset of another interval
Usage
subsetInterval(x, y)
Arguments
x |
First interval given by tuple. |
y |
Second interval given by tuple. |
Value
Boolean answer if "x subseteq y".
References
.
Examples
require(chartreview)
x <- sort(runif(2))
y <- sort(runif(2))
print(subsetInterval(x,y))