| confint.mipfp {mipfp} | R Documentation |
Computing confidence intervals for the mipfp estimates
Description
This function computes the (asymptotic) Wald confidence intervals at a given
significance level for the estimates of an mipfp object generated by
Estimate.
Usage
## S3 method for class 'mipfp'
confint(object, parm, level = 0.95, prop = FALSE, ...)
Arguments
object |
The |
parm |
A specification of which estimates are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all estimates are considered. |
level |
The confidence level required. |
prop |
A boolean indicating if the results should be using counts ( |
... |
Further arguments passed to or from other methods (for instance
|
Details
The confidence interval of the estimates \hat{X}, at significance
level \alpha is given by
\hat{X} \pm z \left( 1-\frac{\alpha}{2} \right) *
\hat{\sigma}
where \hat{\sigma} is the standart deviations of
\hat{X}, z and
\alpha = 1 - level is the inverse of the cumulative
distribution function of the standard normal distribution.
Value
A matrix containing the upper and lower bounds for the estimated
counts/probabilities (depending on the value of the prop argument).
Author(s)
Johan Barthelemy.
Maintainer: Johan Barthelemy johan@uow.edu.au.
References
Smithson, M. (2002). Confidence intervals. Sage Publications.
See Also
confint for the default method to compute
confidence intervals for model parameters.
Estimate, Ipfp and
ObtainModelEstimates to generate the
mipfp objects for this function.
Examples
# true contingency (2-way) table
true.table <- array(c(43, 44, 9, 4), dim = c(2, 2))
# generation of sample, i.e. the seed to be updated
seed <- ceiling(true.table / 10)
# desired targets (margins)
target.row <- apply(true.table, 2, sum)
target.col <- apply(true.table, 1, sum)
# storing the margins in a list
target.data <- list(target.col, target.row)
# list of dimensions of each marginal constrain
target.list <- list(1, 2)
# using ipfp
res <- Estimate(seed, target.list, target.data)
# computing and printing the confidence intervals
print(confint(res))