confint.mipfp: 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 mipfp
confint(object, parm, level = 0.95, prop = FALSE, ...)

Arguments

object

The mipfp object containing the estimates.

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 (FALSE) or proportion (TRUE). Default is FALSE.

…

Further arguments passed to or from other methods (for instance vcov.mipfp).

Value

A matrix containing the upper and lower bounds for the estimated counts/probabilities (depending on the value of the prop argument).

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.

References

Smithson, M. (2002). Confidence intervals. Sage Publications.

Examples

Run this code

# NOT RUN {
# 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))
# }

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