R: Computing confidence intervals for the estimated counts and...
GetConfInt
R Documentation
Computing confidence intervals for the estimated counts and probabilities
(deprecated)
Description
This function computes the (asymptotic) Wald confidence intervals at a given
significance level for the results generated by Ipfp
and ObtainModelEstimates (provided
that their option compute.cov was set to TRUE).
Usage
GetConfInt(list.est, alpha = 0.05)
Arguments
list.est
A list produced either by Ipfp or
ObtainModelEstimates containing
the estimated counts and probabilities as well as their associated
standard deviations.
alpha
Significance level of the confidence interval corresponding to the
100(1 - α)% confidence level.
Details
The confidence interval of the estimates X.hat, at significance
level α is given by
X.hat +/- z(1-α/2) * σ.hat
where σ.hat is the standart deviations of
X.hat and z is the inverse of the cumulative
distribution function of the standard normal distribution.
Value
A list of matrices containing the upper and lower bounds for the estimated
counts and probabilities.
lower.x
Lower bounds of the confidence interval for list.est$x.hat.
upper.x
Upper bounds of the confidence interval for list.est$x.hat.
lower.p
lower bounds of the confidence interval for list.est$p.hat.
upper.p
upper bounds of the confidence interval for list.est$p.hat.
Warning
Note: this function is deprecated, instead use
confint.mipfp.
Smithson, M. (2002).
Confidence intervals.
Sage Publications.
See Also
Estimate, Ipfp and
ObtainModelEstimates to generate the
inputs for this function.
The S3 method confint.mipfp for object of
class mipfp.
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)
# calling the Ipfp function
res <- Ipfp(seed, target.list, target.data)
# addint the standart deviations to res (required by GetConfInt)
cov.res <- vcov(res, seed = seed, target.list = target.list,
target.data = target.data)
res$p.hat.se <- cov.res$p.hat.se
res$x.hat.se <- cov.res$x.hat.se
# computing and printing the confidence intervals
print(GetConfInt(res))