confintr (version 0.1.0)

ci_mean: Confidence Interval for the Population Mean

Description

This function calculates confidence intervals for the population mean. By default, Student's t method is used. Alternatively, Wald and bootstrap confidence intervals are available.

Usage

ci_mean(
  x,
  probs = c(0.025, 0.975),
  type = c("t", "Wald", "bootstrap"),
  boot_type = c("stud", "bca", "perc", "norm", "basic"),
  R = 9999,
  seed = NULL,
  ...
)

Arguments

x

A numeric vector.

probs

Error probabilites. The default c(0.025, 0.975) gives a symmetric 95% confidence interval.

type

Type of confidence interval. One of "t" (default), "Wald", or "bootstrap".

boot_type

Type of bootstrap confidence interval ("stud", "bca", "perc", "norm", "basic"). Only used for type = "bootstrap".

R

The number of bootstrap resamples. Only used for type = "bootstrap".

seed

An integer random seed. Only used for type = "bootstrap".

...

Further arguments passed to boot::boot.

Value

A list with class cint containing these components:

• parameter: The parameter in question.

• interval: The confidence interval for the parameter.

• estimate: The estimate for the parameter.

• probs: A vector of error probabilities.

• type: The type of the interval.

• info: An additional description text for the interval.

Details

Bootstrap confidence intervals are calculated by the package "boot", see references. The default bootstrap type for the mean is "stud" (bootstrap t) as it enjoys the property of being second order accurate and has a stable variance estimator (see Efron, p. 188).

References

1. Smithson, M. (2003). Confidence intervals. Series: Quantitative Applications in the Social Sciences. New York, NY: Sage Publications.

2. Efron, B. and Tibshirani R. J. (1994). An Introduction to the Bootstrap. Chapman & Hall/CRC.

3. Canty, A and Ripley B. (2019). boot: Bootstrap R (S-Plus) Functions.

Examples

# NOT RUN {
x <- 1:100
ci_mean(x)
ci_mean(x, type = "bootstrap", R = 999, seed = 1)
ci_mean(x, type = "bootstrap", R = 999, probs = c(0.025, 1), seed = 1)
ci_mean(x, type = "bootstrap", R = 999, probs = c(0, 0.975), seed = 1)
# }